{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "2.3-la-tj-ak-kc-vl-ensemble_fc_predictions.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "0rWTS99OI8l-", "colab_type": "text" }, "source": [ "## Video Sentiment Analysis in the Wild\n", "### Ensembling Notebook | FC | CS231n\n", "\n", "This notebook runs a basic FC classifier on the predicted outputs of each individual modality. Assumes the preprocessing has already been completed.\n", "\n", "Contains:\n", "- Frame\n", "- Audio\n", "- Pose\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Z6mwwTKlWFCN", "colab_type": "text" }, "source": [ "### Are we working with the tiny dataset or the final dataset?" ] }, { "cell_type": "code", "metadata": { "id": "iY1doqmoWHzR", "colab_type": "code", "colab": {} }, "source": [ "IS_TINY = False" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "AKBv4jZKrhQ0", "colab_type": "text" }, "source": [ "### Copy Pre-Processed Files" ] }, { "cell_type": "code", "metadata": { "id": "QdlpDjXpdiqz", "colab_type": "code", "outputId": "5e4deb2b-a4cc-4a47-e63a-bcf70748c89f", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "sample_data\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "C40EhGh9kopZ", "colab_type": "code", "outputId": "3b9c8093-dc7b-4a4b-9248-44d4733834ff", "colab": { "base_uri": "https://localhost:8080/", "height": 312 } }, "source": [ "!nvidia-smi" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Sun Jun 7 11:40:46 2020 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 440.82 Driver Version: 418.67 CUDA Version: 10.1 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 33C P0 25W / 250W | 0MiB / 16280MiB | 0% Default |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: GPU Memory |\n", "| GPU PID Type Process name Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "XQnFc3BdeCVL", "colab_type": "code", "outputId": "eeccf8cf-fe9f-450d-f622-7bab4b6462d9", "colab": { "base_uri": "https://localhost:8080/", "height": 523 } }, "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')\n", "FULL_PATH = 'My Drive/Machine-Learning-Projects/cs231n-project/datasets/emotiw'\n", "#FULL_PATH = 'My Drive/cs231n-project/datasets/emotiw'\n", "if IS_TINY:\n", " print(\"Using tiny dataset...\")\n", " !cp /content/drive/'$FULL_PATH'/train-tiny-* .\n", " !cp /content/drive/'$FULL_PATH'/val-tiny-* .\n", "else:\n", " print(\"Using final dataset...\")\n", " !cp /content/drive/'$FULL_PATH'/train-final-* .\n", " !cp /content/drive/'$FULL_PATH'/val-final-* .\n", "\n", "!wget https://storage.googleapis.com/cs231n-emotiw/data/Train_labels.txt\n", "!wget https://storage.googleapis.com/cs231n-emotiw/data/Val_labels.txt\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n", "\n", "Enter your authorization code:\n", "··········\n", "Mounted at /content/drive\n", "Using final dataset...\n", "--2020-06-07 11:44:53-- https://storage.googleapis.com/cs231n-emotiw/data/Train_labels.txt\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.189.128, 2404:6800:4008:c07::80\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.189.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 21653 (21K) [text/plain]\n", "Saving to: ‘Train_labels.txt’\n", "\n", "Train_labels.txt 100%[===================>] 21.15K --.-KB/s in 0s \n", "\n", "2020-06-07 11:44:53 (57.4 MB/s) - ‘Train_labels.txt’ saved [21653/21653]\n", "\n", "--2020-06-07 11:44:55-- https://storage.googleapis.com/cs231n-emotiw/data/Val_labels.txt\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.189.128, 2404:6800:4008:c07::80\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.189.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 6265 (6.1K) [text/plain]\n", "Saving to: ‘Val_labels.txt’\n", "\n", "Val_labels.txt 100%[===================>] 6.12K --.-KB/s in 0s \n", "\n", "2020-06-07 11:44:55 (84.9 MB/s) - ‘Val_labels.txt’ saved [6265/6265]\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "yXxCkE_DUvEv", "colab_type": "code", "outputId": "9812567a-52b0-43ce-e203-74cb990d8404", "colab": { "base_uri": "https://localhost:8080/", "height": 104 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "drive\t\t train-final-frames.zip val-final-fer.zip\n", "sample_data\t train-final-pose.zip val-final-frames.zip\n", "train-final-audio.zip Train_labels.txt val-final-pose.zip\n", "train-final-faces.zip val-final-audio.zip Val_labels.txt\n", "train-final-fer.zip val-final-faces.zip\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "sqHRPvB5Cm4F", "colab_type": "code", "outputId": "04f08332-5855-4d25-8486-08c06f980466", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "# RUN THIS FOR TINY FILES (zip does not include root folder)\n", "\n", "if IS_TINY:\n", " !unzip -d train-tiny-audio train-tiny-audio.zip\n", " !unzip -d train-tiny-faces train-tiny-faces.zip\n", " !unzip -d train-tiny-frames train-tiny-frames.zip\n", " !unzip -d train-tiny-pose train-tiny-pose.zip\n", "\n", " !unzip -d val-tiny-audio val-tiny-audio.zip\n", " !unzip -d val-tiny-faces val-tiny-faces.zip\n", " !unzip -d val-tiny-frames val-tiny-frames.zip\n", " !unzip -d val-tiny-pose val-tiny-pose.zip\n", "\n", "\n", "# RUN THIS FOR FINAL FILES (zip includes root folder)\n", "\n", "if not IS_TINY:\n", " !unzip train-final-audio.zip\n", " !unzip train-final-faces.zip\n", " !unzip train-final-frames.zip\n", " !unzip train-final-pose.zip\n", " !unzip -d train-final-fer train-final-fer.zip\n", "\n", " !unzip val-final-audio.zip\n", " !unzip val-final-faces.zip\n", " !unzip val-final-frames.zip\n", " !unzip val-final-pose.zip\n", " !unzip -d val-final-fer val-final-fer.zip\n", "\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", " inflating: val-final-pose/1/frame_212_1.mp4_5_keypoints.json \n", " inflating: val-final-pose/1/frame_289_57.mp4_3_keypoints.json \n", " inflating: val-final-pose/1/frame_86_8.mp4_12_keypoints.json \n", " inflating: 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val-final-pose/2/frame_159_5.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_289_22.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_11_13.mp4_13_keypoints.json \n", " inflating: val-final-pose/2/frame_294_28.mp4_9_keypoints.json \n", " extracting: val-final-pose/2/frame_154_10.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_55_11.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_298_35.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_55_4.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_298_49.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_55_17.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_53_31.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_15_2.mp4_14_keypoints.json \n", " inflating: val-final-pose/2/frame_274_5.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_298_28.mp4_7_keypoints.json \n", " inflating: 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val-final-pose/2/frame_237_1.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_289_66.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_298_26.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_21_1.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_55_20.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_251_3.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_81_19.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_294_28.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_222_18.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_181_9.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_54_5.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_308_4.mp4_13_keypoints.json \n", " inflating: val-final-pose/2/frame_55_11.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_298_14.mp4_1_keypoints.json \n", " inflating: 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val-final-pose/2/frame_294_24.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_159_2.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_298_15.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_289_55.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_325_27.mp4_14_keypoints.json \n", " inflating: val-final-pose/2/frame_294_42.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_294_6.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_53_31.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_15_4.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_53_38.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_294_10.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_70_10.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_53_36.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_237_6.mp4_2_keypoints.json \n", " inflating: 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val-final-pose/2/frame_55_21.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_268_1.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_298_6.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_294_17.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_298_55.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_320_11.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_320_11.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_54_14.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_298_17.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_157_14.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_298_57.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_298_25.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_54_14.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_251_1.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_298_6.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_294_25.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_70_22.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_298_18.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_53_20.mp4_14_keypoints.json \n", " inflating: val-final-pose/2/frame_190_6.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_274_2.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_294_4.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_152_1.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_298_7.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_55_23.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_131_5.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_294_8.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_55_7.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_298_59.mp4_0_keypoints.json \n", " extracting: val-final-pose/2/frame_251_3.mp4_12_keypoints.json \n", " extracting: val-final-pose/2/frame_181_9.mp4_14_keypoints.json \n", " inflating: val-final-pose/2/frame_298_42.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_159_1.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_298_46.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_53_37.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_159_6.mp4_13_keypoints.json \n", " inflating: val-final-pose/2/frame_289_1.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_284_10.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_55_16.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_53_5.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_298_15.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_298_42.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_70_20.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_298_5.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_298_8.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_159_3.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_54_17.mp4_13_keypoints.json \n", " inflating: val-final-pose/2/frame_294_39.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_131_5.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_314_4.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_294_3.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_294_22.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_294_7.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_325_54.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_294_19.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_298_9.mp4_8_keypoints.json \n", " extracting: val-final-pose/2/frame_154_10.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_54_15.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_53_5.mp4_14_keypoints.json \n", " inflating: val-final-pose/2/frame_294_3.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_53_19.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_284_3.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_294_6.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_294_12.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_274_10.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_294_9.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_298_24.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_289_21.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_55_20.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_294_21.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_308_6.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_289_21.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_81_24.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_298_58.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_274_7.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_298_14.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_294_34.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_298_59.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_294_21.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_294_26.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_54_15.mp4_6_keypoints.json \n", " extracting: val-final-pose/2/frame_239_3.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_294_31.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_81_14.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_181_9.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_298_42.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_298_23.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_70_20.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_237_3.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_54_15.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_274_7.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_308_4.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_181_2.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_298_20.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_70_19.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_237_6.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_294_42.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_284_11.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_159_3.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_11_13.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_55_13.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_294_22.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_27_13.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_70_24.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_284_1.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_55_19.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_159_1.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_81_7.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_298_37.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_308_4.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_53_44.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_27_9.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_53_34.mp4_13_keypoints.json \n", " inflating: val-final-pose/2/frame_237_3.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_298_30.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_81_9.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_268_7.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_131_5.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_237_1.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_21_1.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_55_17.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_284_8.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_53_20.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_70_19.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_268_5.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_181_1.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_268_7.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_53_36.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_239_3.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_289_68.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_335_1.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_159_6.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_104_2.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_284_3.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_298_26.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_289_14.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_55_17.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_294_6.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_81_7.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_54_5.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_159_2.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_274_4.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_77_4.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_70_30.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_289_48.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_55_25.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_268_3.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_81_7.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_81_8.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_325_31.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_99_6.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_294_37.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_83_1.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_298_58.mp4_5_keypoints.json \n", " inflating: val-final-pose/2/frame_274_4.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_294_27.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_325_31.mp4_14_keypoints.json \n", " inflating: val-final-pose/2/frame_284_12.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_53_19.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_294_17.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_294_19.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_159_4.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_333_2.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_294_11.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_70_15.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_268_5.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_54_13.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_55_20.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_237_6.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_335_6.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_81_18.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_284_11.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_81_24.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_181_1.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_222_18.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_298_30.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_298_15.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_190_6.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_335_3.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_159_5.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_70_25.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_298_48.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_181_9.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_320_3.mp4_11_keypoints.json \n", " inflating: val-final-pose/2/frame_298_36.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_268_3.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_15_3.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_325_49.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_298_30.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_81_4.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_289_22.mp4_7_keypoints.json \n", " inflating: val-final-pose/2/frame_298_54.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_55_13.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_268_5.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_159_9.mp4_4_keypoints.json \n", " inflating: val-final-pose/2/frame_99_6.mp4_9_keypoints.json \n", " inflating: 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val-final-pose/2/frame_55_25.mp4_6_keypoints.json \n", " inflating: val-final-pose/2/frame_294_25.mp4_9_keypoints.json \n", " inflating: val-final-pose/2/frame_55_11.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_181_4.mp4_13_keypoints.json \n", " inflating: val-final-pose/2/frame_237_3.mp4_3_keypoints.json \n", " inflating: val-final-pose/2/frame_284_12.mp4_13_keypoints.json \n", " inflating: val-final-pose/2/frame_268_1.mp4_2_keypoints.json \n", " inflating: val-final-pose/2/frame_284_10.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_298_53.mp4_8_keypoints.json \n", " inflating: val-final-pose/2/frame_181_4.mp4_0_keypoints.json \n", " inflating: val-final-pose/2/frame_298_14.mp4_10_keypoints.json \n", " inflating: val-final-pose/2/frame_70_25.mp4_1_keypoints.json \n", " inflating: val-final-pose/2/frame_294_7.mp4_12_keypoints.json \n", " inflating: val-final-pose/2/frame_159_3.mp4_14_keypoints.json \n", " inflating: 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"id": "gF6Fy4-SLJVP", "colab_type": "code", "outputId": "ffa96e1d-39db-4fd6-b3e7-6f57af030f72", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "sample_data\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "0jEaDQAsS8XQ", "colab_type": "text" }, "source": [ "### Run Classifiers" ] }, { "cell_type": "code", "metadata": { "id": "w0_gfpBjXP6S", "colab_type": "code", "colab": {} }, "source": [ "%tensorflow_version 2.x" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "dPXBx950Xd8z", "colab_type": "code", "outputId": "ca332c80-a23f-4211-b64d-4f96bd10e801", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "import tensorflow\n", "print(tensorflow.__version__)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "2.2.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "pp_USHxwRAVB", "colab_type": "code", "outputId": "9049c96f-c788-42a0-d254-8bfc20c90bf0", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "!pwd" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "/content\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "O_9UA5NPVYro", "colab_type": "code", "outputId": "3a23fa40-37e0-43ef-f734-af91f37756f7", "colab": { "base_uri": "https://localhost:8080/", "height": 139 } }, "source": [ "!git clone 'https://github.com/kevincong95/cs231n-emotiw.git'" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'cs231n-emotiw'...\n", "remote: Enumerating objects: 239, done.\u001b[K\n", "remote: Counting objects: 100% (239/239), done.\u001b[K\n", "remote: Compressing objects: 100% (187/187), done.\u001b[K\n", "remote: Total 922 (delta 140), reused 121 (delta 51), pack-reused 683\u001b[K\n", "Receiving objects: 100% (922/922), 189.65 MiB | 14.53 MiB/s, done.\n", "Resolving deltas: 100% (548/548), done.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Z7kRhNvrVnbt", "colab_type": "code", "colab": {} }, "source": [ "!mv train-* cs231n-emotiw\n", "!mv val-* cs231n-emotiw" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "tauiFf5eVtpP", "colab_type": "code", "colab": {} }, "source": [ "!mv Train* cs231n-emotiw\n", "!mv Val* cs231n-emotiw" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "qiVbcNhfVwa2", "colab_type": "code", "outputId": "3f285212-2d9b-48f2-a929-628d4243f772", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "!pwd" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "/content\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "hPM9Vm7gVxuR", "colab_type": "code", "outputId": "e7665dfb-1044-4bc2-9d0c-75c65d5421c6", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "import os\n", "os.chdir('/content/cs231n-emotiw')\n", "!pwd" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "/content/cs231n-emotiw\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "9xwIeRF_x40m", "colab_type": "code", "colab": {} }, "source": [ "# Create the concatenated input layer to feed into FC\n", "\n", "from src.classifiers.audio_classifier import AudioClassifier\n", "from src.classifiers.frames_classifier import FramesClassifier\n", "from src.classifiers.pose_classifier import PoseClassifier\n", "from src.classifiers.face_classifier import FaceClassifier\n", "from src.classifiers.utils import get_num_samples\n", "import numpy as np\n", "\n", "def run_classifier(layers_to_extract, audio_folder='train-final-audio', frames_folder='train-final-frames', pose_folder='train-final-pose', face_folder='train-final-fer' , labels_file=\"Train_labels.txt\"):\n", " audio_classifier = AudioClassifier(audio_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5', is_test=False)\n", " frames_classifier = FramesClassifier(frames_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5', is_test=False)\n", " pose_classifier = PoseClassifier(pose_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5', is_test=False)\n", " face_classifier = FaceClassifier(face_folder, model_location='/content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5', is_test=False)\n", " classifiers = [audio_classifier, frames_classifier, pose_classifier] # face_classifier]\n", " \n", " sample_to_true_label = {}\n", " with open(labels_file) as f:\n", " l = 0\n", " for line in f:\n", " if l == 0:\n", " # Skip headers\n", " l += 1\n", " continue\n", " line_arr = line.split(\" \")\n", " sample_to_true_label[line_arr[0].strip()] = int(line_arr[1].strip()) - 1 # subtract one to make labels from 0 to 2\n", " l += 1\n", "\n", " classifier_outputs = []\n", " classifier_samples = []\n", " classifier_dim_sizes = []\n", " output_dim_size = 0\n", " num_samples = 0\n", " sample_to_row = {}\n", "\n", " for i, classifier in enumerate(classifiers):\n", " output, samples = classifier.predict(layers_to_extract[i])\n", " output_dim_size += output.shape[1]\n", " classifier_dim_sizes.append(output.shape[1])\n", " num_samples = len(samples)\n", " classifier_outputs.append(output)\n", " classifier_samples.append(samples)\n", "\n", "\n", " X_train = np.zeros(shape=(num_samples, output_dim_size))\n", " y_train = []\n", "\n", " print(f\"Number of samples: {num_samples}\")\n", " print(f\"Dim shapes: \")\n", " print(classifier_dim_sizes)\n", "\n", " for i, sample in enumerate(classifier_samples[0]):\n", " sample_to_row[sample] = i\n", " y_train.append(sample_to_true_label[sample])\n", "\n", " last_classifier_index = 0\n", " for c, output in enumerate(classifier_outputs):\n", " samples = classifier_samples[c]\n", " print(len(output))\n", " for i, row in enumerate(output):\n", " sample = samples[i]\n", " X_train[sample_to_row[sample], last_classifier_index:last_classifier_index+classifier_dim_sizes[c]] += row\n", " last_classifier_index += classifier_dim_sizes[c]\n", "\n", " return X_train, tf.keras.utils.to_categorical(y_train, num_classes=3)\n", "\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1VGA1fl2pNvE", "colab_type": "code", "outputId": "b05377ca-2cdd-4a06-ac1e-0184c6e07554", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "import tensorflow as tf\n", "\n", "# For each classifier, extract the specific desired layer \n", "# (refer to the model summary for the layer names)\n", "\n", "layers_to_extract = [\n", " \"dense\",\n", " \"concatenate_5\",\n", " \"bidirectional_1\",\n", " #\"dense_27\",\n", "]\n", "\n", "prefix = \"final\"\n", "if IS_TINY:\n", " prefix = \"tiny\"\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\" ,labels_file=\"Train_labels.txt\")\n", "X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\" , labels_file=\"Val_labels.txt\")\n", "\n", "print(X_train.shape)\n", "print(y_train.shape)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "AudioClassifier created with audio_folder = train-final-audio , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "FramesClassifier created with frames_folder = train-final-frames , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5\n", "PoseClassifier created with pose_folder = train-final-pose , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5\n", "load data\n", "FacesClassifier created with face_folder = train-final-fer , is_test = False , model_location = /content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "Skipping unzipping files as input is a folder\n", "Customizing model by returning layer dense\n", "Skipping unzipping files as input is a folder\n", "Found 2661 frames belonging to 2661 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer concatenate_5\n", "Skipping unzipping files as input is a folder\n", "['2', '1', '3']\n", "Found 2661 frames belonging to 2661 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer bidirectional_1\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/content/cs231n-emotiw/src/generators/pose_generator.py:135: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((lx[i] - origin_x) / len_x)\n", "/content/cs231n-emotiw/src/generators/pose_generator.py:136: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((ly[i] - origin_y) / len_y)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Number of samples: 2661\n", "Dim shapes: \n", "[32, 30, 128]\n", "2661\n", "2661\n", "2661\n", "AudioClassifier created with audio_folder = val-final-audio , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "FramesClassifier created with frames_folder = val-final-frames , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5\n", "PoseClassifier created with pose_folder = val-final-pose , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5\n", "load data\n", "FacesClassifier created with face_folder = train-final-fer , is_test = False , model_location = /content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "Skipping unzipping files as input is a folder\n", "Customizing model by returning layer dense\n", "Skipping unzipping files as input is a folder\n", "Found 766 frames belonging to 766 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer concatenate_5\n", "Skipping unzipping files as input is a folder\n", "['2', '1', '3']\n", "Found 766 frames belonging to 766 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer bidirectional_1\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/content/cs231n-emotiw/src/generators/pose_generator.py:135: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((lx[i] - origin_x) / len_x)\n", "/content/cs231n-emotiw/src/generators/pose_generator.py:136: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((ly[i] - origin_y) / len_y)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Number of samples: 766\n", "Dim shapes: \n", "[32, 30, 128]\n", "766\n", "766\n", "766\n", "(2661, 190)\n", "(2661, 3)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "kXNhfFvFho1Q", "colab_type": "code", "outputId": "d6a8b8c7-224d-484e-b907-57dd86d0bd8c", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " data\t\t\t\t\t train-final-frames\n", " LICENSE\t\t\t\t train-final-frames.zip\n", " models\t\t\t\t\t train-final-pose\n", " notebooks\t\t\t\t train-final-pose.zip\n", " README.md\t\t\t\t Train_labels.txt\n", " reports\t\t\t\t val-final-audio\n", " requirements-predictions.txt\t\t val-final-audio.zip\n", " requirements.txt\t\t\t val-final-faces\n", "'Screen Shot 2020-05-26 at 8.53.32 PM.png' val-final-faces.zip\n", " src\t\t\t\t\t val-final-fer\n", " train-final-audio\t\t\t val-final-fer.zip\n", " train-final-audio.zip\t\t\t val-final-frames\n", " train-final-faces\t\t\t val-final-frames.zip\n", " train-final-faces.zip\t\t\t val-final-pose\n", " train-final-fer\t\t\t val-final-pose.zip\n", " train-final-fer.zip\t\t\t Val_labels.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "G0zv6L6hhzsP", "colab_type": "code", "outputId": "84500f82-95a5-497c-f6bd-2d54cc7b3ebe", "colab": { "base_uri": "https://localhost:8080/", "height": 121 } }, "source": [ "# !mkdir ensemble-scene-pose-audio-face\n", "# np.save(\"ensemble-scene-pose-audio-face/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face.zip ensemble-scene-pose-audio-face\n", "# !cp ensemble-scene-pose-audio-face.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "# !mkdir ensemble-scene-pose-audio-face-v2\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face-v2.zip ensemble-scene-pose-audio-face-v2\n", "# !cp ensemble-scene-pose-audio-face-v2.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "\n", "!mkdir ensemble-scene-pose-audio-v1\n", "np.save(\"ensemble-scene-pose-audio-v1/X_train.npy\", X_train)\n", "np.save(\"ensemble-scene-pose-audio-v1/y_train.npy\", y_train)\n", "np.save(\"ensemble-scene-pose-audio-v1/X_valid.npy\", X_valid)\n", "np.save(\"ensemble-scene-pose-audio-v1/y_valid.npy\", y_valid)\n", "!zip -r ensemble-scene-pose-audio-v1.zip ensemble-scene-pose-audio-v1\n", "!cp ensemble-scene-pose-audio-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "mkdir: cannot create directory ‘ensemble-scene-pose-audio-v1’: File exists\n", " adding: ensemble-scene-pose-audio-v1/ (stored 0%)\n", " adding: ensemble-scene-pose-audio-v1/y_valid.npy (deflated 96%)\n", " adding: ensemble-scene-pose-audio-v1/X_valid.npy (deflated 46%)\n", " adding: ensemble-scene-pose-audio-v1/X_train.npy (deflated 46%)\n", " adding: ensemble-scene-pose-audio-v1/y_train.npy (deflated 97%)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "xGS2rrUIZ_YX", "colab_type": "text" }, "source": [ "## Remove the Face!" ] }, { "cell_type": "code", "metadata": { "id": "t74F7g-72k8U", "colab_type": "code", "outputId": "ae34a3c5-3db6-41cc-efdb-9a347fecdda2", "colab": { "base_uri": "https://localhost:8080/", "height": 312 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " data\t\t\t\t\t train-final-fer.zip\n", " ensemble-scene-pose-audio-v1\t\t train-final-frames\n", " ensemble-scene-pose-audio-v1.zip\t train-final-frames.zip\n", " LICENSE\t\t\t\t train-final-pose\n", " models\t\t\t\t\t train-final-pose.zip\n", " notebooks\t\t\t\t Train_labels.txt\n", " README.md\t\t\t\t val-final-audio\n", " reports\t\t\t\t val-final-audio.zip\n", " requirements-predictions.txt\t\t val-final-faces\n", " requirements.txt\t\t\t val-final-faces.zip\n", "'Screen Shot 2020-05-26 at 8.53.32 PM.png' val-final-fer\n", " src\t\t\t\t\t val-final-fer.zip\n", " train-final-audio\t\t\t val-final-frames\n", " train-final-audio.zip\t\t\t val-final-frames.zip\n", " train-final-faces\t\t\t val-final-pose\n", " train-final-faces.zip\t\t\t val-final-pose.zip\n", " train-final-fer\t\t\t Val_labels.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "SvJo_G-jp8Au", "colab_type": "code", "outputId": "53a94996-d0f9-4c56-814e-2e72ec9ecd83", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "#!rm -rf ensemble-scene-pose-audio\n", "#!cp ../drive/'My Drive'/cs231n-project/datasets/emotiw/ensemble-scene-pose-audio.zip .\n", "#!unzip ensemble-scene-pose-audio.zip\n", "\n", "X_train = np.load(\"ensemble-scene-pose-audio-v1/X_train.npy\")\n", "y_train = np.load(\"ensemble-scene-pose-audio-v1/y_train.npy\")\n", "X_valid = np.load(\"ensemble-scene-pose-audio-v1/X_valid.npy\")\n", "y_valid = np.load(\"ensemble-scene-pose-audio-v1/y_valid.npy\")\n", "X_train.shape" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(2661, 190)" ] }, "metadata": { "tags": [] }, "execution_count": 24 } ] }, { "cell_type": "code", "metadata": { "id": "_zUL5LC_3pFt", "colab_type": "code", "outputId": "52476219-5e69-4369-d1c0-a6db715c7a34", "colab": { "base_uri": "https://localhost:8080/", "height": 520 } }, "source": [ "!pip install keras-tuner" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Collecting keras-tuner\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a7/f7/4b41b6832abf4c9bef71a664dc563adb25afc5812831667c6db572b1a261/keras-tuner-1.0.1.tar.gz (54kB)\n", "\r\u001b[K |██████ | 10kB 26.2MB/s eta 0:00:01\r\u001b[K |████████████ | 20kB 1.6MB/s eta 0:00:01\r\u001b[K |██████████████████ | 30kB 2.2MB/s eta 0:00:01\r\u001b[K |████████████████████████ | 40kB 2.5MB/s eta 0:00:01\r\u001b[K |██████████████████████████████ | 51kB 1.9MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 61kB 1.9MB/s \n", "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.16.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.18.4)\n", "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.8.7)\n", "Collecting terminaltables\n", " Downloading https://files.pythonhosted.org/packages/9b/c4/4a21174f32f8a7e1104798c445dacdc1d4df86f2f26722767034e4de4bff/terminaltables-3.1.0.tar.gz\n", "Collecting colorama\n", " Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (4.41.1)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (2.23.0)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.4.1)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.22.2.post1)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2.9)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (1.24.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2020.4.5.1)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (3.0.4)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->keras-tuner) (0.15.1)\n", "Building wheels for collected packages: keras-tuner, terminaltables\n", " Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for keras-tuner: filename=keras_tuner-1.0.1-cp36-none-any.whl size=73200 sha256=919bfa89fd0bfebaf3f5382fea17304f64d70f504ad94ff4842c286421c21423\n", " Stored in directory: /root/.cache/pip/wheels/b9/cc/62/52716b70dd90f3db12519233c3a93a5360bc672da1a10ded43\n", " Building wheel for terminaltables (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for terminaltables: filename=terminaltables-3.1.0-cp36-none-any.whl size=15356 sha256=bf99681f101e98d650848379db8bb0e4ba9a99b524af57fad56a866af5d9d321\n", " Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n", "Successfully built keras-tuner terminaltables\n", "Installing collected packages: terminaltables, colorama, keras-tuner\n", "Successfully installed colorama-0.4.3 keras-tuner-1.0.1 terminaltables-3.1.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "3_pa5mFUSrJK", "colab_type": "code", "colab": {} }, "source": [ "#\n", "# CONFIGURATION\n", "#\n", "# Define any constants for the model here\n", "#\n", "\n", "MODEL_NAME = \"ensemble-scene-pose-audio-v1\"\n", "\n", "from pathlib import Path\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "\n", "def build_model(hp):\n", " Path(f\"drive/My Drive/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", " checkpoint_path = \"drive/My Drive/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", " cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " period=1\n", " )\n", "\n", " def create_model(inputs):\n", " x = tf.keras.layers.Dense(hp.Int('units', min_value=8, max_value=128, step=8), activation='relu', kernel_regularizer=tf.keras.regularizers.l2())(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(\n", " hp.Choice('learning_rate',\n", " values=[1e-2, 1e-3, 1e-4])),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", " inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", " model = create_model(inputs)\n", " # model.summary()\n", " return model\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "f42dNrHP3y8h", "colab_type": "code", "outputId": "561a5848-5f17-4c7e-ed10-f4c265a2f5fd", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "from kerastuner.tuners import RandomSearch\n", "tuner = RandomSearch(\n", " build_model,\n", " objective='val_accuracy',\n", " max_trials=5,\n", " executions_per_trial=3,\n", " directory='fc-prediction',\n", " project_name='fc-prediction'\n", ")\n", "tuner.search_space_summary()\n", "\n", "tuner.search(X_train, y_train,\n", " epochs=30,\n", " validation_data=(X_valid, y_valid),\n", " verbose=0)\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "text/html": [ "

Search space summary

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units (Int)

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learning_rate (Choice)

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Results summary

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-learning_rate: 0.0001" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-units: 8" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "eZONQbr_aBpo", "colab_type": "code", "outputId": "33c484ee-153f-416c-f569-012b90b1f833", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "MODEL_NAME = \"ensemble-scene-pose-audio-v1\"\n", "Path(f\"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", "checkpoint_path = \"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " monitor='val_accuracy',\n", " period=1\n", ")\n", "\n", "def create_model(inputs):\n", " x = tf.keras.layers.Dense(72, activation='relu')(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(0.01),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", "inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", "model = create_model(inputs)\n", "\n", "import pickle\n", "history = model.fit(\n", " x=X_train,\n", " y=y_train,\n", " epochs=30,\n", " callbacks=[cp_callback],\n", " validation_data=(X_valid, y_valid)\n", ")\n", "\n", "acc = history.history['accuracy']\n", "val_acc = history.history['val_accuracy']\n", "\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(acc, label='Training Accuracy')\n", "plt.plot(val_acc, label='Validation Accuracy')\n", "plt.legend(loc='lower right')\n", "plt.ylabel('Accuracy')\n", "plt.title('Training and Validation Accuracy')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(loss, label='Training Loss')\n", "plt.plot(val_loss, label='Validation Loss')\n", "plt.legend(loc='upper right')\n", "plt.ylabel('Cross Entropy')\n", "plt.title('Training and Validation Loss')\n", "plt.xlabel('epoch')\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n", "Epoch 1/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.5563 - accuracy: 0.7851\n", "Epoch 00001: val_accuracy improved from -inf to 0.61358, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-audio-v1/cp-0001.h5\n", "84/84 [==============================] - 0s 5ms/step - loss: 0.5568 - accuracy: 0.7843 - val_loss: 0.9488 - val_accuracy: 0.6136\n", "Epoch 2/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4770 - accuracy: 0.8168\n", "Epoch 00002: val_accuracy improved from 0.61358 to 0.62402, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-audio-v1/cp-0002.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4754 - accuracy: 0.8174 - val_loss: 1.0231 - val_accuracy: 0.6240\n", "Epoch 3/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4772 - accuracy: 0.8231\n", "Epoch 00003: val_accuracy did not improve from 0.62402\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4705 - accuracy: 0.8256 - val_loss: 0.9719 - val_accuracy: 0.6057\n", "Epoch 4/30\n", "67/84 [======================>.......] - ETA: 0s - loss: 0.4576 - accuracy: 0.8195\n", "Epoch 00004: val_accuracy did not improve from 0.62402\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4578 - accuracy: 0.8207 - val_loss: 1.0179 - val_accuracy: 0.6188\n", "Epoch 5/30\n", "67/84 [======================>.......] - ETA: 0s - loss: 0.4487 - accuracy: 0.8228\n", "Epoch 00005: val_accuracy did not improve from 0.62402\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4549 - accuracy: 0.8219 - val_loss: 0.9761 - val_accuracy: 0.6240\n", "Epoch 6/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4421 - accuracy: 0.8328\n", "Epoch 00006: val_accuracy improved from 0.62402 to 0.62924, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-audio-v1/cp-0006.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4411 - accuracy: 0.8331 - val_loss: 1.0232 - val_accuracy: 0.6292\n", "Epoch 7/30\n", "63/84 [=====================>........] - ETA: 0s - loss: 0.4213 - accuracy: 0.8462\n", "Epoch 00007: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4456 - accuracy: 0.8339 - val_loss: 0.9563 - val_accuracy: 0.6227\n", "Epoch 8/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4368 - accuracy: 0.8359\n", "Epoch 00008: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4364 - accuracy: 0.8358 - val_loss: 0.9801 - val_accuracy: 0.6057\n", "Epoch 9/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4388 - accuracy: 0.8276\n", "Epoch 00009: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4231 - accuracy: 0.8395 - val_loss: 1.0150 - val_accuracy: 0.6253\n", "Epoch 10/30\n", "68/84 [=======================>......] - ETA: 0s - loss: 0.4286 - accuracy: 0.8382\n", "Epoch 00010: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4271 - accuracy: 0.8373 - val_loss: 0.9086 - val_accuracy: 0.6214\n", "Epoch 11/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4129 - accuracy: 0.8438\n", "Epoch 00011: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4094 - accuracy: 0.8470 - val_loss: 1.0457 - val_accuracy: 0.6201\n", "Epoch 12/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4142 - accuracy: 0.8445\n", "Epoch 00012: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4126 - accuracy: 0.8448 - val_loss: 1.0457 - val_accuracy: 0.6201\n", "Epoch 13/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8448\n", "Epoch 00013: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4165 - accuracy: 0.8448 - val_loss: 1.0851 - val_accuracy: 0.6084\n", "Epoch 14/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4116 - accuracy: 0.8426\n", "Epoch 00014: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4121 - accuracy: 0.8425 - val_loss: 1.0460 - val_accuracy: 0.6175\n", "Epoch 15/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4063 - accuracy: 0.8475\n", "Epoch 00015: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4088 - accuracy: 0.8440 - val_loss: 1.1238 - val_accuracy: 0.6123\n", "Epoch 16/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4020 - accuracy: 0.8438\n", "Epoch 00016: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4017 - accuracy: 0.8437 - val_loss: 0.9763 - val_accuracy: 0.6214\n", "Epoch 17/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4232 - accuracy: 0.8381\n", "Epoch 00017: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4033 - accuracy: 0.8489 - val_loss: 1.1832 - val_accuracy: 0.6018\n", "Epoch 18/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4126 - accuracy: 0.8399\n", "Epoch 00018: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4088 - accuracy: 0.8463 - val_loss: 1.1020 - val_accuracy: 0.6057\n", "Epoch 19/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.3977 - accuracy: 0.8470\n", "Epoch 00019: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3977 - accuracy: 0.8470 - val_loss: 1.0716 - val_accuracy: 0.6084\n", "Epoch 20/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.3991 - accuracy: 0.8491\n", "Epoch 00020: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3987 - accuracy: 0.8493 - val_loss: 1.0270 - val_accuracy: 0.6266\n", "Epoch 21/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.3976 - accuracy: 0.8494\n", "Epoch 00021: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3924 - accuracy: 0.8512 - val_loss: 1.2443 - val_accuracy: 0.6110\n", "Epoch 22/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.3903 - accuracy: 0.8599\n", "Epoch 00022: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3829 - accuracy: 0.8610 - val_loss: 1.1135 - val_accuracy: 0.6005\n", "Epoch 23/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.3909 - accuracy: 0.8513\n", "Epoch 00023: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3904 - accuracy: 0.8516 - val_loss: 1.0982 - val_accuracy: 0.6084\n", "Epoch 24/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4108 - accuracy: 0.8452\n", "Epoch 00024: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3999 - accuracy: 0.8512 - val_loss: 1.1010 - val_accuracy: 0.6136\n", "Epoch 25/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.3705 - accuracy: 0.8589\n", "Epoch 00025: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3744 - accuracy: 0.8583 - val_loss: 1.1659 - val_accuracy: 0.6188\n", "Epoch 26/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.3616 - accuracy: 0.8657\n", "Epoch 00026: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3632 - accuracy: 0.8662 - val_loss: 1.1384 - val_accuracy: 0.6292\n", "Epoch 27/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3764 - accuracy: 0.8591\n", "Epoch 00027: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3722 - accuracy: 0.8621 - val_loss: 1.1645 - val_accuracy: 0.6057\n", "Epoch 28/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.3631 - accuracy: 0.8660\n", "Epoch 00028: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3724 - accuracy: 0.8602 - val_loss: 1.1798 - val_accuracy: 0.6123\n", "Epoch 29/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.3662 - accuracy: 0.8556\n", "Epoch 00029: val_accuracy improved from 0.62924 to 0.63185, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-audio-v1/cp-0029.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3668 - accuracy: 0.8579 - val_loss: 1.1184 - val_accuracy: 0.6319\n", "Epoch 30/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.3611 - accuracy: 0.8647\n", "Epoch 00030: val_accuracy did not improve from 0.63185\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3609 - accuracy: 0.8643 - val_loss: 1.1422 - val_accuracy: 0.6110\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "Yl41VT-ias0D", "colab_type": "text" }, "source": [ "#### Load Best Model to Perform Prediction" ] }, { "cell_type": "code", "metadata": { "id": "7XT7iPJSXtiE", "colab_type": "code", "colab": {} }, "source": [ "model = tf.keras.models.load_model(\"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-audio-v1/cp-0029.h5\")\n", "\n", "y_pred = model.predict(X_valid)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "IPINaSN3b0-A", "colab_type": "code", "outputId": "5da757d4-daf8-41a2-9a7e-7be60d5497f2", "colab": { "base_uri": "https://localhost:8080/", "height": 624 } }, "source": [ "y_valid_labels = np.argmax(y_valid, axis=1) + 1\n", "y_valid_labels" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 1, 1, 2, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 3, 3, 1, 1, 2, 1,\n", " 2, 1, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3,\n", " 3, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 3, 3,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 1, 3, 3, 2, 3, 3, 3, 3,\n", " 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 1,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1,\n", " 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2,\n", " 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n", " 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,\n", " 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1,\n", " 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n", " 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 3,\n", " 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3,\n", " 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3,\n", " 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 1, 3, 2, 3, 3, 2, 2, 2, 2,\n", " 2, 1, 2, 1, 2, 1, 3, 3, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1,\n", " 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2,\n", " 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n", " 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2,\n", " 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 3, 3, 3, 3,\n", " 1, 3, 3, 3, 1, 3, 1, 3, 2, 3, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 2, 3, 3])" ] }, "metadata": { "tags": [] }, "execution_count": 33 } ] }, { "cell_type": "code", "metadata": { "id": "vv4ithDtbr3r", "colab_type": "code", "outputId": "ffbfd260-6aa8-41fa-c62f-5725de0c29c6", "colab": { "base_uri": "https://localhost:8080/", "height": 381 } }, "source": [ "y_pred_labels = np.argmax(y_pred, axis=1) + 1\n", "print(y_pred_labels)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "[3 1 2 2 1 2 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 1 1 2 2\n", " 2 3 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2\n", " 2 2 2 2 2 2 2 2 2 2 3 2 2 2 1 2 1 1 1 3 1 1 1 3 1 1 1 1 1 1 1 1 3 3 3 2 2\n", " 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 2 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 3 3 3 3 3 2 2 2 2 2 2 3 2 2 1\n", " 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 3 2\n", " 3 2 2 3 3 3 3 3 2 3 3 1 2 3 3 3 3 3 2 2 1 2 1 1 3 2 3 3 3 3 2 3 1 1 1 1 1\n", " 1 1 2 1 3 1 1 3 1 2 2 1 2 2 2 2 1 1 1 1 1 1 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2\n", " 3 2 1 1 3 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 3 3 3 3 3 3 3 3 3 3 3\n", " 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 2 2 1 1 1 1 1 1 1 1 2 2 2 2 3\n", " 2 1 3 3 2 2 2 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3\n", " 3 3 3 2 2 2 3 3 3 3 2 3 3 2 2 2 2 2 2 3 3 3 2 3 2 2 2 2 2 2 2 3 3 3 3 3 3\n", " 3 3 3 3 3 3 1 3 2 2 2 3 1 1 1 1 3 3 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 1 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2\n", " 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 1 2 2 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 2 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 3 2 1 3 1 1 1 1 1\n", " 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 3 1 3 1 3 2 3 3 1 1 1 3 1 1 1 1 1 1 1 3 3 3\n", " 3 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 1 1 3 1 3 3 2 1 1]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "VY0tll5ybslJ", "colab_type": "code", "outputId": "443f0aa2-e289-4195-c0ad-5588e0e347bb", "colab": { "base_uri": "https://localhost:8080/", "height": 597 } }, "source": [ "from sklearn import metrics \n", "import pandas as pd\n", "import seaborn as sn\n", "cm=metrics.confusion_matrix(y_valid_labels,y_pred_labels)\n", "import matplotlib.pyplot as plt\n", "\n", "classes=['Pos' , 'Neu' , 'Neg'] \n", "y_pred_final , y_true_final = y_pred_labels - 1 , y_valid_labels - 1\n", "con_mat = tensorflow.math.confusion_matrix(labels=y_true_final, predictions=y_pred_final).numpy()\n", "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n", "con_mat_df = pd.DataFrame(con_mat_norm,\n", " index = classes, \n", " columns = classes)\n", "\n", "figure = plt.figure(figsize=(11, 9))\n", "plt.title(\"FC Voting Confusion Matrix with Scene, Pose, and Audio\")\n", "\n", "\n", "plt.tick_params(labelsize=24)\n", "plt.title(\"Audio-Scene-Pose Model Confusion Matrix\", fontsize=24)\n", "sn.set(font_scale=3.0)\n", "sn.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues , vmin=0 , vmax=0.8)\n", "\n", "\n", "accuracy = (y_pred_labels == y_valid_labels).mean()\n", "print(f\"Accuracy: {accuracy}\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 0.6318537859007833\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "BItJz7Sfcdwe", "colab_type": "code", "outputId": "c94a8e46-3d2e-4ef2-ee0b-dcc11ceeb4b1", "colab": { "base_uri": "https://localhost:8080/", "height": 191 } }, "source": [ "from sklearn.metrics import classification_report\n", "target_names = [\"Positive\" , \"Neutral\" , \"Negative\"]\n", "print(classification_report(y_valid_labels, y_pred_labels, target_names=target_names, digits=3))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Positive 0.661 0.523 0.584 302\n", " Neutral 0.571 0.718 0.636 280\n", " Negative 0.714 0.679 0.696 184\n", "\n", " accuracy 0.632 766\n", " macro avg 0.649 0.640 0.639 766\n", "weighted avg 0.641 0.632 0.630 766\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "VsyF79ftwp74", "colab_type": "text" }, "source": [ "## Remove the Scene!" ] }, { "cell_type": "code", "metadata": { "id": "jJPinA8-wCC-", "colab_type": "code", "colab": {} }, "source": [ "# Create the concatenated input layer to feed into FC\n", "\n", "from src.classifiers.audio_classifier import AudioClassifier\n", "from src.classifiers.frames_classifier import FramesClassifier\n", "from src.classifiers.pose_classifier import PoseClassifier\n", "from src.classifiers.face_classifier import FaceClassifier\n", "from src.classifiers.utils import get_num_samples\n", "import numpy as np\n", "\n", "def run_classifier(layers_to_extract, audio_folder='train-final-audio', frames_folder='train-final-frames', pose_folder='train-final-pose', face_folder='train-final-fer' , labels_file=\"Train_labels.txt\"):\n", " audio_classifier = AudioClassifier(audio_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5', is_test=False)\n", " frames_classifier = FramesClassifier(frames_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5', is_test=False)\n", " pose_classifier = PoseClassifier(pose_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5', is_test=False)\n", " face_classifier = FaceClassifier(face_folder, model_location='/content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5', is_test=False)\n", " classifiers = [audio_classifier, pose_classifier , face_classifier] # frames_classifier\n", " \n", " sample_to_true_label = {}\n", " with open(labels_file) as f:\n", " l = 0\n", " for line in f:\n", " if l == 0:\n", " # Skip headers\n", " l += 1\n", " continue\n", " line_arr = line.split(\" \")\n", " sample_to_true_label[line_arr[0].strip()] = int(line_arr[1].strip()) - 1 # subtract one to make labels from 0 to 2\n", " l += 1\n", "\n", " classifier_outputs = []\n", " classifier_samples = []\n", " classifier_dim_sizes = []\n", " output_dim_size = 0\n", " num_samples = 0\n", " sample_to_row = {}\n", "\n", " for i, classifier in enumerate(classifiers):\n", " output, samples = classifier.predict(layers_to_extract[i])\n", " output_dim_size += output.shape[1]\n", " classifier_dim_sizes.append(output.shape[1])\n", " num_samples = len(samples)\n", " classifier_outputs.append(output)\n", " classifier_samples.append(samples)\n", "\n", "\n", " X_train = np.zeros(shape=(num_samples, output_dim_size))\n", " y_train = []\n", "\n", " print(f\"Number of samples: {num_samples}\")\n", " print(f\"Dim shapes: \")\n", " print(classifier_dim_sizes)\n", "\n", " for i, sample in enumerate(classifier_samples[0]):\n", " sample_to_row[sample] = i\n", " y_train.append(sample_to_true_label[sample])\n", "\n", " last_classifier_index = 0\n", " for c, output in enumerate(classifier_outputs):\n", " samples = classifier_samples[c]\n", " print(len(output))\n", " for i, row in enumerate(output):\n", " sample = samples[i]\n", " X_train[sample_to_row[sample], last_classifier_index:last_classifier_index+classifier_dim_sizes[c]] += row\n", " last_classifier_index += classifier_dim_sizes[c]\n", "\n", " return X_train, tf.keras.utils.to_categorical(y_train, num_classes=3)\n", "\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "b4f36e9b-5d92-4464-91d2-9d08c5923ad4", "id": "vQ0VttE7r25G", "colab": { "base_uri": "https://localhost:8080/", "height": 973 } }, "source": [ "import tensorflow as tf\n", "\n", "# For each classifier, extract the specific desired layer \n", "# (refer to the model summary for the layer names)\n", "\n", "layers_to_extract = [\n", " \"dense\",\n", " #\"concatenate_5\",\n", " \"bidirectional_1\",\n", " \"dense_27\",\n", "]\n", "\n", "prefix = \"final\"\n", "if IS_TINY:\n", " prefix = \"tiny\"\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\" , labels_file=\"Val_labels.txt\")\n", "\n", "X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "print(X_train.shape)\n", "print(y_train.shape)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "AudioClassifier created with audio_folder = train-final-audio , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "FramesClassifier created with frames_folder = train-final-frames , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5\n", "PoseClassifier created with pose_folder = train-final-pose , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5\n", "load data\n", "FacesClassifier created with face_folder = train-final-fer , is_test = False , model_location = /content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "Skipping unzipping files as input is a folder\n", "Customizing model by returning layer dense\n", "Skipping unzipping files as input is a folder\n", "['2', '1', '3']\n", "Found 2661 frames belonging to 2661 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer bidirectional_1\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/content/cs231n-emotiw/src/generators/pose_generator.py:135: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((lx[i] - origin_x) / len_x)\n", "/content/cs231n-emotiw/src/generators/pose_generator.py:136: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((ly[i] - origin_y) / len_y)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Customizing model by returning layer dense_27\n", "Number of samples: 2661\n", "Dim shapes: \n", "[32, 128, 8]\n", "2661\n", "2661\n", "2661\n", "AudioClassifier created with audio_folder = val-final-audio , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "FramesClassifier created with frames_folder = train-final-frames , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5\n", "PoseClassifier created with pose_folder = val-final-pose , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5\n", "load data\n", "FacesClassifier created with face_folder = val-final-fer , is_test = False , model_location = /content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "Skipping unzipping files as input is a folder\n", "Customizing model by returning layer dense\n", "Skipping unzipping files as input is a folder\n", "['2', '1', '3']\n", "Found 766 frames belonging to 766 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer bidirectional_1\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/content/cs231n-emotiw/src/generators/pose_generator.py:135: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((lx[i] - origin_x) / len_x)\n", "/content/cs231n-emotiw/src/generators/pose_generator.py:136: RuntimeWarning: invalid value encountered in double_scalars\n", " x_new.append((ly[i] - origin_y) / len_y)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Customizing model by returning layer dense_27\n", "Number of samples: 766\n", "Dim shapes: \n", "[32, 128, 8]\n", "766\n", "766\n", "766\n", "(2661, 168)\n", "(2661, 3)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "YG0XE08Gr25J", "colab": {} }, "source": [ "!ls" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "f348a6ba-1afa-40f6-d5c7-6c6d48745dc9", "id": "uxUWXzJkr25K", "colab": { "base_uri": "https://localhost:8080/", "height": 121 } }, "source": [ "# !mkdir ensemble-scene-pose-audio-face\n", "# np.save(\"ensemble-scene-pose-audio-face/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face.zip ensemble-scene-pose-audio-face\n", "# !cp ensemble-scene-pose-audio-face.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "# !mkdir ensemble-scene-pose-audio-face-v2\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face-v2.zip ensemble-scene-pose-audio-face-v2\n", "# !cp ensemble-scene-pose-audio-face-v2.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "\n", "# !mkdir ensemble-scene-pose-audio-v1\n", "# np.save(\"ensemble-scene-pose-audio-v1/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-v1/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-v1/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-v1/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-v1.zip ensemble-scene-pose-audio-v1\n", "# !cp ensemble-scene-pose-audio-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n", "\n", "\n", "!mkdir ensemble-pose-audio-face-v1\n", "np.save(\"ensemble-pose-audio-face-v1/X_train.npy\", X_train)\n", "np.save(\"ensemble-pose-audio-face-v1/y_train.npy\", y_train)\n", "np.save(\"ensemble-pose-audio-face-v1/X_valid.npy\", X_valid)\n", "np.save(\"ensemble-pose-audio-face-v1/y_valid.npy\", y_valid)\n", "!zip -r ensemble-pose-audio-face-v1.zip ensemble-pose-audio-face-v1\n", "!cp ensemble-pose-audio-face-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "mkdir: cannot create directory ‘ensemble-pose-audio-face-v1’: File exists\n", " adding: ensemble-pose-audio-face-v1/ (stored 0%)\n", " adding: ensemble-pose-audio-face-v1/y_valid.npy (deflated 96%)\n", " adding: ensemble-pose-audio-face-v1/X_valid.npy (deflated 48%)\n", " adding: ensemble-pose-audio-face-v1/X_train.npy (deflated 48%)\n", " adding: ensemble-pose-audio-face-v1/y_train.npy (deflated 97%)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LhFMS4Zzr25N" }, "source": [ "## Create FC Model (WITHOUT SCENE)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "80f61d60-3c9d-457a-aa2f-9a45a1aa1be7", "id": "L8kZjLY7r25O", "colab": { "base_uri": "https://localhost:8080/", "height": 347 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " data\t\t\t\t\t train-final-faces.zip\n", " drive\t\t\t\t\t train-final-fer\n", " ensemble-pose-audio-face-v1\t\t train-final-fer.zip\n", " ensemble-pose-audio-face-v1.zip\t train-final-frames\n", " ensemble-scene-pose-audio-v1\t\t train-final-frames.zip\n", " ensemble-scene-pose-audio-v1.zip\t train-final-pose\n", " fc-prediction\t\t\t\t train-final-pose.zip\n", " LICENSE\t\t\t\t Train_labels.txt\n", " models\t\t\t\t\t val-final-audio\n", " notebooks\t\t\t\t val-final-audio.zip\n", " README.md\t\t\t\t val-final-faces\n", " reports\t\t\t\t val-final-faces.zip\n", " requirements-predictions.txt\t\t val-final-fer\n", " requirements.txt\t\t\t val-final-fer.zip\n", "'Screen Shot 2020-05-26 at 8.53.32 PM.png' val-final-frames\n", " src\t\t\t\t\t val-final-frames.zip\n", " train-final-audio\t\t\t val-final-pose\n", " train-final-audio.zip\t\t\t val-final-pose.zip\n", " train-final-faces\t\t\t Val_labels.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "d88317b3-511a-4827-e4fb-e3dd39f2ccd8", "id": "HvKjTy9jr25Q", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "#!rm -rf ensemble-scene-pose-audio\n", "#!cp ../drive/'My Drive'/cs231n-project/datasets/emotiw/ensemble-scene-pose-audio.zip .\n", "#!unzip ensemble-scene-pose-audio.zip\n", "\n", "X_train = np.load(\"ensemble-pose-audio-face-v1/X_train.npy\")\n", "y_train = np.load(\"ensemble-pose-audio-face-v1/y_train.npy\")\n", "X_valid = np.load(\"ensemble-pose-audio-face-v1/X_valid.npy\")\n", "y_valid = np.load(\"ensemble-pose-audio-face-v1/y_valid.npy\")\n", "X_train.shape" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(2661, 168)" ] }, "metadata": { "tags": [] }, "execution_count": 54 } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "67068f2d-0a6c-403f-9586-03a22c73dde5", "id": "QifUhdEar25S", "colab": { "base_uri": "https://localhost:8080/", "height": 297 } }, "source": [ "!pip install keras-tuner" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: keras-tuner in /usr/local/lib/python3.6/dist-packages (1.0.1)\n", "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.16.0)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (2.23.0)\n", "Requirement already satisfied: colorama in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.4.3)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.22.2.post1)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (4.41.1)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.4.1)\n", "Requirement already satisfied: terminaltables in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (3.1.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.18.4)\n", "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.8.7)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (1.24.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2020.4.5.1)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2.9)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (3.0.4)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->keras-tuner) (0.15.1)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "5HV2Nfn-r25T", "colab": {} }, "source": [ "#\n", "# CONFIGURATION\n", "#\n", "# Define any constants for the model here\n", "#\n", "\n", "MODEL_NAME = \"ensemble-pose-audio-face-v1\"\n", "\n", "from pathlib import Path\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "\n", "def build_model(hp):\n", " Path(f\"drive/My Drive/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", " checkpoint_path = \"drive/My Drive/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", " cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " period=1\n", " )\n", "\n", " def create_model(inputs):\n", " x = tf.keras.layers.Dense(hp.Int('units', min_value=8, max_value=128, step=8), activation='relu', kernel_regularizer=tf.keras.regularizers.l2())(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(\n", " hp.Choice('learning_rate',\n", " values=[1e-2, 1e-3, 1e-4])),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", " inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", " model = create_model(inputs)\n", " # model.summary()\n", " return model\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "a86fc04c-18cd-4813-e7ea-2a8421a57b9d", "id": "l-eJNn9tr25V", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "from kerastuner.tuners import RandomSearch\n", "tuner = RandomSearch(\n", " build_model,\n", " objective='val_accuracy',\n", " max_trials=5,\n", " executions_per_trial=3,\n", " directory='fc-prediction-ablate-scene',\n", " project_name='fc-prediction-ablate-scene'\n", ")\n", "tuner.search_space_summary()\n", "\n", "tuner.search(X_train, y_train,\n", " epochs=30,\n", " validation_data=(X_valid, y_valid),\n", " verbose=0)\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "text/html": [ "

Search space summary

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-Default search space size: 2" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ "

units (Int)

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learning_rate (Choice)

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Trial complete

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Trial complete

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Trial complete

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Trial summary

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Trial complete

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Trial complete

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Results summary

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Please use `save_freq` to specify the frequency in number of batches seen.\n", "Epoch 1/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.6234 - accuracy: 0.7704\n", "Epoch 00001: val_accuracy improved from -inf to 0.55744, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0001.h5\n", "84/84 [==============================] - 0s 5ms/step - loss: 0.6234 - accuracy: 0.7704 - val_loss: 1.0988 - val_accuracy: 0.5574\n", "Epoch 2/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.5605 - accuracy: 0.7884\n", "Epoch 00002: val_accuracy improved from 0.55744 to 0.57050, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0002.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.5605 - accuracy: 0.7884 - val_loss: 1.0414 - val_accuracy: 0.5705\n", "Epoch 3/30\n", "63/84 [=====================>........] - ETA: 0s - loss: 0.5316 - accuracy: 0.8036\n", "Epoch 00003: val_accuracy improved from 0.57050 to 0.57311, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0003.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.5365 - accuracy: 0.8023 - val_loss: 1.0985 - val_accuracy: 0.5731\n", "Epoch 4/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.5360 - accuracy: 0.7967\n", "Epoch 00004: val_accuracy improved from 0.57311 to 0.59399, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0004.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.5360 - accuracy: 0.7967 - val_loss: 1.0735 - val_accuracy: 0.5940\n", "Epoch 5/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.5209 - accuracy: 0.8012\n", "Epoch 00005: val_accuracy did not improve from 0.59399\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.5209 - accuracy: 0.8012 - val_loss: 1.0935 - val_accuracy: 0.5862\n", "Epoch 6/30\n", "74/84 [=========================>....] - ETA: 0s - loss: 0.5055 - accuracy: 0.8155\n", "Epoch 00006: val_accuracy did not improve from 0.59399\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.5118 - accuracy: 0.8113 - val_loss: 1.0713 - val_accuracy: 0.5888\n", "Epoch 7/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.5001 - accuracy: 0.8130\n", "Epoch 00007: val_accuracy improved from 0.59399 to 0.59661, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0007.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.5104 - accuracy: 0.8110 - val_loss: 0.9810 - val_accuracy: 0.5966\n", "Epoch 8/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.5029 - accuracy: 0.8087\n", "Epoch 00008: val_accuracy did not improve from 0.59661\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.5054 - accuracy: 0.8053 - val_loss: 1.1102 - val_accuracy: 0.5849\n", "Epoch 9/30\n", "75/84 [=========================>....] - ETA: 0s - loss: 0.5053 - accuracy: 0.8142\n", "Epoch 00009: val_accuracy improved from 0.59661 to 0.59791, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0009.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.5061 - accuracy: 0.8136 - val_loss: 1.1056 - val_accuracy: 0.5979\n", "Epoch 10/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4916 - accuracy: 0.8120\n", "Epoch 00010: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.5019 - accuracy: 0.8113 - val_loss: 1.0321 - val_accuracy: 0.5849\n", "Epoch 11/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4776 - accuracy: 0.8231\n", "Epoch 00011: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4941 - accuracy: 0.8140 - val_loss: 1.1497 - val_accuracy: 0.5836\n", "Epoch 12/30\n", "79/84 [===========================>..] - ETA: 0s - loss: 0.4898 - accuracy: 0.8165\n", "Epoch 00012: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4871 - accuracy: 0.8181 - val_loss: 1.2121 - val_accuracy: 0.5731\n", "Epoch 13/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4833 - accuracy: 0.8207\n", "Epoch 00013: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4807 - accuracy: 0.8211 - val_loss: 1.1452 - val_accuracy: 0.5796\n", "Epoch 14/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4891 - accuracy: 0.8099\n", "Epoch 00014: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4912 - accuracy: 0.8098 - val_loss: 1.1868 - val_accuracy: 0.5836\n", "Epoch 15/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4781 - accuracy: 0.8135\n", "Epoch 00015: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4771 - accuracy: 0.8211 - val_loss: 1.2087 - val_accuracy: 0.5875\n", "Epoch 16/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4684 - accuracy: 0.8261\n", "Epoch 00016: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4688 - accuracy: 0.8256 - val_loss: 1.1262 - val_accuracy: 0.5692\n", "Epoch 17/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4950 - accuracy: 0.8071\n", "Epoch 00017: val_accuracy did not improve from 0.59791\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4822 - accuracy: 0.8166 - val_loss: 1.3370 - val_accuracy: 0.5836\n", "Epoch 18/30\n", "67/84 [======================>.......] - ETA: 0s - loss: 0.4792 - accuracy: 0.8223\n", "Epoch 00018: val_accuracy improved from 0.59791 to 0.60183, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0018.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4764 - accuracy: 0.8230 - val_loss: 1.2106 - val_accuracy: 0.6018\n", "Epoch 19/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4708 - accuracy: 0.8249\n", "Epoch 00019: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4707 - accuracy: 0.8249 - val_loss: 1.2655 - val_accuracy: 0.5901\n", "Epoch 20/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4696 - accuracy: 0.8170\n", "Epoch 00020: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4698 - accuracy: 0.8174 - val_loss: 1.2340 - val_accuracy: 0.5796\n", "Epoch 21/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4722 - accuracy: 0.8168\n", "Epoch 00021: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4719 - accuracy: 0.8181 - val_loss: 1.1117 - val_accuracy: 0.5888\n", "Epoch 22/30\n", "67/84 [======================>.......] - ETA: 0s - loss: 0.4798 - accuracy: 0.8172\n", "Epoch 00022: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4713 - accuracy: 0.8230 - val_loss: 1.3189 - val_accuracy: 0.5888\n", "Epoch 23/30\n", "79/84 [===========================>..] - ETA: 0s - loss: 0.4654 - accuracy: 0.8303\n", "Epoch 00023: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4636 - accuracy: 0.8294 - val_loss: 1.1896 - val_accuracy: 0.5940\n", "Epoch 24/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4763 - accuracy: 0.8216\n", "Epoch 00024: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4650 - accuracy: 0.8253 - val_loss: 1.1915 - val_accuracy: 0.5888\n", "Epoch 25/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4505 - accuracy: 0.8306\n", "Epoch 00025: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4582 - accuracy: 0.8328 - val_loss: 1.1605 - val_accuracy: 0.5822\n", "Epoch 26/30\n", "77/84 [==========================>...] - ETA: 0s - loss: 0.4536 - accuracy: 0.8283\n", "Epoch 00026: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4459 - accuracy: 0.8320 - val_loss: 1.2933 - val_accuracy: 0.5822\n", "Epoch 27/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4407 - accuracy: 0.8361\n", "Epoch 00027: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4455 - accuracy: 0.8328 - val_loss: 1.3122 - val_accuracy: 0.5601\n", "Epoch 28/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4398 - accuracy: 0.8381\n", "Epoch 00028: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4470 - accuracy: 0.8339 - val_loss: 1.1357 - val_accuracy: 0.5966\n", "Epoch 29/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4609 - accuracy: 0.8319\n", "Epoch 00029: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4568 - accuracy: 0.8313 - val_loss: 1.2523 - val_accuracy: 0.5875\n", "Epoch 30/30\n", "63/84 [=====================>........] - ETA: 0s - loss: 0.4549 - accuracy: 0.8229\n", "Epoch 00030: val_accuracy did not improve from 0.60183\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4552 - accuracy: 0.8256 - val_loss: 1.2709 - val_accuracy: 0.5783\n" ], "name": "stdout" }, { 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Gx9Kj278r25b" }, "source": [ "#### Load Best Model to Perform Prediction" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "kfwsPZFbr25c", "colab": {} }, "source": [ "model = tf.keras.models.load_model(\"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-pose-audio-face-v1/cp-0018.h5\")\n", "\n", "y_pred = model.predict(X_valid)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "85c0d2c2-5c14-4857-ac7f-edbb113861a5", "id": "o295UR9Tr25d", "colab": { "base_uri": "https://localhost:8080/", "height": 624 } }, "source": [ "y_valid_labels = np.argmax(y_valid, axis=1) + 1\n", "y_valid_labels" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 1, 1, 2, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 3, 3, 1, 1, 2, 1,\n", " 2, 1, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3,\n", " 3, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 3, 3,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 1, 3, 3, 2, 3, 3, 3, 3,\n", " 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 1,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1,\n", " 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2,\n", " 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n", " 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,\n", " 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1,\n", " 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n", " 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 3,\n", " 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3,\n", " 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3,\n", " 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 1, 3, 2, 3, 3, 2, 2, 2, 2,\n", " 2, 1, 2, 1, 2, 1, 3, 3, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1,\n", " 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2,\n", " 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n", " 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2,\n", " 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 3, 3, 3, 3,\n", " 1, 3, 3, 3, 1, 3, 1, 3, 2, 3, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 2, 3, 3])" ] }, "metadata": { "tags": [] }, "execution_count": 72 } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "9a7f00e0-bc83-4338-944c-7048bc35c707", "id": "a0_78BY1r25f", "colab": { "base_uri": "https://localhost:8080/", "height": 381 } }, "source": [ "y_pred_labels = np.argmax(y_pred, axis=1) + 1\n", "print(y_pred_labels)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "[3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 2 2 3 1 2 2 3 3 3 2 2 1 3 3 1 3 2\n", " 2 3 1 1 1 1 1 2 1 3 3 1 1 3 1 2 1 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2\n", " 2 2 2 2 2 2 2 2 2 2 3 2 3 2 1 2 1 1 1 1 1 1 1 3 1 3 3 3 3 3 1 3 3 3 3 2 2\n", " 3 3 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3\n", " 3 3 1 3 2 1 3 1 1 1 1 1 1 3 1 1 1 1 1 1 3 3 3 3 3 3 3 3 2 2 2 2 2 3 2 3 3\n", " 3 3 3 3 1 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 3 2\n", " 1 2 2 3 3 3 3 3 2 3 3 2 2 3 3 3 3 3 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 2 2 2 2\n", " 2 2 2 2 3 2 2 3 2 2 2 3 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 2 2 2 2\n", " 3 2 3 3 3 2 1 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 3 2 1 2 2 2 3 3 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 1 1 1 3 3 3 3 3 3 3 3 3 3 3\n", " 3 3 3 3 3 3 3 3 3 3 2 1 2 2 2 2 2 1 1 1 3 2 2 2 3 3 3 3 1 3 3 3 2 2 2 2 3\n", " 2 3 3 3 2 2 2 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3\n", " 3 3 3 2 2 2 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 2 3 2 2 2 2 2 1 2 3 3 3 3 3 3\n", " 3 3 3 3 3 3 1 3 1 2 2 3 1 1 1 1 3 1 1 1 1 1 3 1 3 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 1 1 2 2 2 1 2 2\n", " 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 2\n", " 2 1 2 2 2 3 2 2 2 2 2 2 1 2 2 3 2 2 1 2 1 1 2 1 1 1 1 1 1 3 1 1 3 3 1 1 1\n", " 1 1 1 1 1 2 1 1 1 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 2 1 1 2 1 1 3 1 1 1 3\n", " 3 1 3 3 2 1 1 1 1 1 1 1 3 1 3 3 1 3 1 3 2 3 3 1 1 3 3 1 3 3 3 3 3 1 3 3 3\n", " 3 1 1 3 1 1 1 1 3 1 1 1 3 1 3 3 1 1 3 3 3 2 3 2 3 3]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "5bf0e5b0-bfd4-4fe6-aed1-32344ec06bf3", "id": "BOucWsRhr25h", "colab": { "base_uri": "https://localhost:8080/", "height": 597 } }, "source": [ "from sklearn import metrics \n", "import pandas as pd\n", "import seaborn as sn\n", "cm=metrics.confusion_matrix(y_valid_labels,y_pred_labels)\n", "import matplotlib.pyplot as plt\n", "\n", "classes=['Pos' , 'Neu' , 'Neg'] \n", "y_pred_final , y_true_final = y_pred_labels - 1 , y_valid_labels - 1\n", "con_mat = tensorflow.math.confusion_matrix(labels=y_true_final, predictions=y_pred_final).numpy()\n", "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n", "con_mat_df = pd.DataFrame(con_mat_norm,\n", " index = classes, \n", " columns = classes)\n", "\n", "figure = plt.figure(figsize=(11, 9))\n", "plt.title(\"FC Voting Confusion Matrix with Scene, Pose, and Audio\")\n", "\n", "\n", "plt.tick_params(labelsize=24)\n", "plt.title(\"Audio-Pose-FER Model Confusion Matrix\", fontsize=24)\n", "sn.set(font_scale=3.0)\n", "sn.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues , vmin=0 , vmax=0.8)\n", "\n", "\n", "accuracy = (y_pred_labels == y_valid_labels).mean()\n", "print(f\"Accuracy: {accuracy}\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 0.6018276762402088\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "1895c6ec-ed7e-40e7-f2d2-92a5a78e0660", "id": "02g5uc2ar25i", "colab": { "base_uri": "https://localhost:8080/", "height": 191 } }, "source": [ "from sklearn.metrics import classification_report\n", "target_names = [\"Positive\" , \"Neutral\" , \"Negative\"]\n", "print(classification_report(y_valid_labels, y_pred_labels, target_names=target_names, digits=3))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Positive 0.720 0.434 0.541 302\n", " Neutral 0.581 0.682 0.627 280\n", " Negative 0.545 0.755 0.633 184\n", "\n", " accuracy 0.602 766\n", " macro avg 0.615 0.624 0.601 766\n", "weighted avg 0.627 0.602 0.595 766\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AmMsKUMF5YI8" }, "source": [ "## Remove the Pose!" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "cRyBKFFi5YI-", "colab": {} }, "source": [ "# Create the concatenated input layer to feed into FC\n", "\n", "from src.classifiers.audio_classifier import AudioClassifier\n", "from src.classifiers.frames_classifier import FramesClassifier\n", "from src.classifiers.pose_classifier import PoseClassifier\n", "from src.classifiers.face_classifier import FaceClassifier\n", "from src.classifiers.utils import get_num_samples\n", "import numpy as np\n", "\n", "def run_classifier(layers_to_extract, audio_folder='train-final-audio', frames_folder='train-final-frames', pose_folder='train-final-pose', face_folder='train-final-fer' , labels_file=\"Train_labels.txt\"):\n", " audio_classifier = AudioClassifier(audio_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5', is_test=False)\n", " frames_classifier = FramesClassifier(frames_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5', is_test=False)\n", " pose_classifier = PoseClassifier(pose_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5', is_test=False)\n", " face_classifier = FaceClassifier(face_folder, model_location='/content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5', is_test=False)\n", " classifiers = [audio_classifier, frames_classifier , face_classifier] # pose_classifier\n", " \n", " sample_to_true_label = {}\n", " with open(labels_file) as f:\n", " l = 0\n", " for line in f:\n", " if l == 0:\n", " # Skip headers\n", " l += 1\n", " continue\n", " line_arr = line.split(\" \")\n", " sample_to_true_label[line_arr[0].strip()] = int(line_arr[1].strip()) - 1 # subtract one to make labels from 0 to 2\n", " l += 1\n", "\n", " classifier_outputs = []\n", " classifier_samples = []\n", " classifier_dim_sizes = []\n", " output_dim_size = 0\n", " num_samples = 0\n", " sample_to_row = {}\n", "\n", " for i, classifier in enumerate(classifiers):\n", " output, samples = classifier.predict(layers_to_extract[i])\n", " output_dim_size += output.shape[1]\n", " classifier_dim_sizes.append(output.shape[1])\n", " num_samples = len(samples)\n", " classifier_outputs.append(output)\n", " classifier_samples.append(samples)\n", "\n", "\n", " X_train = np.zeros(shape=(num_samples, output_dim_size))\n", " y_train = []\n", "\n", " print(f\"Number of samples: {num_samples}\")\n", " print(f\"Dim shapes: \")\n", " print(classifier_dim_sizes)\n", "\n", " for i, sample in enumerate(classifier_samples[0]):\n", " sample_to_row[sample] = i\n", " y_train.append(sample_to_true_label[sample])\n", "\n", " last_classifier_index = 0\n", " for c, output in enumerate(classifier_outputs):\n", " samples = classifier_samples[c]\n", " print(len(output))\n", " for i, row in enumerate(output):\n", " sample = samples[i]\n", " X_train[sample_to_row[sample], last_classifier_index:last_classifier_index+classifier_dim_sizes[c]] += row\n", " last_classifier_index += classifier_dim_sizes[c]\n", "\n", " return X_train, tf.keras.utils.to_categorical(y_train, num_classes=3)\n", "\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "f0050819-a6f4-47c6-b1dc-67199f2d94ef", "id": "WgEomMFY5YJB", "colab": { "base_uri": "https://localhost:8080/", "height": 800 } }, "source": [ "import tensorflow as tf\n", "\n", "# For each classifier, extract the specific desired layer \n", "# (refer to the model summary for the layer names)\n", "\n", "layers_to_extract = [\n", " \"dense\",\n", " \"concatenate_5\",\n", " #\"bidirectional_1\",\n", " \"dense_27\",\n", "]\n", "\n", "prefix = \"final\"\n", "if IS_TINY:\n", " prefix = \"tiny\"\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\" , labels_file=\"Val_labels.txt\")\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "print(X_train.shape)\n", "print(y_train.shape)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "AudioClassifier created with audio_folder = train-final-audio , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "FramesClassifier created with frames_folder = train-final-frames , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5\n", "PoseClassifier created with pose_folder = train-final-pose , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5\n", "load data\n", "FacesClassifier created with face_folder = train-final-fer , is_test = False , model_location = /content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "Skipping unzipping files as input is a folder\n", "Customizing model by returning layer dense\n", "Skipping unzipping files as input is a folder\n", "Found 2661 frames belonging to 2661 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer concatenate_5\n", "Customizing model by returning layer dense_27\n", "Number of samples: 2661\n", "Dim shapes: \n", "[32, 30, 8]\n", "2661\n", "2661\n", "2661\n", "AudioClassifier created with audio_folder = val-final-audio , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "FramesClassifier created with frames_folder = val-final-frames , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5\n", "PoseClassifier created with pose_folder = train-final-pose , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5\n", "load data\n", "FacesClassifier created with face_folder = val-final-fer , is_test = False , model_location = /content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "Skipping unzipping files as input is a folder\n", "Customizing model by returning layer dense\n", "Skipping unzipping files as input is a folder\n", "Found 766 frames belonging to 766 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer concatenate_5\n", "Customizing model by returning layer dense_27\n", "Number of samples: 766\n", "Dim shapes: \n", "[32, 30, 8]\n", "766\n", "766\n", "766\n", "(2661, 70)\n", "(2661, 3)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "hcQSHHkQ5YJE", "colab": {} }, "source": [ "!ls" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "7c2034dc-ee0d-4a2c-f67f-c20b0c236ee7", "id": "L6YIBOC35YJG", "colab": { "base_uri": "https://localhost:8080/", "height": 121 } }, "source": [ "# !mkdir ensemble-scene-pose-audio-face\n", "# np.save(\"ensemble-scene-pose-audio-face/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face.zip ensemble-scene-pose-audio-face\n", "# !cp ensemble-scene-pose-audio-face.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "# !mkdir ensemble-scene-pose-audio-face-v2\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face-v2.zip ensemble-scene-pose-audio-face-v2\n", "# !cp ensemble-scene-pose-audio-face-v2.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "\n", "# !mkdir ensemble-scene-pose-audio-v1\n", "# np.save(\"ensemble-scene-pose-audio-v1/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-v1/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-v1/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-v1/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-v1.zip ensemble-scene-pose-audio-v1\n", "# !cp ensemble-scene-pose-audio-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n", "\n", "\n", "# !mkdir ensemble-pose-audio-face-v1\n", "# np.save(\"ensemble-pose-audio-face-v1/X_train.npy\", X_train)\n", "# np.save(\"ensemble-pose-audio-face-v1/y_train.npy\", y_train)\n", "# np.save(\"ensemble-pose-audio-face-v1/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-pose-audio-face-v1/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-pose-audio-face-v1.zip ensemble-pose-audio-face-v1\n", "# !cp ensemble-pose-audio-face-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n", "\n", "!mkdir ensemble-scene-audio-face-v1\n", "np.save(\"ensemble-scene-audio-face-v1/X_train.npy\", X_train)\n", "np.save(\"ensemble-scene-audio-face-v1/y_train.npy\", y_train)\n", "np.save(\"ensemble-scene-audio-face-v1/X_valid.npy\", X_valid)\n", "np.save(\"ensemble-scene-audio-face-v1/y_valid.npy\", y_valid)\n", "!zip -r ensemble-scene-audio-face-v1.zip ensemble-scene-audio-face-v1\n", "!cp ensemble-scene-audio-face-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n", "\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "mkdir: cannot create directory ‘ensemble-scene-audio-face-v1’: File exists\n", "updating: ensemble-scene-audio-face-v1/ (stored 0%)\n", "updating: ensemble-scene-audio-face-v1/y_valid.npy (deflated 96%)\n", "updating: ensemble-scene-audio-face-v1/X_valid.npy (deflated 51%)\n", "updating: ensemble-scene-audio-face-v1/X_train.npy (deflated 51%)\n", "updating: ensemble-scene-audio-face-v1/y_train.npy (deflated 97%)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "wOInsOez5YJI" }, "source": [ "## Create FC Model (WITHOUT POSE)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "80f61d60-3c9d-457a-aa2f-9a45a1aa1be7", "id": "0c24Epu35YJI", "colab": { "base_uri": "https://localhost:8080/", "height": 347 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " data\t\t\t\t\t train-final-faces.zip\n", " drive\t\t\t\t\t train-final-fer\n", " ensemble-pose-audio-face-v1\t\t train-final-fer.zip\n", " ensemble-pose-audio-face-v1.zip\t train-final-frames\n", " ensemble-scene-pose-audio-v1\t\t train-final-frames.zip\n", " ensemble-scene-pose-audio-v1.zip\t train-final-pose\n", " fc-prediction\t\t\t\t train-final-pose.zip\n", " LICENSE\t\t\t\t Train_labels.txt\n", " models\t\t\t\t\t val-final-audio\n", " notebooks\t\t\t\t val-final-audio.zip\n", " README.md\t\t\t\t val-final-faces\n", " reports\t\t\t\t val-final-faces.zip\n", " requirements-predictions.txt\t\t val-final-fer\n", " requirements.txt\t\t\t val-final-fer.zip\n", "'Screen Shot 2020-05-26 at 8.53.32 PM.png' val-final-frames\n", " src\t\t\t\t\t val-final-frames.zip\n", " train-final-audio\t\t\t val-final-pose\n", " train-final-audio.zip\t\t\t val-final-pose.zip\n", " train-final-faces\t\t\t Val_labels.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "39b34b01-9b2a-4eb1-d43c-c8cf7050c2fa", "id": "RoUAKe2_5YJK", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "#!rm -rf ensemble-scene-pose-audio\n", "#!cp ../drive/'My Drive'/cs231n-project/datasets/emotiw/ensemble-scene-pose-audio.zip .\n", "#!unzip ensemble-scene-pose-audio.zip\n", "\n", "X_train = np.load(\"ensemble-scene-audio-face-v1/X_train.npy\")\n", "y_train = np.load(\"ensemble-scene-audio-face-v1/y_train.npy\")\n", "X_valid = np.load(\"ensemble-scene-audio-face-v1/X_valid.npy\")\n", "y_valid = np.load(\"ensemble-scene-audio-face-v1/y_valid.npy\")\n", "X_train.shape" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(2661, 70)" ] }, "metadata": { "tags": [] }, "execution_count": 82 } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "67068f2d-0a6c-403f-9586-03a22c73dde5", "id": "MThrvpeq5YJM", "colab": { "base_uri": "https://localhost:8080/", "height": 297 } }, "source": [ "!pip install keras-tuner" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: keras-tuner in /usr/local/lib/python3.6/dist-packages (1.0.1)\n", "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.16.0)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (2.23.0)\n", "Requirement already satisfied: colorama in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.4.3)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.22.2.post1)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (4.41.1)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.4.1)\n", "Requirement already satisfied: terminaltables in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (3.1.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.18.4)\n", "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.8.7)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (1.24.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2020.4.5.1)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2.9)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (3.0.4)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->keras-tuner) (0.15.1)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "R2Hp76dT5YJO", "colab": {} }, "source": [ "#\n", "# CONFIGURATION\n", "#\n", "# Define any constants for the model here\n", "#\n", "\n", "MODEL_NAME = \"ensemble-scene-audio-face-v1\"\n", "\n", "from pathlib import Path\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "\n", "def build_model(hp):\n", " Path(f\"drive/My Drive/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", " checkpoint_path = \"drive/My Drive/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", " cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " period=1\n", " )\n", "\n", " def create_model(inputs):\n", " x = tf.keras.layers.Dense(hp.Int('units', min_value=8, max_value=128, step=8), activation='relu', kernel_regularizer=tf.keras.regularizers.l2())(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(\n", " hp.Choice('learning_rate',\n", " values=[1e-2, 1e-3, 1e-4])),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", " inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", " model = create_model(inputs)\n", " # model.summary()\n", " return model\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "9b4ced24-0fa3-45aa-dc03-693eadcf51f3", "id": "qMiYPmtD5YJR", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "from kerastuner.tuners import RandomSearch\n", "tuner = RandomSearch(\n", " build_model,\n", " objective='val_accuracy',\n", " max_trials=5,\n", " executions_per_trial=3,\n", " directory='fc-prediction-ablate-pose',\n", " project_name='fc-prediction-ablate-pose'\n", ")\n", "tuner.search_space_summary()\n", "\n", "tuner.search(X_train, y_train,\n", " epochs=30,\n", " validation_data=(X_valid, y_valid),\n", " verbose=0)\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "text/html": [ "

Search space summary

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-Default search space size: 2" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ "

units (Int)

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-default: None" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-max_value: 128" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-min_value: 8" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-sampling: None" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-step: 8" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ "

learning_rate (Choice)

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Trial complete

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Trial complete

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Trial complete

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Trial complete

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Please use `save_freq` to specify the frequency in number of batches seen.\n", "Epoch 1/30\n", "77/84 [==========================>...] - ETA: 0s - loss: 0.5320 - accuracy: 0.8011\n", "Epoch 00001: val_accuracy improved from -inf to 0.60444, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-audio-face-v1/cp-0001.h5\n", "84/84 [==============================] - 0s 5ms/step - loss: 0.5322 - accuracy: 0.8023 - val_loss: 0.9022 - val_accuracy: 0.6044\n", "Epoch 2/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4688 - accuracy: 0.8350\n", "Epoch 00002: val_accuracy improved from 0.60444 to 0.61488, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-audio-face-v1/cp-0002.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4729 - accuracy: 0.8294 - val_loss: 0.9573 - val_accuracy: 0.6149\n", "Epoch 3/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4643 - accuracy: 0.8205\n", "Epoch 00003: val_accuracy improved from 0.61488 to 0.62141, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-audio-face-v1/cp-0003.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4647 - accuracy: 0.8200 - val_loss: 0.9636 - val_accuracy: 0.6214\n", "Epoch 4/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4581 - accuracy: 0.8309\n", "Epoch 00004: val_accuracy did not improve from 0.62141\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4533 - accuracy: 0.8328 - val_loss: 1.0489 - val_accuracy: 0.6097\n", "Epoch 5/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4474 - accuracy: 0.8365\n", "Epoch 00005: val_accuracy did not improve from 0.62141\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4471 - accuracy: 0.8362 - val_loss: 1.0203 - val_accuracy: 0.6149\n", "Epoch 6/30\n", "79/84 [===========================>..] - ETA: 0s - loss: 0.4480 - accuracy: 0.8354\n", "Epoch 00006: val_accuracy improved from 0.62141 to 0.62924, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-audio-face-v1/cp-0006.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4459 - accuracy: 0.8354 - val_loss: 0.9445 - val_accuracy: 0.6292\n", "Epoch 7/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.4373 - accuracy: 0.8369\n", "Epoch 00007: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4373 - accuracy: 0.8369 - val_loss: 1.0066 - val_accuracy: 0.6057\n", "Epoch 8/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4471 - accuracy: 0.8313\n", "Epoch 00008: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4469 - accuracy: 0.8316 - val_loss: 0.9397 - val_accuracy: 0.6253\n", "Epoch 9/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4312 - accuracy: 0.8338\n", "Epoch 00009: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4338 - accuracy: 0.8339 - val_loss: 1.0310 - val_accuracy: 0.6123\n", "Epoch 10/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4315 - accuracy: 0.8363\n", "Epoch 00010: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4266 - accuracy: 0.8380 - val_loss: 1.2536 - val_accuracy: 0.5966\n", "Epoch 11/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4327 - accuracy: 0.8409\n", "Epoch 00011: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8422 - val_loss: 0.9502 - val_accuracy: 0.6149\n", "Epoch 12/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4094 - accuracy: 0.8527\n", "Epoch 00012: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4173 - accuracy: 0.8493 - val_loss: 1.0912 - val_accuracy: 0.6057\n", "Epoch 13/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4086 - accuracy: 0.8527\n", "Epoch 00013: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4098 - accuracy: 0.8519 - val_loss: 0.9902 - val_accuracy: 0.6136\n", "Epoch 14/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4107 - accuracy: 0.8526\n", "Epoch 00014: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4138 - accuracy: 0.8512 - val_loss: 1.0275 - val_accuracy: 0.6149\n", "Epoch 15/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.4121 - accuracy: 0.8531\n", "Epoch 00015: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4121 - accuracy: 0.8531 - val_loss: 1.0142 - val_accuracy: 0.6084\n", "Epoch 16/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4076 - accuracy: 0.8514\n", "Epoch 00016: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4055 - accuracy: 0.8519 - val_loss: 1.0155 - val_accuracy: 0.6149\n", "Epoch 17/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4023 - accuracy: 0.8514\n", "Epoch 00017: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4087 - accuracy: 0.8516 - val_loss: 0.9908 - val_accuracy: 0.6253\n", "Epoch 18/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.4056 - accuracy: 0.8526\n", "Epoch 00018: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4094 - accuracy: 0.8508 - val_loss: 1.0519 - val_accuracy: 0.6175\n", "Epoch 19/30\n", "79/84 [===========================>..] - ETA: 0s - loss: 0.4083 - accuracy: 0.8521\n", "Epoch 00019: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4058 - accuracy: 0.8519 - val_loss: 1.1045 - val_accuracy: 0.6057\n", "Epoch 20/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.4017 - accuracy: 0.8557\n", "Epoch 00020: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4017 - accuracy: 0.8557 - val_loss: 1.0762 - val_accuracy: 0.5979\n", "Epoch 21/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.3970 - accuracy: 0.8554\n", "Epoch 00021: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3958 - accuracy: 0.8568 - val_loss: 1.1321 - val_accuracy: 0.6097\n", "Epoch 22/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4040 - accuracy: 0.8540\n", "Epoch 00022: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4022 - accuracy: 0.8542 - val_loss: 1.0151 - val_accuracy: 0.6097\n", "Epoch 23/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.3890 - accuracy: 0.8539\n", "Epoch 00023: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3885 - accuracy: 0.8549 - val_loss: 1.0437 - val_accuracy: 0.6292\n", "Epoch 24/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.3877 - accuracy: 0.8569\n", "Epoch 00024: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3872 - accuracy: 0.8572 - val_loss: 1.1373 - val_accuracy: 0.6110\n", "Epoch 25/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.3816 - accuracy: 0.8647\n", "Epoch 00025: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3926 - accuracy: 0.8595 - val_loss: 1.1029 - val_accuracy: 0.6136\n", "Epoch 26/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.3874 - accuracy: 0.8623\n", "Epoch 00026: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3866 - accuracy: 0.8617 - val_loss: 1.0993 - val_accuracy: 0.6097\n", "Epoch 27/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.3910 - accuracy: 0.8623\n", "Epoch 00027: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3886 - accuracy: 0.8632 - val_loss: 1.0959 - val_accuracy: 0.5979\n", "Epoch 28/30\n", "77/84 [==========================>...] - ETA: 0s - loss: 0.3863 - accuracy: 0.8608\n", "Epoch 00028: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3885 - accuracy: 0.8591 - val_loss: 1.0447 - val_accuracy: 0.5979\n", "Epoch 29/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.3811 - accuracy: 0.8614\n", "Epoch 00029: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3809 - accuracy: 0.8617 - val_loss: 1.1244 - val_accuracy: 0.5992\n", "Epoch 30/30\n", "67/84 [======================>.......] - ETA: 0s - loss: 0.3709 - accuracy: 0.8633\n", "Epoch 00030: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3809 - accuracy: 0.8572 - val_loss: 1.0800 - val_accuracy: 0.6018\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "h3OH3LnN5YJW" }, "source": [ "#### Load Best Model to Perform Prediction" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "u3a6AnuX5YJX", "colab": {} }, "source": [ "model = tf.keras.models.load_model(\"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-audio-face-v1/cp-0006.h5\")\n", "\n", "y_pred = model.predict(X_valid)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "0920a459-28a5-40f9-8159-f32f2d12aee4", "id": "MlNpYSp55YJZ", "colab": { "base_uri": "https://localhost:8080/", "height": 624 } }, "source": [ "y_valid_labels = np.argmax(y_valid, axis=1) + 1\n", "y_valid_labels" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 1, 1, 2, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 3, 3, 1, 1, 2, 1,\n", " 2, 1, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3,\n", " 3, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 3, 3,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 1, 3, 3, 2, 3, 3, 3, 3,\n", " 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 1,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1,\n", " 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2,\n", " 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n", " 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,\n", " 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1,\n", " 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n", " 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 3,\n", " 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3,\n", " 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3,\n", " 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 1, 3, 2, 3, 3, 2, 2, 2, 2,\n", " 2, 1, 2, 1, 2, 1, 3, 3, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1,\n", " 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2,\n", " 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n", " 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2,\n", " 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 3, 3, 3, 3,\n", " 1, 3, 3, 3, 1, 3, 1, 3, 2, 3, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 2, 3, 3])" ] }, "metadata": { "tags": [] }, "execution_count": 89 } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "70ddd0cd-4d8d-4e9b-caec-9dcdeb77e928", "id": "WYe795YW5YJb", "colab": { "base_uri": "https://localhost:8080/", "height": 381 } }, "source": [ "y_pred_labels = np.argmax(y_pred, axis=1) + 1\n", "print(y_pred_labels)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "[3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 2 1 2 2 3 2 2 1 3 3 1 2 2\n", " 2 3 1 1 1 1 1 1 1 3 3 1 1 1 1 2 1 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 1 1 1 3 1 1 1 3 1 1 1 3 2 2 2 2 2\n", " 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 3 3 1 3 2 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 2 2 3 3 3 3 2 2 2 2 3 3 2 3 3\n", " 3 3 3 3 1 3 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 3 2\n", " 3 2 2 3 3 3 3 3 2 3 3 1 2 3 3 3 3 3 2 2 2 2 1 1 3 1 3 3 3 3 2 3 3 1 1 2 1\n", " 1 1 2 1 3 1 1 3 1 2 2 3 2 1 2 2 1 1 1 2 1 1 2 1 1 2 1 2 1 2 2 2 1 2 2 2 2\n", " 3 2 1 1 3 2 1 2 2 2 2 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 3 3 3 3 3 3 3 3 3 3 3\n", " 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 2 3 3 1 1 1 1 1 1 3 2 2 2 2 3\n", " 2 3 3 3 2 2 2 3 2 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3\n", " 3 3 3 2 2 2 3 3 3 3 2 3 3 3 2 2 2 2 2 3 3 3 2 3 3 2 2 3 2 2 2 3 3 3 3 3 3\n", " 3 3 3 3 3 3 1 3 1 2 2 3 1 1 1 1 3 3 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 1 1 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2\n", " 2 1 2 2 2 1 1 2 1 2 2 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 2 1 3 1 2 1 2 2 2 2 1 2 1 2 2 1 2 2 2 1 2 2 1 3 2 1 1 3 1 3 3 3\n", " 1 1 3 3 2 1 3 1 1 1 1 1 3 1 3 3 3 3 1 3 3 3 3 1 1 3 3 1 1 1 3 3 3 1 3 3 3\n", " 3 1 1 3 1 1 1 1 3 1 1 1 3 1 3 3 1 1 3 3 3 3 3 2 3 3]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "25b32356-2ac5-4c19-8aae-cd7397846e18", "id": "vg4hLc9m5YJc", "colab": { "base_uri": "https://localhost:8080/", "height": 597 } }, "source": [ "from sklearn import metrics \n", "import pandas as pd\n", "import seaborn as sn\n", "cm=metrics.confusion_matrix(y_valid_labels,y_pred_labels)\n", "import matplotlib.pyplot as plt\n", "\n", "classes=['Pos' , 'Neu' , 'Neg'] \n", "y_pred_final , y_true_final = y_pred_labels - 1 , y_valid_labels - 1\n", "con_mat = tensorflow.math.confusion_matrix(labels=y_true_final, predictions=y_pred_final).numpy()\n", "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n", "con_mat_df = pd.DataFrame(con_mat_norm,\n", " index = classes, \n", " columns = classes)\n", "\n", "figure = plt.figure(figsize=(11, 9))\n", "plt.title(\"FC Voting Confusion Matrix with Scene, Pose, and Audio\")\n", "\n", "\n", "plt.tick_params(labelsize=24)\n", "plt.title(\"Audio-Scene-FER Model Confusion Matrix\", fontsize=24)\n", "sn.set(font_scale=3.0)\n", "sn.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues , vmin=0 , vmax=0.8)\n", "\n", "\n", "accuracy = (y_pred_labels == y_valid_labels).mean()\n", "print(f\"Accuracy: {accuracy}\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 0.6292428198433421\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "5020e36e-5be4-47c1-dc70-32f203301c76", "id": "GOykXkBQ5YJe", "colab": { "base_uri": "https://localhost:8080/", "height": 191 } }, "source": [ "from sklearn.metrics import classification_report\n", "target_names = [\"Positive\" , \"Neutral\" , \"Negative\"]\n", "print(classification_report(y_valid_labels, y_pred_labels, target_names=target_names, digits=3))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Positive 0.713 0.526 0.606 302\n", " Neutral 0.582 0.661 0.619 280\n", " Negative 0.613 0.750 0.675 184\n", "\n", " accuracy 0.629 766\n", " macro avg 0.636 0.646 0.633 766\n", "weighted avg 0.641 0.629 0.627 766\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6zaopmqk8fFS" }, "source": [ "## Remove the Audio!" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "cO9sL9bI8fFU", "colab": {} }, "source": [ "# Create the concatenated input layer to feed into FC\n", "\n", "from src.classifiers.audio_classifier import AudioClassifier\n", "from src.classifiers.frames_classifier import FramesClassifier\n", "from src.classifiers.pose_classifier import PoseClassifier\n", "from src.classifiers.face_classifier import FaceClassifier\n", "from src.classifiers.utils import get_num_samples\n", "import numpy as np\n", "\n", "def run_classifier(layers_to_extract, audio_folder='train-final-audio', frames_folder='train-final-frames', pose_folder='train-final-pose', face_folder='train-final-fer' , labels_file=\"Train_labels.txt\"):\n", " audio_classifier = AudioClassifier(audio_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5', is_test=False)\n", " frames_classifier = FramesClassifier(frames_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5', is_test=False)\n", " pose_classifier = PoseClassifier(pose_folder, model_location='https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5', is_test=False)\n", " face_classifier = FaceClassifier(face_folder, model_location='/content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5', is_test=False)\n", " classifiers = [pose_classifier, frames_classifier , face_classifier] # audio_classifier\n", " \n", " sample_to_true_label = {}\n", " with open(labels_file) as f:\n", " l = 0\n", " for line in f:\n", " if l == 0:\n", " # Skip headers\n", " l += 1\n", " continue\n", " line_arr = line.split(\" \")\n", " sample_to_true_label[line_arr[0].strip()] = int(line_arr[1].strip()) - 1 # subtract one to make labels from 0 to 2\n", " l += 1\n", "\n", " classifier_outputs = []\n", " classifier_samples = []\n", " classifier_dim_sizes = []\n", " output_dim_size = 0\n", " num_samples = 0\n", " sample_to_row = {}\n", "\n", " for i, classifier in enumerate(classifiers):\n", " output, samples = classifier.predict(layers_to_extract[i])\n", " output_dim_size += output.shape[1]\n", " classifier_dim_sizes.append(output.shape[1])\n", " num_samples = len(samples)\n", " classifier_outputs.append(output)\n", " classifier_samples.append(samples)\n", "\n", "\n", " X_train = np.zeros(shape=(num_samples, output_dim_size))\n", " y_train = []\n", "\n", " print(f\"Number of samples: {num_samples}\")\n", " print(f\"Dim shapes: \")\n", " print(classifier_dim_sizes)\n", "\n", " for i, sample in enumerate(classifier_samples[0]):\n", " sample_to_row[sample] = i\n", " y_train.append(sample_to_true_label[sample])\n", "\n", " last_classifier_index = 0\n", " for c, output in enumerate(classifier_outputs):\n", " samples = classifier_samples[c]\n", " print(len(output))\n", " for i, row in enumerate(output):\n", " sample = samples[i]\n", " X_train[sample_to_row[sample], last_classifier_index:last_classifier_index+classifier_dim_sizes[c]] += row\n", " last_classifier_index += classifier_dim_sizes[c]\n", "\n", " return X_train, tf.keras.utils.to_categorical(y_train, num_classes=3)\n", "\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "bad32ca4-157c-4151-8294-c7c42af70079", "id": "yZHvZZ338fFX", "colab": { "base_uri": "https://localhost:8080/", "height": 627 } }, "source": [ "import tensorflow as tf\n", "\n", "# For each classifier, extract the specific desired layer \n", "# (refer to the model summary for the layer names)\n", "\n", "layers_to_extract = [\n", " #\"dense\",\n", " \"concatenate_5\",\n", " \"bidirectional_1\",\n", " \"dense_27\",\n", "]\n", "\n", "prefix = \"final\"\n", "if IS_TINY:\n", " prefix = \"tiny\"\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\" , labels_file=\"Val_labels.txt\")\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "# X_train, y_train = run_classifier(layers_to_extract, audio_folder=f\"train-{prefix}-audio\", frames_folder=f\"train-{prefix}-frames\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "X_train, y_train = run_classifier(layers_to_extract, frames_folder=f\"train-{prefix}-frames\", pose_folder=f\"train-{prefix}-pose\", face_folder=f\"train-{prefix}-fer\" ,labels_file=\"Train_labels.txt\")\n", "X_valid, y_valid = run_classifier(layers_to_extract, frames_folder=f\"val-{prefix}-frames\", pose_folder=f\"val-{prefix}-pose\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\n", "\n", "\n", "print(X_train.shape)\n", "print(y_train.shape)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "AudioClassifier created with audio_folder = train-final-audio , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/openl3-cnn-lstm-tuned-lr.h5\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n", "FramesClassifier created with frames_folder = train-final-frames , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/scene-classifier-resnet-lstm-x3.h5\n", "PoseClassifier created with pose_folder = train-final-pose , is_test = False , model_location = https://storage.googleapis.com/cs231n-emotiw/models/pose-classifier-64lstm-0.01reg.h5\n", "load data\n", "FacesClassifier created with face_folder = train-final-fer , is_test = False , model_location = /content/drive/My Drive/Machine-Learning-Projects/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "Skipping unzipping files as input is a folder\n", "['2', '1', '3']\n", "Found 2661 frames belonging to 2661 videos belonging to 3 classes.\n", "Min frames determined to be 7\n", "Customizing model by returning layer concatenate_5\n" ], "name": "stdout" }, { "output_type": "error", "ename": "ValueError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;31m# X_valid, y_valid = run_classifier(layers_to_extract, audio_folder=f\"val-{prefix}-audio\", frames_folder=f\"val-{prefix}-frames\", face_folder=f\"val-{prefix}-fer\" , labels_file=\"Val_labels.txt\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayers_to_extract\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"train-{prefix}-frames\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpose_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"train-{prefix}-pose\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mface_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"train-{prefix}-fer\"\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mlabels_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Train_labels.txt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 32\u001b[0m \u001b[0mX_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayers_to_extract\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"val-{prefix}-frames\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpose_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"val-{prefix}-pose\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mface_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"val-{prefix}-fer\"\u001b[0m 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\u001b[0;36mpredict\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Customizing model by returning layer {layer}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No such layer: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: No such layer: concatenate_5" ] } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "K4JFDCG88fFa", "colab": {} }, "source": [ "!ls" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "e6cb9dc4-c35d-42a1-9bc4-f5a1756a7dba", "id": "b92HDpI18fFc", "colab": { "base_uri": "https://localhost:8080/", "height": 104 } }, "source": [ "# !mkdir ensemble-scene-pose-audio-face\n", "# np.save(\"ensemble-scene-pose-audio-face/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face.zip ensemble-scene-pose-audio-face\n", "# !cp ensemble-scene-pose-audio-face.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "# !mkdir ensemble-scene-pose-audio-face-v2\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-face-v2/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-face-v2.zip ensemble-scene-pose-audio-face-v2\n", "# !cp ensemble-scene-pose-audio-face-v2.zip ../drive/'My Drive'/cs231n-project/datasets/emotiw\n", "\n", "\n", "# !mkdir ensemble-scene-pose-audio-v1\n", "# np.save(\"ensemble-scene-pose-audio-v1/X_train.npy\", X_train)\n", "# np.save(\"ensemble-scene-pose-audio-v1/y_train.npy\", y_train)\n", "# np.save(\"ensemble-scene-pose-audio-v1/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-scene-pose-audio-v1/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-scene-pose-audio-v1.zip ensemble-scene-pose-audio-v1\n", "# !cp ensemble-scene-pose-audio-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n", "\n", "\n", "# !mkdir ensemble-pose-audio-face-v1\n", "# np.save(\"ensemble-pose-audio-face-v1/X_train.npy\", X_train)\n", "# np.save(\"ensemble-pose-audio-face-v1/y_train.npy\", y_train)\n", "# np.save(\"ensemble-pose-audio-face-v1/X_valid.npy\", X_valid)\n", "# np.save(\"ensemble-pose-audio-face-v1/y_valid.npy\", y_valid)\n", "# !zip -r ensemble-pose-audio-face-v1.zip ensemble-pose-audio-face-v1\n", "# !cp ensemble-pose-audio-face-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n", "\n", "!mkdir ensemble-scene-pose-face-v1\n", "np.save(\"ensemble-scene-pose-face-v1/X_train.npy\", X_train)\n", "np.save(\"ensemble-scene-pose-face-v1/y_train.npy\", y_train)\n", "np.save(\"ensemble-scene-pose-face-v1/X_valid.npy\", X_valid)\n", "np.save(\"ensemble-scene-pose-face-v1/y_valid.npy\", y_valid)\n", "!zip -r ensemble-scene-pose-face-v1.zip ensemble-scene-pose-face-v1\n", "!cp ensemble-scene-pose-face-v1.zip ../drive/'My Drive/Machine-Learning-Projects'/cs231n-project/datasets/emotiw\n", "\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " adding: ensemble-scene-pose-face-v1/ (stored 0%)\n", " adding: ensemble-scene-pose-face-v1/y_valid.npy (deflated 96%)\n", " adding: ensemble-scene-pose-face-v1/X_valid.npy (deflated 51%)\n", " adding: ensemble-scene-pose-face-v1/X_train.npy (deflated 51%)\n", " adding: ensemble-scene-pose-face-v1/y_train.npy (deflated 97%)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1_lEOR5z8fFe" }, "source": [ "## Create FC Model (WITHOUT POSE)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "80f61d60-3c9d-457a-aa2f-9a45a1aa1be7", "id": "LBb2p1BR8fFe", "colab": { "base_uri": "https://localhost:8080/", "height": 347 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " data\t\t\t\t\t train-final-faces.zip\n", " drive\t\t\t\t\t train-final-fer\n", " ensemble-pose-audio-face-v1\t\t train-final-fer.zip\n", " ensemble-pose-audio-face-v1.zip\t train-final-frames\n", " ensemble-scene-pose-audio-v1\t\t train-final-frames.zip\n", " ensemble-scene-pose-audio-v1.zip\t train-final-pose\n", " fc-prediction\t\t\t\t train-final-pose.zip\n", " LICENSE\t\t\t\t Train_labels.txt\n", " models\t\t\t\t\t val-final-audio\n", " notebooks\t\t\t\t val-final-audio.zip\n", " README.md\t\t\t\t val-final-faces\n", " reports\t\t\t\t val-final-faces.zip\n", " requirements-predictions.txt\t\t val-final-fer\n", " requirements.txt\t\t\t val-final-fer.zip\n", "'Screen Shot 2020-05-26 at 8.53.32 PM.png' val-final-frames\n", " src\t\t\t\t\t val-final-frames.zip\n", " train-final-audio\t\t\t val-final-pose\n", " train-final-audio.zip\t\t\t val-final-pose.zip\n", " train-final-faces\t\t\t Val_labels.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "b880418b-2a1a-43fb-9a6c-b39b483d99d3", "id": "ivsbk4Sr8fFi", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "#!rm -rf ensemble-scene-pose-audio\n", "#!cp ../drive/'My Drive'/cs231n-project/datasets/emotiw/ensemble-scene-pose-audio.zip .\n", "#!unzip ensemble-scene-pose-audio.zip\n", "\n", "X_train = np.load(\"ensemble-scene-pose-face-v1/X_train.npy\")\n", "y_train = np.load(\"ensemble-scene-pose-face-v1/y_train.npy\")\n", "X_valid = np.load(\"ensemble-scene-pose-face-v1/X_valid.npy\")\n", "y_valid = np.load(\"ensemble-scene-pose-face-v1/y_valid.npy\")\n", "X_train.shape" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(2661, 70)" ] }, "metadata": { "tags": [] }, "execution_count": 97 } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "67068f2d-0a6c-403f-9586-03a22c73dde5", "id": "vK9mNm5t8fFk", "colab": { "base_uri": "https://localhost:8080/", "height": 297 } }, "source": [ "!pip install keras-tuner" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: keras-tuner in /usr/local/lib/python3.6/dist-packages (1.0.1)\n", "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.16.0)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (2.23.0)\n", "Requirement already satisfied: colorama in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.4.3)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.22.2.post1)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (4.41.1)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.4.1)\n", "Requirement already satisfied: terminaltables in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (3.1.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.18.4)\n", "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.8.7)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (1.24.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2020.4.5.1)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2.9)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (3.0.4)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->keras-tuner) (0.15.1)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "TovGf7Ce8fFm", "colab": {} }, "source": [ "#\n", "# CONFIGURATION\n", "#\n", "# Define any constants for the model here\n", "#\n", "\n", "MODEL_NAME = \"ensemble-scene-pose-face-v1\"\n", "\n", "from pathlib import Path\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "\n", "def build_model(hp):\n", " Path(f\"drive/My Drive/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", " checkpoint_path = \"drive/My Drive/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", " cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " period=1\n", " )\n", "\n", " def create_model(inputs):\n", " x = tf.keras.layers.Dense(hp.Int('units', min_value=8, max_value=128, step=8), activation='relu', kernel_regularizer=tf.keras.regularizers.l2())(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(\n", " hp.Choice('learning_rate',\n", " values=[1e-2, 1e-3, 1e-4])),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", " inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", " model = create_model(inputs)\n", " # model.summary()\n", " return model\n", "\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "4391fec2-c374-4bb1-e767-88f5fdfa690c", "id": "dtSNTFjb8fFo", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "from kerastuner.tuners import RandomSearch\n", "tuner = RandomSearch(\n", " build_model,\n", " objective='val_accuracy',\n", " max_trials=5,\n", " executions_per_trial=3,\n", " directory='fc-prediction-ablate-audio',\n", " project_name='fc-prediction-ablate-audio'\n", ")\n", "tuner.search_space_summary()\n", "\n", "tuner.search(X_train, y_train,\n", " epochs=30,\n", " validation_data=(X_valid, y_valid),\n", " verbose=0)\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. 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Search space summary

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units (Int)

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learning_rate (Choice)

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Trial complete

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Trial summary

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Hyperparameters:

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Results summary

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

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Trial summary

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Hyperparameters:

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-learning_rate: 0.0001" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/html": [ " |-units: 72" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "8df3e910-4df1-4906-e055-435b8f71558b", "id": "o9ec4iLB8fFt", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "MODEL_NAME = \"ensemble-scene-pose-face-v1\"\n", "Path(f\"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", "checkpoint_path = \"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " monitor='val_accuracy',\n", " period=1\n", ")\n", "\n", "def create_model(inputs):\n", " x = tf.keras.layers.Dense(96, activation='relu')(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(0.01),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", "inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", "model = create_model(inputs)\n", "\n", "import pickle\n", "history = model.fit(\n", " x=X_train,\n", " y=y_train,\n", " epochs=30,\n", " callbacks=[cp_callback],\n", " validation_data=(X_valid, y_valid)\n", ")\n", "\n", "acc = history.history['accuracy']\n", "val_acc = history.history['val_accuracy']\n", "\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "plt.tick_params(labelsize=12)\n", "sn.set(font_scale=1.0)\n", "plt.figure(figsize=(8, 8))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(acc, label='Training Accuracy')\n", "plt.plot(val_acc, label='Validation Accuracy')\n", "plt.legend(loc='lower right')\n", "plt.ylabel('Accuracy')\n", "plt.title('Training and Validation Accuracy' , fontsize=12)\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(loss, label='Training Loss')\n", "plt.plot(val_loss, label='Validation Loss')\n", "plt.legend(loc='upper right')\n", "plt.ylabel('Cross Entropy')\n", "plt.title('Training and Validation Loss' , fontsize=12)\n", "plt.xlabel('epoch')\n", "\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n", "Epoch 1/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.5323 - accuracy: 0.8063\n", "Epoch 00001: val_accuracy improved from -inf to 0.61227, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-face-v1/cp-0001.h5\n", "84/84 [==============================] - 0s 5ms/step - loss: 0.5319 - accuracy: 0.8061 - val_loss: 0.8984 - val_accuracy: 0.6123\n", "Epoch 2/30\n", "76/84 [==========================>...] - ETA: 0s - loss: 0.4688 - accuracy: 0.8302\n", "Epoch 00002: val_accuracy did not improve from 0.61227\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4705 - accuracy: 0.8275 - val_loss: 0.9973 - val_accuracy: 0.5992\n", "Epoch 3/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4611 - accuracy: 0.8369\n", "Epoch 00003: val_accuracy did not improve from 0.61227\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4582 - accuracy: 0.8362 - val_loss: 1.0390 - val_accuracy: 0.6070\n", "Epoch 4/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4578 - accuracy: 0.8218\n", "Epoch 00004: val_accuracy improved from 0.61227 to 0.61358, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-face-v1/cp-0004.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4659 - accuracy: 0.8230 - val_loss: 0.9332 - val_accuracy: 0.6136\n", "Epoch 5/30\n", "77/84 [==========================>...] - ETA: 0s - loss: 0.4578 - accuracy: 0.8324\n", "Epoch 00005: val_accuracy did not improve from 0.61358\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4565 - accuracy: 0.8328 - val_loss: 1.0168 - val_accuracy: 0.6110\n", "Epoch 6/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.4339 - accuracy: 0.8324\n", "Epoch 00006: val_accuracy improved from 0.61358 to 0.61749, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-face-v1/cp-0006.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4339 - accuracy: 0.8324 - val_loss: 1.0037 - val_accuracy: 0.6175\n", "Epoch 7/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4481 - accuracy: 0.8323\n", "Epoch 00007: val_accuracy did not improve from 0.61749\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4457 - accuracy: 0.8339 - val_loss: 1.0225 - val_accuracy: 0.6084\n", "Epoch 8/30\n", "76/84 [==========================>...] - ETA: 0s - loss: 0.4374 - accuracy: 0.8438\n", "Epoch 00008: val_accuracy did not improve from 0.61749\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4311 - accuracy: 0.8459 - val_loss: 0.9645 - val_accuracy: 0.6070\n", "Epoch 9/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4163 - accuracy: 0.8477\n", "Epoch 00009: val_accuracy improved from 0.61749 to 0.62924, saving model to drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-face-v1/cp-0009.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4195 - accuracy: 0.8459 - val_loss: 0.9754 - val_accuracy: 0.6292\n", "Epoch 10/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4146 - accuracy: 0.8490\n", "Epoch 00010: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4249 - accuracy: 0.8433 - val_loss: 1.0538 - val_accuracy: 0.6044\n", "Epoch 11/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4155 - accuracy: 0.8418\n", "Epoch 00011: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4155 - accuracy: 0.8418 - val_loss: 1.0314 - val_accuracy: 0.6084\n", "Epoch 12/30\n", "77/84 [==========================>...] - ETA: 0s - loss: 0.4275 - accuracy: 0.8442\n", "Epoch 00012: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4242 - accuracy: 0.8444 - val_loss: 0.9975 - val_accuracy: 0.6057\n", "Epoch 13/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4182 - accuracy: 0.8464\n", "Epoch 00013: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4168 - accuracy: 0.8470 - val_loss: 1.0079 - val_accuracy: 0.6136\n", "Epoch 14/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.4217 - accuracy: 0.8430\n", "Epoch 00014: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4197 - accuracy: 0.8440 - val_loss: 1.0492 - val_accuracy: 0.6097\n", "Epoch 15/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4167 - accuracy: 0.8486\n", "Epoch 00015: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4127 - accuracy: 0.8501 - val_loss: 1.0033 - val_accuracy: 0.6097\n", "Epoch 16/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4005 - accuracy: 0.8521\n", "Epoch 00016: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4005 - accuracy: 0.8519 - val_loss: 1.0693 - val_accuracy: 0.6123\n", "Epoch 17/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4142 - accuracy: 0.8479\n", "Epoch 00017: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4139 - accuracy: 0.8482 - val_loss: 1.1062 - val_accuracy: 0.6057\n", "Epoch 18/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.3964 - accuracy: 0.8561\n", "Epoch 00018: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3990 - accuracy: 0.8542 - val_loss: 1.1841 - val_accuracy: 0.5953\n", "Epoch 19/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.3994 - accuracy: 0.8475\n", "Epoch 00019: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4017 - accuracy: 0.8455 - val_loss: 1.1132 - val_accuracy: 0.6123\n", "Epoch 20/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3866 - accuracy: 0.8543\n", "Epoch 00020: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3918 - accuracy: 0.8531 - val_loss: 1.1048 - val_accuracy: 0.5940\n", "Epoch 21/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.4050 - accuracy: 0.8505\n", "Epoch 00021: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4000 - accuracy: 0.8542 - val_loss: 1.1702 - val_accuracy: 0.6005\n", "Epoch 22/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.3970 - accuracy: 0.8575\n", "Epoch 00022: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3963 - accuracy: 0.8579 - val_loss: 1.0725 - val_accuracy: 0.5940\n", "Epoch 23/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.3937 - accuracy: 0.8628\n", "Epoch 00023: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3942 - accuracy: 0.8553 - val_loss: 1.1577 - val_accuracy: 0.5888\n", "Epoch 24/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.3927 - accuracy: 0.8621\n", "Epoch 00024: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3927 - accuracy: 0.8621 - val_loss: 1.0267 - val_accuracy: 0.6162\n", "Epoch 25/30\n", "77/84 [==========================>...] - ETA: 0s - loss: 0.3859 - accuracy: 0.8596\n", "Epoch 00025: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3855 - accuracy: 0.8610 - val_loss: 1.0868 - val_accuracy: 0.6149\n", "Epoch 26/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3788 - accuracy: 0.8649\n", "Epoch 00026: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3866 - accuracy: 0.8598 - val_loss: 1.0459 - val_accuracy: 0.6175\n", "Epoch 27/30\n", "63/84 [=====================>........] - ETA: 0s - loss: 0.3923 - accuracy: 0.8616\n", "Epoch 00027: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3851 - accuracy: 0.8636 - val_loss: 1.1440 - val_accuracy: 0.6188\n", "Epoch 28/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.3836 - accuracy: 0.8629\n", "Epoch 00028: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3817 - accuracy: 0.8636 - val_loss: 1.1500 - val_accuracy: 0.6175\n", "Epoch 29/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.3818 - accuracy: 0.8689\n", "Epoch 00029: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3814 - accuracy: 0.8688 - val_loss: 1.0687 - val_accuracy: 0.6018\n", "Epoch 30/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.3792 - accuracy: 0.8641\n", "Epoch 00030: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3798 - accuracy: 0.8636 - val_loss: 1.1934 - val_accuracy: 0.6005\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "TpEOqmCN8fFv" }, "source": [ "#### Load Best Model to Perform Prediction" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "22KEapo78fFv", "colab": {} }, "source": [ "model = tf.keras.models.load_model(\"drive/My Drive/Machine-Learning-Projects/cs231n-project/models/ensemble-scene-pose-face-v1/cp-0009.h5\")\n", "\n", "y_pred = model.predict(X_valid)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "8d8bbfba-a785-476d-acc2-9c43d8b65dba", "id": "8hG-qt1d8fFx", "colab": { "base_uri": "https://localhost:8080/", "height": 624 } }, "source": [ "y_valid_labels = np.argmax(y_valid, axis=1) + 1\n", "y_valid_labels" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 1, 1, 2, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 3, 3, 1, 1, 2, 1,\n", " 2, 1, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3,\n", " 3, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 3, 3,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 1, 3, 3, 2, 3, 3, 3, 3,\n", " 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 1,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1,\n", " 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2,\n", " 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n", " 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1,\n", " 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,\n", " 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1,\n", " 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n", " 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 3,\n", " 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3,\n", " 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3,\n", " 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 1, 3, 2, 3, 3, 2, 2, 2, 2,\n", " 2, 1, 2, 1, 2, 1, 3, 3, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1,\n", " 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2,\n", " 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n", " 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2,\n", " 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 3, 3, 3, 3,\n", " 1, 3, 3, 3, 1, 3, 1, 3, 2, 3, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1,\n", " 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 2, 3, 3])" ] }, "metadata": { "tags": [] }, "execution_count": 103 } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "83528575-c080-4c96-e1e4-a15bd0e59e39", "id": "75IhDGuW8fFy", "colab": { "base_uri": "https://localhost:8080/", "height": 381 } }, "source": [ "y_pred_labels = np.argmax(y_pred, axis=1) + 1\n", "print(y_pred_labels)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "[3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 1 2 2 2 3 3 2 1 3 2 3 2 2 1 1 3 1 2 2\n", " 2 3 1 1 1 1 1 1 1 3 1 1 1 1 1 2 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2\n", " 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 1 1 1 1 1 1 3 1 1 1 3 1 1 1 1 3 2 2 2 2\n", " 2 2 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 3 2 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 2 3 3 3 3 2 2 2 3 3 3 2 3 1\n", " 3 3 3 3 1 3 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 3 2 1 1 2 2 2 2 2 3 2\n", " 3 2 2 3 3 3 3 3 2 3 3 1 2 3 3 3 3 3 2 2 2 2 1 1 3 1 3 3 3 3 2 3 3 1 1 2 1\n", " 1 1 2 1 3 1 1 3 1 2 2 3 2 1 2 2 1 1 1 2 1 1 2 1 1 2 1 2 1 2 2 2 1 2 2 2 2\n", " 1 2 1 1 3 1 1 1 2 2 2 1 2 2 1 1 2 2 2 2 2 2 1 2 2 1 2 1 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 3 3 3 3 3 3 3 3 3 3 3\n", " 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 1 1 1 1 1 1 3 2 3 2 3 3\n", " 2 3 3 3 2 2 2 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3\n", " 3 3 3 2 2 2 3 3 3 3 3 3 3 3 2 2 3 2 3 3 3 3 3 3 3 2 2 3 3 2 2 3 3 3 3 3 3\n", " 3 3 3 3 3 3 1 1 2 2 2 3 1 1 1 1 3 3 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 1 1 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2\n", " 2 1 1 2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 2 1 3 1 2 1 2 2 2 2 1 2 1 1 1 1 1 2 3 1 1 2 1 3 2 1 1 3 1 1 3 3\n", " 1 1 3 3 2 1 3 1 1 1 1 1 3 1 3 3 3 3 1 3 1 3 3 1 1 3 3 1 1 1 3 1 3 1 3 3 3\n", " 3 1 3 3 1 1 1 1 3 1 1 1 3 1 3 3 1 1 3 3 3 3 3 1 1 1]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "8bd382b6-f024-48f7-cd23-a6bd57b90141", "id": "JLl65c-_8fF1", "colab": { "base_uri": "https://localhost:8080/", "height": 597 } }, "source": [ "from sklearn import metrics \n", "import pandas as pd\n", "import seaborn as sn\n", "cm=metrics.confusion_matrix(y_valid_labels,y_pred_labels)\n", "import matplotlib.pyplot as plt\n", "\n", "classes=['Pos' , 'Neu' , 'Neg'] \n", "y_pred_final , y_true_final = y_pred_labels - 1 , y_valid_labels - 1\n", "con_mat = tensorflow.math.confusion_matrix(labels=y_true_final, predictions=y_pred_final).numpy()\n", "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n", "con_mat_df = pd.DataFrame(con_mat_norm,\n", " index = classes, \n", " columns = classes)\n", "\n", "figure = plt.figure(figsize=(11, 9))\n", "plt.title(\"FC Voting Confusion Matrix with Scene, Pose, and Audio\")\n", "\n", "\n", "plt.tick_params(labelsize=24)\n", "plt.title(\"Scene-Pose-FER Model Confusion Matrix\", fontsize=24)\n", "sn.set(font_scale=3.0)\n", "sn.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues , vmin=0 , vmax=0.8)\n", "\n", "\n", "accuracy = (y_pred_labels == y_valid_labels).mean()\n", "print(f\"Accuracy: {accuracy}\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 0.6292428198433421\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "5a80812e-09b4-46fb-c1b5-cc71fc55c407", "id": "i_aO13gZ8fF3", "colab": { "base_uri": "https://localhost:8080/", "height": 191 } }, "source": [ "from sklearn.metrics import classification_report\n", "target_names = [\"Positive\" , \"Neutral\" , \"Negative\"]\n", "print(classification_report(y_valid_labels, y_pred_labels, target_names=target_names, digits=3))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Positive 0.664 0.556 0.605 302\n", " Neutral 0.607 0.618 0.612 280\n", " Negative 0.618 0.766 0.684 184\n", "\n", " accuracy 0.629 766\n", " macro avg 0.630 0.647 0.634 766\n", "weighted avg 0.632 0.629 0.627 766\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "VrTlbUYRrlgh", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ipuuZaA39B4X", "colab_type": "text" }, "source": [ "## FC Classifier ALL\n", "\n", "> Indented block\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "BUgymLxh9JS5", "colab_type": "code", "outputId": "16a18540-98aa-4a7d-dd6f-e0e58f8a3f2c", "colab": { "base_uri": "https://localhost:8080/", "height": 357 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " data\t\t\t\t\t train-final-faces\n", " drive\t\t\t\t\t train-final-faces.zip\n", " ensemble-scene-pose-audio\t\t train-final-fer\n", " ensemble-scene-pose-audio-face\t\t train-final-fer.zip\n", " ensemble-scene-pose-audio-face.zip\t train-final-frames\n", " ensemble-scene-pose-audio.zip\t\t train-final-frames.zip\n", " ensemble-scenex2-pose-audio\t\t train-final-pose\n", " ensemble-scenex2-pose-audio.zip\t train-final-pose.zip\n", " fc-prediction\t\t\t\t Train_labels.txt\n", " LICENSE\t\t\t\t val-final-audio\n", " models\t\t\t\t\t val-final-audio.zip\n", " notebooks\t\t\t\t val-final-faces\n", " README.md\t\t\t\t val-final-faces.zip\n", " reports\t\t\t\t val-final-fer\n", " requirements-predictions.txt\t\t val-final-fer.zip\n", " requirements.txt\t\t\t val-final-frames\n", "'Screen Shot 2020-05-26 at 8.53.32 PM.png' val-final-frames.zip\n", " src\t\t\t\t\t val-final-pose\n", " train-final-audio\t\t\t val-final-pose.zip\n", " train-final-audio.zip\t\t\t Val_labels.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "QQCa6z609Do3", "colab_type": "code", "outputId": "019b0301-1ae1-43f9-dcdc-e15c5dc24787", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "# !rm -rf ensemble-scene-pose-audio-face-v2\n", "# !cp ../drive/'My Drive'/cs231n-project/datasets/emotiw/ensemble-scene-pose-audio-face-v2.zip .\n", "# !unzip ensemble-scene-pose-audio-face-v2.zip\n", "\n", "X_train = np.load(\"ensemble-scene-pose-audio-face-v2/X_train.npy\")\n", "y_train = np.load(\"ensemble-scene-pose-audio-face-v2/y_train.npy\")\n", "X_valid = np.load(\"ensemble-scene-pose-audio-face-v2/X_valid.npy\")\n", "y_valid = np.load(\"ensemble-scene-pose-audio-face-v2/y_valid.npy\")\n", "X_train.shape" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(2661, 198)" ] }, "metadata": { "tags": [] }, "execution_count": 50 } ] }, { "cell_type": "code", "metadata": { "id": "v49oeUZZ9Brl", "colab_type": "code", "outputId": "44a55ab0-b9d8-4cda-a231-8cef695e7e3f", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "MODEL_NAME = \"ensemble-fc-v3-face-v2\"\n", "Path(f\"drive/My Drive/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", "checkpoint_path = \"drive/My Drive/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " monitor='val_accuracy',\n", " period=1\n", ")\n", "\n", "def create_model(inputs):\n", " x = tf.keras.layers.Dense(48, activation='relu')(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(0.005),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", "inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", "model = create_model(inputs)\n", "\n", "import pickle\n", "history = model.fit(\n", " x=X_train,\n", " y=y_train,\n", " epochs=30,\n", " callbacks=[cp_callback],\n", " validation_data=(X_valid, y_valid)\n", ")\n", "\n", "acc = history.history['accuracy']\n", "val_acc = history.history['val_accuracy']\n", "\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(acc, label='Training Accuracy')\n", "plt.plot(val_acc, label='Validation Accuracy')\n", "plt.legend(loc='lower right')\n", "plt.ylabel('Accuracy')\n", "plt.title('Training and Validation Accuracy')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(loss, label='Training Loss')\n", "plt.plot(val_loss, label='Validation Loss')\n", "plt.legend(loc='upper right')\n", "plt.ylabel('Cross Entropy')\n", "plt.title('Training and Validation Loss')\n", "plt.xlabel('epoch')\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n", "Epoch 1/30\n", "70/84 [========================>.....] - ETA: 0s - loss: 0.5175 - accuracy: 0.8027\n", "Epoch 00001: val_accuracy improved from -inf to 0.59399, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-face-v2/cp-0001.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.5169 - accuracy: 0.8031 - val_loss: 1.0100 - val_accuracy: 0.5940\n", "Epoch 2/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4628 - accuracy: 0.8210\n", "Epoch 00002: val_accuracy improved from 0.59399 to 0.61227, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-face-v2/cp-0002.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4605 - accuracy: 0.8226 - val_loss: 1.0223 - val_accuracy: 0.6123\n", "Epoch 3/30\n", "70/84 [========================>.....] - ETA: 0s - loss: 0.4356 - accuracy: 0.8313\n", "Epoch 00003: val_accuracy improved from 0.61227 to 0.62924, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-face-v2/cp-0003.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4409 - accuracy: 0.8298 - val_loss: 0.9756 - val_accuracy: 0.6292\n", "Epoch 4/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.4411 - accuracy: 0.8347\n", "Epoch 00004: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4407 - accuracy: 0.8309 - val_loss: 0.9825 - val_accuracy: 0.6149\n", "Epoch 5/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.4197 - accuracy: 0.8437\n", "Epoch 00005: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4197 - accuracy: 0.8437 - val_loss: 0.9533 - val_accuracy: 0.6084\n", "Epoch 6/30\n", "66/84 [======================>.......] - ETA: 0s - loss: 0.4079 - accuracy: 0.8438\n", "Epoch 00006: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4176 - accuracy: 0.8429 - val_loss: 0.9398 - val_accuracy: 0.6175\n", "Epoch 7/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3994 - accuracy: 0.8462\n", "Epoch 00007: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4074 - accuracy: 0.8422 - val_loss: 0.9728 - val_accuracy: 0.6031\n", "Epoch 8/30\n", "68/84 [=======================>......] - ETA: 0s - loss: 0.4182 - accuracy: 0.8336\n", "Epoch 00008: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4195 - accuracy: 0.8320 - val_loss: 1.0285 - val_accuracy: 0.6018\n", "Epoch 9/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4008 - accuracy: 0.8453\n", "Epoch 00009: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3990 - accuracy: 0.8467 - val_loss: 0.9622 - val_accuracy: 0.6136\n", "Epoch 10/30\n", "70/84 [========================>.....] - ETA: 0s - loss: 0.3960 - accuracy: 0.8473\n", "Epoch 00010: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3950 - accuracy: 0.8478 - val_loss: 0.9625 - val_accuracy: 0.6227\n", "Epoch 11/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.3897 - accuracy: 0.8466\n", "Epoch 00011: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3890 - accuracy: 0.8459 - val_loss: 1.0818 - val_accuracy: 0.6084\n", "Epoch 12/30\n", "71/84 [========================>.....] - ETA: 0s - loss: 0.3920 - accuracy: 0.8508\n", "Epoch 00012: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3896 - accuracy: 0.8497 - val_loss: 1.0028 - val_accuracy: 0.6201\n", "Epoch 13/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.3848 - accuracy: 0.8555\n", "Epoch 00013: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3852 - accuracy: 0.8534 - val_loss: 1.0359 - val_accuracy: 0.6149\n", "Epoch 14/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.3668 - accuracy: 0.8619\n", "Epoch 00014: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3749 - accuracy: 0.8568 - val_loss: 1.0953 - val_accuracy: 0.6044\n", "Epoch 15/30\n", "73/84 [=========================>....] - ETA: 0s - loss: 0.3730 - accuracy: 0.8519\n", "Epoch 00015: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3727 - accuracy: 0.8493 - val_loss: 1.0438 - val_accuracy: 0.6136\n", "Epoch 16/30\n", "71/84 [========================>.....] - ETA: 0s - loss: 0.3607 - accuracy: 0.8680\n", "Epoch 00016: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3622 - accuracy: 0.8647 - val_loss: 1.0299 - val_accuracy: 0.6175\n", "Epoch 17/30\n", "70/84 [========================>.....] - ETA: 0s - loss: 0.3573 - accuracy: 0.8612\n", "Epoch 00017: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3620 - accuracy: 0.8583 - val_loss: 1.1673 - val_accuracy: 0.6149\n", "Epoch 18/30\n", "63/84 [=====================>........] - ETA: 0s - loss: 0.3519 - accuracy: 0.8641\n", "Epoch 00018: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3572 - accuracy: 0.8587 - val_loss: 1.0798 - val_accuracy: 0.6214\n", "Epoch 19/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3533 - accuracy: 0.8572\n", "Epoch 00019: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3576 - accuracy: 0.8549 - val_loss: 1.1597 - val_accuracy: 0.6097\n", "Epoch 20/30\n", "70/84 [========================>.....] - ETA: 0s - loss: 0.3558 - accuracy: 0.8625\n", "Epoch 00020: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3457 - accuracy: 0.8681 - val_loss: 1.1352 - val_accuracy: 0.6227\n", "Epoch 21/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.3461 - accuracy: 0.8678\n", "Epoch 00021: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3531 - accuracy: 0.8662 - val_loss: 1.1346 - val_accuracy: 0.6123\n", "Epoch 22/30\n", "63/84 [=====================>........] - ETA: 0s - loss: 0.3312 - accuracy: 0.8775\n", "Epoch 00022: val_accuracy improved from 0.62924 to 0.63577, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-face-v2/cp-0022.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3404 - accuracy: 0.8715 - val_loss: 1.1582 - val_accuracy: 0.6358\n", "Epoch 23/30\n", "68/84 [=======================>......] - ETA: 0s - loss: 0.3431 - accuracy: 0.8727\n", "Epoch 00023: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3384 - accuracy: 0.8752 - val_loss: 1.2323 - val_accuracy: 0.6044\n", "Epoch 24/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3364 - accuracy: 0.8726\n", "Epoch 00024: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3348 - accuracy: 0.8734 - val_loss: 1.3090 - val_accuracy: 0.6070\n", "Epoch 25/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.3288 - accuracy: 0.8736\n", "Epoch 00025: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3302 - accuracy: 0.8734 - val_loss: 1.1478 - val_accuracy: 0.6201\n", "Epoch 26/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3286 - accuracy: 0.8750\n", "Epoch 00026: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3241 - accuracy: 0.8734 - val_loss: 1.2284 - val_accuracy: 0.6084\n", "Epoch 27/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.3053 - accuracy: 0.8804\n", "Epoch 00027: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3146 - accuracy: 0.8767 - val_loss: 1.4133 - val_accuracy: 0.5953\n", "Epoch 28/30\n", "62/84 [=====================>........] - ETA: 0s - loss: 0.3212 - accuracy: 0.8816\n", "Epoch 00028: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3216 - accuracy: 0.8797 - val_loss: 1.2789 - val_accuracy: 0.6084\n", "Epoch 29/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.3199 - accuracy: 0.8804\n", "Epoch 00029: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3172 - accuracy: 0.8805 - val_loss: 1.2166 - val_accuracy: 0.6201\n", "Epoch 30/30\n", "69/84 [=======================>......] - ETA: 0s - loss: 0.3111 - accuracy: 0.8850\n", "Epoch 00030: val_accuracy did not improve from 0.63577\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3074 - accuracy: 0.8835 - val_loss: 1.3568 - val_accuracy: 0.6057\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "4lgrTzBC_VDa", "colab_type": "code", "colab": {} }, "source": [ "model = tf.keras.models.load_model(\"drive/My Drive/cs231n-project/models/ensemble-fc-v3-face-v2/cp-0022.h5\")\n", "\n", "y_pred = model.predict(X_valid)\n", "y_valid_labels = np.argmax(y_valid, axis=1) + 1\n", "y_pred_labels = np.argmax(y_pred, axis=1) + 1\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1vK14PuL8_gB", "colab_type": "code", "outputId": "a29614d1-fd50-4e10-b82e-9487d631bda0", "colab": { "base_uri": "https://localhost:8080/", "height": 740 } }, "source": [ "from sklearn import metrics \n", "import pandas as pd\n", "import seaborn as sn\n", "cm=metrics.confusion_matrix(y_valid_labels,y_pred_labels)\n", "import matplotlib.pyplot as plt\n", "import tensorflow\n", "\n", "classes=['Pos' , 'Neu' , 'Neg'] \n", "y_pred_final , y_true_final = y_pred_labels - 1 , y_valid_labels - 1\n", "con_mat = tensorflow.math.confusion_matrix(labels=y_true_final, predictions=y_pred_final).numpy()\n", "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n", "con_mat_df = pd.DataFrame(con_mat_norm,\n", " index = classes, \n", " columns = classes)\n", "\n", "figure = plt.figure(figsize=(11, 9))\n", "plt.title(\"FC Voting Confusion Matrix with Scene, Pose, Face, and Audio\")\n", "sn.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues)\n", "\n", "\n", "accuracy = (y_pred_labels == y_valid_labels).mean()\n", "print(f\"Accuracy: {accuracy}\")\n", "from sklearn.metrics import classification_report\n", "target_names = [\"Positive\" , \"Neutral\" , \"Negative\"]\n", "print(classification_report(y_valid_labels, y_pred_labels, target_names=target_names, digits=3))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 0.6357702349869452\n", " precision recall f1-score support\n", "\n", " Positive 0.709 0.500 0.586 302\n", " Neutral 0.598 0.654 0.625 280\n", " Negative 0.619 0.832 0.710 184\n", "\n", " accuracy 0.636 766\n", " macro avg 0.642 0.662 0.640 766\n", "weighted avg 0.647 0.636 0.630 766\n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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yHKyI6ARcDuwHTAPGRsSNKaWJFcXOAq5PKV0ZEVsCY4ANW6rXBEuSJHVkOwGTU0pTUkrzgdHAoU3KJGDt/Od1gBmtVWqCJUmSytPGc7AiYiQwsmLVqJTSqIrlgcDUiuVpwM5NqjkPuC0ivgJ0B/Zt7bh2sCRJUs3KO1OjWi3YsqOAa1JKP46IXYHfRsTWKaWG5nawgyVJkspT/n2wpgODK5YH5esqHQcMB0gp3R8R3YBewOzmKi39rCRJkko0FhgaEUMioiswArixSZkXgY8ARMQWQDfg5ZYqNcGSJEnlKfk+WCmlhRFxEnAr0Am4OqX0ZEScD4xLKd0IfB24KiK+Rjbh/diUUmqpXjtYkiSpQ0spjSG79ULlunMqfp4IfHh56rSDJUmSylP+HKw2UZtnJUmSVCITLEmSVB4TLEmSJBVhgiVJkspTV+63CNuKCZYkSVKVmWBJkqTyOAdLkiRJRZhgSZKk8pR8J/e2YgdLkiSVxyFCSZIkFWGCJUmSylOjQ4QmWJIkSVVmgiVJksrjHCxJkiQVYYIlSZLK4xwsSZIkFWGCJUmSyuMcLEmSJBVhgiVJksrjHCxJkiQVYYIlSZLK4xwsSZIkFWGCJUmSyuMcLEmSJBVhgiVJksrjHCxJkiQVYYIlSZLKY4IlSZKkIkywJElSeWr0W4R2sCRJUnkcIpQkSVIRJliSJKk8NTpEaIIlSZJUZSZYkiSpPM7BkiRJUhEmWJIkqTzOwZIkSVIRJliSJKk0YYIlSZKkIkywJElSaUywJEmSVIgJliRJKk9tBlgmWJIkSdVmgiVJkkrjHCxJkiQVYoIlSZJKY4IlSZKkQkywJElSaUywJEmSVIgJliRJKo0JliRJkgoxwWpnth2wFsfuNIi6CO58dg7/mDCr0fa9Nl6PY3YcwKtvLwDg1qde4c5n55TRVNWg/z3+ELf/9gpSQwPbDjuQ3Q45qtH2R+64iYdv/wdR14mu3bpx4HGn0nvQBsx9eSajvvEF1us/GICBm2zBgcd9tYxTUA156P7/cPklF9HQ0MBBhxzOUZ89rtH2Jx4dx+WXXMyU/z3LWRdcxF777L942367bceQjYcC0KdvPy780WWrtO1aDrUZYNnBak8i4Au7DOa7t01mztsL+P7BmzFu6utMf/3dRuX++/xcfvPgtJJaqVrV0FDPrddcxlFnXsTa6/XmN2efyNDtd6P3oA0Wl9lqt33Yft+PAfDMw//lX7+/khGn/wCAdfsO4Ivf/2UpbVftqa+v52c/+h4X/2wUvfv05cufP4pd9xjGhkM2XlymT9/+fPPsC7nhD9cstX/X1VZj1G9vWIUt1opyiFBtbpNeazBr3nvMfnM+9Q2J/z73Gh8avE7ZzVIHMeN/T9Oj7wB69BlAp85d2HKXYTz78H2Nyqy2RvfFPy94711q9qOnSvfUxAkMHLQ+AwYOokuXLuy933D+e8+/G5XpN2AgGw/dlAjfytT+FEqwIqI78E5KqSEiNgU2B/6ZUlrQpq3rYNZboytz3pq/eHnO2/PZpHf3pcrtvP66bNFnTV6a9y7XjZ3OnLf9NWjlvfHqK6zds8/i5bXW682M/z21VLlxt/2Dh/75Z+oXLuTob/9w8frXX57Jr7/1/+i6enf2+tTnWX/zbVZJu1WbXnl5Fr379F283LtPXyY9Ob7w/vPnz+eEY0fQqVMnRnz2OHbfa5+2aKaqoD0kWBExHPgp0An4VUrpB022XwLsnS+uAfRJKa3bUp1FhwjvAfaIiB7AbcBY4Ejg6GYaOhIYCbDDsd9m42GfKHgYtebhaa9z33OvsbAhse+mPfny7htwwW2Ty26WOpAd9z+UHfc/lCfv+xf3/f33fOz401lz3fU48ae/Z4211uGl557hzz85l5EX/apR4iWtSn/42y307tOXGdOncdqJX2SjjYcyYNDgspuldigiOgGXA/sB04CxEXFjSmniojIppa9VlP8K8MHW6i2aq0ZK6W3gcOCKlNKngK2aK5xSGpVS2jGltKOdq+JefXs+Pbt3Xbzcc42uvPZW43TqzffqWdiQAPjXs3PYqOcaq7SNql1rrdeLeXNmL15+49WXWatHz2bLb7nr3jwzLhtC7NylK2uslQ1n9x+yKT369ufVmc4T1Irr1bsvL89e8iWfl2fPolfvPi3s0dii9GvAwEFsu/2OPPvMpKq3UdUREW36KGAnYHJKaUpKaT4wGji0hfJHAX9srdLCHayI2JUssbo5X9ep4L4q6H+vvE2/tVej95pd6VQX7DakB+Omvd6ozLqrLwkddxy8zlIT4KUVNWCjzXht5nTmzn6J+oULmPjAXQzdYbdGZSo7TZMfe5Ae/QYB8Na8uTQ01APw2uwZvDpzOuv26b/qGq+as/kWWzF96gu8NGMaCxYs4N+338JuewwrtO8b8+Yxf3423eL1ua/x5BOPsUHF5Hh1LBExMiLGVTxGNikyEJhasTwtX7esujYAhgB3tnbcokOEXwXOBP6WUnoyIjYC/t3KPlpODQmufnAa39p3Y+rqgruencO0ue/yqe36MWXO2zw8dR4HbtGbHQavQ0MDvPneQq74zwtlN1s1oq5TJ/Y/9iuMvugMGhoa2Hav4fQetCF3//ka+g/ZlE132I1xt/2D5yc8Ql2nznTrviYfO/6bAEx96gnu+fO11HXqTNQFB37hq6y+5toln5Hezzp17sxXTvsWp59yAg0N9Rz40Y+z4Uab8JtRl7PZ5luy255789TECZx7+ld584153P+fu7n2qiu5+o9/48Xnp3DJRecTUUdKDYz47BcafftQ7Utbz8FKKY0CRlWpuhHAn1NK9a0VjJRS4VojYk2AlNKbRfc58tpHix9AakMHbdWr7CZIi31k4+LDXVJbG9RjtdJmmvf87B/btJ8w57qjWjy3fITuvJTSAfnymQAppe8vo+yjwIkppf+2dtxCQ4QRsU1e6ZPAxIh4OCKanYMlSZJUSLTxo3VjgaERMSQiupKlVDcu1cyIzYEewP1FKi06B+uXwKkppQ1SSusDXweuKrivJElSu5RSWgicBNwKTAKuz6dDnR8Rh1QUHQGMTgWH/orOweqeUlo85yqldFd+byxJkqQV1h7ug5VSGgOMabLunCbL5y1PnUU7WFMi4mzgt/nyMcCU5TmQJElSR1G0g/UF4DvAX4EE3JuvkyRJWmHtIcFqCy12sCKiG3A8sAkwHvi6fx5HkiSpZa0lWNcCC8gSqwOBLcjuiSVJkrTSOmSCBWyZUtoGICJ+DTzU9k2SJEl6f2utg7V4ODCltLBWe5mSJKkkNdq1aK2DtW1EzMt/DmD1fDmAlFLyb2FIkiQ10WIHK6XkH3SWJEltplZHx4reyV2SJEkFFb0PliRJUtXVaoJlB0uSJJWmVjtYDhFKkiRVmQmWJEkqjQmWJEmSCjHBkiRJ5anNAMsES5IkqdpMsCRJUmmcgyVJkqRCTLAkSVJpTLAkSZJUiAmWJEkqjQmWJEmSCjHBkiRJ5anNAMsES5IkqdpMsCRJUmmcgyVJkqRCTLAkSVJpTLAkSZJUiAmWJEkqjQmWJEmSCjHBkiRJpanVBMsOliRJKk9t9q8cIpQkSao2EyxJklSaWh0iNMGSJEmqMhMsSZJUGhMsSZIkFWKCJUmSSlOjAZYJliRJUrWZYEmSpNI4B0uSJEmFmGBJkqTS1GiAZYIlSZJUbSZYkiSpNM7BkiRJUiEmWJIkqTQ1GmCZYEmSJFWbCZYkSSpNXV1tRlgmWJIkSVVmgiVJkkrjHCxJkiQVYoIlSZJKU6v3wbKDJUmSSlOj/SuHCCVJkqrNBEuSJJWmVocITbAkSVKHFhHDI+LpiJgcEWc0U+aIiJgYEU9GxB9aq9MES5IklabsBCsiOgGXA/sB04CxEXFjSmliRZmhwJnAh1NKr0VEn9bqNcGSJEkd2U7A5JTSlJTSfGA0cGiTMl8CLk8pvQaQUprdWqV2sCRJUmki2vZRwEBgasXytHxdpU2BTSPivoh4ICKGt1apQ4SSJKlmRcRIYGTFqlEppVHLWU1nYCgwDBgE3BMR26SU5ra0gyRJUinaeg5W3plqqUM1HRhcsTwoX1dpGvBgSmkB8FxEPEPW4RrbXKUOEUqSpI5sLDA0IoZERFdgBHBjkzJ/J0uviIheZEOGU1qq1ARLkiSVpuzbYKWUFkbEScCtQCfg6pTSkxFxPjAupXRjvm3/iJgI1APfSCnNaaleO1iSJKlDSymNAcY0WXdOxc8JODV/FGIHS5Iklabs+2C1FedgSZIkVZkJliRJKk2NBlgmWJIkSdVmgiVJkkrjHCxJkiQVYoIlSZJKU6MBlgmWJElStZlgSZKk0tTqHKw272BdOHzztj6EVMi+372j7CZIi+1x9n5lN0FSGzLBkiRJpanRAMsOliRJKk+tDhE6yV2SJKnKTLAkSVJpajTAMsGSJEmqNhMsSZJUGudgSZIkqRATLEmSVJoaDbBMsCRJkqrNBEuSJJXGOViSJEkqxARLkiSVxgRLkiRJhZhgSZKk0tRogGWCJUmSVG0mWJIkqTTOwZIkSVIhJliSJKk0NRpgmWBJkiRVmwmWJEkqjXOwJEmSVIgJliRJKk2NBlh2sCRJUnnqarSH5RChJElSlZlgSZKk0tRogGWCJUmSVG0mWJIkqTTepkGSJEmFmGBJkqTS1NVmgGWCJUmSVG0mWJIkqTTOwZIkSVIhJliSJKk0NRpgmWBJkiRVmwmWJEkqTVCbEZYJliRJUpWZYEmSpNJ4HyxJkiQVYoIlSZJK432wJEmSVIgJliRJKk2NBlgmWJIkSdVmgiVJkkpTV6MRlh0sSZJUmhrtXzlEKEmSVG0mWJIkqTTepkGSJKkGRcTwiHg6IiZHxBnL2H5sRLwcEY/ljy+2VqcJliRJKk3ZAVZEdAIuB/YDpgFjI+LGlNLEJkX/lFI6qWi9JliSJKkj2wmYnFKaklKaD4wGDl3ZSu1gSZKk0tRFtOkjIkZGxLiKx8gmTRgITK1Ynpava+oTEfFERPw5Iga3dl4OEUqSpJqVUhoFjFrJam4C/phSei8i/h9wLbBPSzuYYEmSpNJEGz8KmA5UJlKD8nWLpZTmpJTeyxd/BezQWqV2sCRJUkc2FhgaEUMioiswArixskBE9K9YPASY1FqlDhFKkqTSlH0frJTSwog4CbgV6ARcnVJ6MiLOB8allG4ETo6IQ4CFwKvAsa3VawdLkiR1aCmlMcCYJuvOqfj5TODM5anTDpYkSSpNXW3eyN05WJIkSdVmgiVJkkpT9hystmKCJUmSVGUmWJIkqTQ1GmCZYEmSJFWbCZYkSSqNc7AkSZJUiAmWJEkqTa3eB8sOliRJKo1DhJIkSSrEBEuSJJWmNvMrEyxJkqSqM8GSJEmlqXMOliRJkoowwZIkSaWp0QDLBEuSJKnaTLAkSVJpvA+WJEmSCjHBkiRJpanRAMsES5IkqdpMsCRJUmlq9T5YdrDagYcfvI9RP7uYhoYG9j/4MD51zBcabZ/w2MNcddkPeW7Ks3zz3B+w+7D9Fm+7+spLGHf/vTQ0JD74oV0YefI3a3bCoNreXlv05tzDt6ZTXTD6/he58o7JS5U5+IP9+dqBm5ESTJr+Oidf9ygAUy79KE/NmAfAjNfe4YtXjV2lbVftGffAffzipxfR0NDA8I8exhGfOa7R9vGPPcwvf3Yxz/3vWc447yL22Dt7bXz8kYcY9bMfLS439cXnOOO8i9htz31WafvVsdnBKll9fT1XXvJ9LvzJL+jZuy9fG3k0O+++F+tvuPHiMr379uOr3zqfv46+rtG+k8Y/xqTxj3HZb24A4JsnfZ7xj43jAx/80Co9B9WGuoALPrUNR1/+ADPnvsONp+3BHRNm8uzMNxeX2bB3d07cbyiHX3If895ZQM81uy7e9u6Ceg66+J4ymq4aVF9fz+U/+R7fu+SX9OrTl1O++Gl23n0YGwxZ8trYp28/vv6tC/jLH69ttO+22+/E5ddcD8Ab817nC0d+lO132nWVtl/F1Wom4Byskj0zaQL9Bw6m34BBdOnShT0/cgAP/OeuRmX69h/IkI03XTpGjWD+/PksXLiABQvmU79wIT169Fx1jVdN2W6DHjz/8ltMnfM2C9Jlu+QAABiqSURBVOoTNz0yg/226deozFG7rs919z7PvHcWADDnzfllNFUdwDOTJjBg0GD6D8xeG/fad/iyXxs32ZSoa/6t7N5/386Ou+xOt26rt3GLpcYKJVgRsf6y1qeUXqxuczqeOa/MpnefJW9ivXr35emJ4wvtu8XW2/KBD36Izx62LynBRw8/ksEbbtRWTVWN67duN16a+87i5ZfmvssHN1i3UZkhfdYE4C9f/TB1dcGl/3yauye9DMBqneu46bQ9WNiQuPL2ydw2fuaqa7xqzisvN31t7FP4tbHSPf+6hcOO/Ew1m6Yqq9VpLUWHCG8GEhBAN2AI8DSw1bIKR8RIYCTA+T+8jBFNxs1VHTOmvcjUF6ZwzZ9vA+Csrx/PhMcfYettty+5ZapVneuCDXt358if/Zf+63bj+lM+zAE/uIt57yxkt/P+xazX32VwzzX440m78tRL83jxlbfLbrI6sFdfeZnnpkxmh513K7sp6oAKdbBSSttULkfE9sCXWyg/ChgF8Oysd9LKNLDW9ezVh5dnL/mk/8rLs+jZu0+hfe+/90422+oDrL7GGgDsuPOHeerJx+1gaYXMnPsu/dddMozSf91uzHz93UZlXpr7Do+9MJeFDYmpr77Dc7PfZMPe3XnixdeZlZedOudtHpg8h60HrWMHSyusV++mr42z6dm773LVcc+dt7HbHvvQuXOXajdPVVSrc5VW6LxSSo8AO1e5LR3SpptvxYxpLzJzxnQWLFjAPf+6lZ0/vFehfXv36c+Exx6mfuFCFi5cwPjHHmbwBg4RasU8/uJchvTuzuD1VqdLp+Bj2w/g9ibDfLeNn8kum2Tz/Hp078qQPmvy4itvs/bqXejauW7x+h2H9ODZmW+s8nNQ7dh0862YMfVFZs6YxoIFC7j7jlvYpeBr4yJ33fFPhu03vI1aKLWs6BysUysW64DtgRlt0qIOplPnzhz/1TM457QTaGhoYL+DDmWDIZvwu19fwdDNtmTn3YfxzKQJfPesU3nzjXk89N97+MPVV3LFdX/lw8P25YlHHuLEYz9FRLD9zrsV7pxJTdU3JM758wSu+/IudKoLrn9gKs/OfJNTD9qMJ16cyx0TZnH3pJfZc/Pe3PGtYdQ3JL73j4nMfXsBOwzpwfeO/AANKVEXwZV3TG707UNpeXXq3JkTTj2Ts049gfqGBvY/+ONssNEmXPery9l0863YZfdhPD1pAhd862u8+cY8Hrzvbn736yv45e/+BsCsl6bzyuyZbLPdjiWfiVpTq3OwIqXWR/Ai4tyKxYXA88BfUkrvLnuPJRwiVHux73fvKLsJ0mL/Pnu/1gtJq8hGvbuV1ss5+e9PtWk/4Wcf37yUcys6B+s7ABGxRkrJSRWSJKkq6mozwCo2Bysido2IicBT+fK2EXFFm7ZMkiTVvLpo20dp51Ww3KXAAcAcgJTS48CebdUoSZKk97PCfyonpTS1yUS0+uo3R5IkdSS1Osm9aAdrakTsBqSI6AKcAkxqu2ZJkiS9fxXtYB0P/BQYCEwHbgNObKtGSZKkjqFWJ7kX/RbhK8DRbdwWSZKkmtBiBysizmlhc0opXVDl9kiSpA6kRqdgtZpgvbWMdd2B44CegB0sSZKkJlrsYKWUfrzo54hYi2xy++eB0cCPm9tPkiSpiLoajbBanYMVEesBp5LNwboW2D6l9FpbN0ySJOn9qrU5WD8EDgdGAduklPzrrZIkqWqK3vH8/aa18/o6MAA4C5gREfPyxxsRMa/tmydJkvT+09ocrFrtWEqSpHagRqdg1WwyJ0mSVJrCf4tQkiSp2mr1W4QmWJIkSVVmgiVJkkpTowGWCZYkSVK1mWBJkqTS1JlgSZIkqQgTLEmSVJpa/RahHSxJklSaGu1fOUQoSZJUbXawJElSaeqibR9FRMTwiHg6IiZHxBktlPtERKSI2LHV8yr+FEiSJNWWiOgEXA4cCGwJHBURWy6j3FrAKcCDReq1gyVJkkoTbfxfATsBk1NKU1JK84HRwKHLKHcBcBHwbpFK7WBJkqSaFREjI2JcxWNkkyIDgakVy9PydZV1bA8MTindXPS4fotQkiSVpq1vNJpSGgWMWtH9I6IO+Alw7PLsZ4IlSZI6sunA4IrlQfm6RdYCtgbuiojngV2AG1ub6G6CJUmSStMO/lTOWGBoRAwh61iNAD69aGNK6XWg16LliLgLOC2lNK6lSk2wJElSh5VSWgicBNwKTAKuTyk9GRHnR8QhK1qvCZYkSSpNtINbuaeUxgBjmqw7p5myw4rUaYIlSZJUZSZYkiSpNO1gDlabMMGSJEmqMhMsSZJUmnYwBatNmGBJkiRVmQmWJEkqTV2NRlgmWJIkSVVmgiVJkkrjtwglSZJUiAmWJEkqTY1OwbKDJUmSylNHbfawHCKUJEmqMhMsSZJUmlodIjTBkiRJqjITLEmSVBpv0yBJkqRCTLAkSVJp/FM5kiRJKsQES5IklaZGAywTLEmSpGozwZIkSaVxDpYkSZIKMcGSJEmlqdEAywRLkiSp2kywJElSaWo16anV85IkSSqNCZYkSSpN1OgkLBMsSZKkKjPBkiRJpanN/MoES5IkqepMsCRJUmlq9U7udrAkSVJparN75RChJElS1ZlgSZKk0tToCKEJliRJUrWZYEmSpNJ4o1FJkiQVYoIlSZJKU6tJT62elyRJUmlMsCRJUmmcgyVJkqRCTLAkSVJpajO/MsGSJEmqujZPsAb3XL2tDyEV8vhFB5XdBGmx/rudUnYTpMXeefTnpR3bOViSJEkqxDlYkiSpNLWa9NTqeUmSJJXGBEuSJJXGOViSJEkqxARLkiSVpjbzKxMsSZKkqjPBkiRJpanRKVh2sCRJUnnqanSQ0CFCSZKkKjPBkiRJpanVIUITLEmS1KFFxPCIeDoiJkfEGcvYfnxEjI+IxyLiPxGxZWt12sGSJEmliTb+r9XjR3QCLgcOBLYEjlpGB+oPKaVtUkrbARcDP2mtXjtYkiSpI9sJmJxSmpJSmg+MBg6tLJBSmlex2B1IrVXqHCxJklSadjAHayAwtWJ5GrBz00IRcSJwKtAV2Ke1Sk2wJElSzYqIkRExruIxckXqSSldnlLaGDgdOKu18iZYkiSpNG19H6yU0ihgVAtFpgODK5YH5euaMxq4srXjmmBJkqSObCwwNCKGRERXYARwY2WBiBhasXgw8GxrlZpgSZKk0pQ9ByultDAiTgJuBToBV6eUnoyI84FxKaUbgZMiYl9gAfAa8LnW6rWDJUmSOrSU0hhgTJN151T8fMry1mkHS5IklabsBKutOAdLkiSpykywJElSaYrcbf39yARLkiSpykywJElSaepqM8AywZIkSao2EyxJklQa52BJkiSpEBMsSZJUGu+DJUmSpEJMsCRJUmlqdQ6WHSxJklQab9MgSZKkQkywJElSaWp1iNAES5IkqcpMsCRJUmm8TYMkSZIKMcGSJEmlqdEAywRLkiSp2kywJElSaepqdBKWCZYkSVKVmWBJkqTS1GZ+ZYIlSZJUdSZYkiSpPDUaYZlgSZIkVZkJliRJKo1/i1CSJEmFmGBJkqTS1OhtsEywJEmSqs0ES5IklaZGAywTLEmSpGozwZIkSeWp0QjLDpYkSSqNt2mQJElSISZYkiSpNN6mQZIkSYWYYEmSpNLUaIBlgiVJklRtJliSJKk8NRphmWBJkiRVmQmWJEkqjffBkiRJUiEmWJIkqTTeB0uSJEmFmGBJkqTS1GiAZYIlSZJUbSZYkiSpPDUaYZlgSZIkVZkJliRJKo33wZIkSVIhJliSJKk03gdLkiRJhZhgSZKk0tRogGUHS5IklahGe1gOEUqSJFWZCZYkSSqNt2mQJEmqQRExPCKejojJEXHGMrafGhETI+KJiPhXRGzQWp12sNqB++69h0MOPoCPDt+PX181aqnt8+fP5xtf/yofHb4fR4/4FNOnTwPg5v+7kSMOP3TxY7utN+epSZNWdfNVo+6/714+dehBfOJjB3Dt1Vcttf3Rh8fx2RGfYLcdtuFft99aQgtV6/bbbQse/9vZTPjHuZz2+f2W2j64Xw9uGXUy9//xdB7605kcsPuWAOy41QY8MPoMHhh9Bg/+6QwO2fsDq7rpWg4Rbfto/fjRCbgcOBDYEjgqIrZsUuxRYMeU0geAPwMXt1avQ4Qlq6+v53vfPZ9fXvUb+vbty6eP/CTD9t6HjTfZZHGZv/3lBtZee23+75bb+eeYm7n0Jz/ihz++lIM/eggHf/QQAJ595mm+evKJbL7FFmWdimpIfX09P/z+hVz2i1/Rp29fjj36SPbYa2822njJddm3X3/OPv97/P6635TYUtWqurrg0jOO4OATfs70WXP5z++/wf/dPZ6npsxcXOb0Lw7nL7c/wlU3/IfNN+rH3y87gc0PPpcn/zeDDx99MfX1DfTrtTYP/ulMbr5nAvX1DSWekdqxnYDJKaUpABExGjgUmLioQErp3xXlHwCOaa1SE6ySTRj/BIMHb8CgwYPp0rUrww86mLv+/a9GZf59550ccuhhAOy3/wE89MD9pJQalfnnmJsZfuDBq6zdqm0TJ4xn0OD1GThoMF26dGW/Aw7knrvubFRmwMCBDN10M+rClxFV34e23pD/TX2F56fPYcHCem649RE+OqxxEpVSYu3u3QBYZ83Veenl1wF4590FiztTq3XtstTrpdqXaOtHxMiIGFfxGNmkCQOBqRXL0/J1zTkO+Gdr51UowYqI9Zax+o2U0oIi+6t5s2fNol//fouX+/Tty/gnnmhcZvYs+vXrD0Dnzp1Zc621mDv3NXr0WPJrufWWMVx62RWrptGqebNnz6Jvv8rrsh9Pjn+ihT2k6hrQZx2mzXpt8fL0Wa+x09YbNirz3V+O4aYrTuKEEXuxxuqrcfDxly3e9qGtN+AX5x3D+v3X47izrjW96sBSSqOApeffrICIOAbYEdirtbJFP3o+ArwMPAM8m//8fEQ8EhE7LKMBi3uLy5pTpOp64onH6dZtdYYO3bTspkjSKnPE8B353U0PsMnwsznsK1fy6ws/S+STbsZOeIEdPvlddj/mYr7xhf1ZraszYtqtto6wWjcdGFyxPChf17iZEfsC3wYOSSm911qlRTtYtwMHpZR6pZR6kk0E+z/gy8BSsUlKaVRKaceU0o7HfalpEqdKffr2ZeZLS+YUzJ41i759+zYu06cvM2e+BMDChQt58403WHfdHou33zrmZg48yOFBVU+fPn2ZNbPyupxJ7z59SmyROpoZs19nUN8lr3MD+/Zgej4EuMjnPr4rf7ntEQAefOI5unXtQq91uzcq8/Rzs3jz7ffYapMBbd9ovV+NBYZGxJCI6AqMAG6sLBARHwR+Sda5ml2k0qIdrF1SSou/JpRSug3YNaX0ALBawTq0DFttvQ0vvvg806ZNZcH8+dwy5mb22nufRmWG7b0PN/7jbwDcftut7LTzLos/pTU0NHDrrf90/pWqaouttmbqiy8wY/o0FiyYz+23/pM999q77GapAxn35Atssn5vNhjQky6dO/GpA7bn5rsaD1NPnfkqw3baDIDNhvSl22pdePm1N9lgQE86dcre3tbv34PNhvTjhRlzVvk5qJho4/9ak1JaCJwE3ApMAq5PKT0ZEedHxCF5sR8CawI3RMRjEXFjM9UtVjQzfSkiTgdG58tHArPyrzY6sL0SOnfuzJnfPocTRn6RhoZ6Pn7YJ9hkk6FcftlP2WqrrRm2z0c47BOf5NtnfIOPDt+PtddZh4t/dMni/R8eN5Z+/fozaPDgFo4iLZ/OnTtz2hnf5uQTvkRDQwMfO/QwNtpkKL+84jK22HIr9hy2DxMnjOebp57MG/Pmce89/+aqK3/O6L/eVHbTVSPq6xv42kXXc9MVJ9KpLrj2Hw8wacpMzj7hYB6Z+CI33z2eM37yN644+yi+cszepARfOue3AOz2wY047fP7s2BhPQ0NiVO+9yfmzH2r5DNSe5ZSGgOMabLunIqf913eOqPItysiohdwLrA7kID7gPOB14H1U0qTm9v33YX49Q21C+8uqC+7CdJi/Xc7pewmSIu98+jPS7ud+tMz327TfsJm/dYo5dwKJVgppVeAr0RE95RS048BzXauJEmSOqJCc7AiYreImEg2NklEbBsR3hNAkiStlPK/RNg2ik5yvwQ4AJgDkFJ6HNizrRolSZL0flb4xiAppanR+I/6OKFFkiStnDJjpjZUtIM1NSJ2A1JEdAFOIR8ulCRJUmNFO1jHAz8l+9s804HbgBPbqlGSJKljKHKvqvej5fkW4dFt3BZJkqSa0GIHKyLOaWFzSildUOX2SJKkDiRqM8BqNcFa1q1vuwPHAT0BO1iSJGmF1Wj/quUOVkrpx4t+joi1yCa3f57sT+b8uLn9JEmSOrJW52BFxHrAqWRzsK4Ftk8pvdbWDZMkSR1AjUZYrc3B+iFwODAK2Cal9OYqaZUkSdL7WGsJ1teB94CzgG9X3Gg0yCa5r92GbZMkSTWuQ96mIaVU9E/pSJIkKVf4T+VIkiRVW63epsGESpIkqcpMsCRJUmlqNMAywZIkSao2EyxJklSeGo2wTLAkSZKqzARLkiSVplbvg2WCJUmSVGUmWJIkqTTeB0uSJEmFmGBJkqTS1GiAZYIlSZJUbSZYkiSpNM7BkiRJUiEmWJIkqUS1GWGZYEmSJFWZCZYkSSpNrc7BsoMlSZJKU6P9K4cIJUmSqs0ES5IklaZWhwhNsCRJkqrMBEuSJJUmanQWlgmWJElSlZlgSZKk8tRmgGWCJUmSVG0mWJIkqTQ1GmCZYEmSJFWbCZYkSSqN98GSJElSISZYkiSpNN4HS5IkSYWYYEmSpPLUZoBlgiVJklRtJliSJKk0NRpgmWBJkiRVmwmWJEkqjffBkiRJUiEmWJIkqTS1eh8sO1iSJKk0DhFKkiSpEDtYkiSpQ4uI4RHxdERMjogzlrF9z4h4JCIWRsQni9RpB0uSJHVYEdEJuBw4ENgSOCoitmxS7EXgWOAPRet1DpYkSSpNO5iDtRMwOaU0BSAiRgOHAhMXFUgpPZ9vayhaqQmWJEmqWRExMiLGVTxGNikyEJhasTwtX7dSTLAkSVJp2vo2DSmlUcCoNj3IMphgSZKkjmw6MLhieVC+bqWYYEmSpNK0gzlYY4GhETGErGM1Avj0ylZqgiVJkjqslNJC4CTgVmAScH1K6cmIOD8iDgGIiA9FxDTgU8AvI+LJ1uo1wZIkSaUpP8CClNIYYEyTdedU/DyWbOiwMBMsSZKkKjPBkiRJ5WkPEVYbMMGSJEmqMhMsSZJUmra+D1ZZTLAkSZKqzARLkiSVph3cB6tNmGBJkiRVmQmWJEkqTY0GWCZYkiRJ1WaCJUmSylOjEZYdLEmSVBpv0yBJkqRCTLAkSVJpvE2DJEmSComUUtltUAERMTKlNKrsdkjg9aj2w2tR7ZUJ1vvHyLIbIFXwelR74bWodskOliRJUpXZwZIkSaoyO1jvH84xUHvi9aj2wmtR7ZKT3CVJkqrMBEuSJKnK7GC1IxFRHxGPRcSEiLghItYou03qeCIiRcSPK5ZPi4jzSmySOiivRb2f2cFqX95JKW2XUtoamA8cX3aD1CG9BxweEb3Kbog6PK9FvW/ZwWq/7gU2iYj1IuLvEfFERDwQER8AiIi98rTrsYh4NCLWKrm9qh0LySYOf63phojoHRF/iYix+ePD+frzIuK0inITImLDVdVg1awVuRZ7R8TtEfFkRPwqIl6wg6Yy2MFqhyKiM3AgMB74DvBoSukDwLeA6/JipwEnppS2A/YA3imjrapZlwNHR8Q6Tdb/FLgkpfQh4BPAr1Z5y9TRLO+1eC5wZ0ppK+DPwPqrrKVSBf/Yc/uyekQ8lv98L/Br4EGyFw9SSndGRM+IWBu4D/hJRPwe+GtKaVopLVZNSinNi4jrgJNp3HnfF9gylvx11rUjYs1V3T51HCtwLe4OHJbve0tEvLYq2ystYgerfXknT6QWi2b+zHhK6QcRcTNwEHBfRByQUnpqFbRRHcelwCPAbyrW1QG7pJTerSwYEQtpnIh3a/vmqQNZnmtxVbZLapZDhO3fvcDRABExDHgl/0S3cUppfErpImAssHmJbVQNSim9ClwPHFex+jbgK4sWImLRB4Lnge3zddsDQ1ZNK9URLOe1eB9wRL5uf6DHKmqm1IgdrPbvPGCHiHgC+AHwuXz9V/OJxE8AC4B/ltQ+1bYfA5UThE8Gdsy/dDGRJd90/QuwXkQ8CZwEPLNqm6kOoOi1+B1g/4iYAHwKmAm8sUpbKuGd3CVJNSQiVgPqU0oLI2JX4MqmUy+kVcE5WJKkWrI+cH1E1JHdT/BLJbdHHZQJliRJUpU5B0uSJKnK7GBJkiRVmR0sSZKkKrODJUmSVGV2sCRJkqrMDpYkSVKV/X9pcKd3EXDfzQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "hG8lLocT9i5B", "colab_type": "text" }, "source": [ "## FC Classifier (WITH DOUBLE SCENE)" ] }, { "cell_type": "code", "metadata": { "id": "aa6Heda69dgk", "colab_type": "code", "outputId": "6a8e9776-529d-446a-db9d-2896c00ae78e", "colab": { "base_uri": "https://localhost:8080/", "height": 357 } }, "source": [ "!ls" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " data\t\t\t\t\t train-final-faces\n", " drive\t\t\t\t\t train-final-faces.zip\n", " ensemble-scene-pose-audio\t\t train-final-fer\n", " ensemble-scene-pose-audio-face\t\t train-final-fer.zip\n", " ensemble-scene-pose-audio-face.zip\t train-final-frames\n", " ensemble-scene-pose-audio.zip\t\t train-final-frames.zip\n", " ensemble-scenex2-pose-audio\t\t train-final-pose\n", " ensemble-scenex2-pose-audio.zip\t train-final-pose.zip\n", " fc-prediction\t\t\t\t Train_labels.txt\n", " LICENSE\t\t\t\t val-final-audio\n", " models\t\t\t\t\t val-final-audio.zip\n", " notebooks\t\t\t\t val-final-faces\n", " README.md\t\t\t\t val-final-faces.zip\n", " reports\t\t\t\t val-final-fer\n", " requirements-predictions.txt\t\t val-final-fer.zip\n", " requirements.txt\t\t\t val-final-frames\n", "'Screen Shot 2020-05-26 at 8.53.32 PM.png' val-final-frames.zip\n", " src\t\t\t\t\t val-final-pose\n", " train-final-audio\t\t\t val-final-pose.zip\n", " train-final-audio.zip\t\t\t Val_labels.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "KJeEF0ZI9mGI", "colab_type": "code", "outputId": "8d70015d-1815-4c07-c731-2752f6b404b5", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "# !rm -rf ensemble-scenex2-pose-audio\n", "# !cp ../drive/'My Drive'/cs231n-project/datasets/emotiw/ensemble-scenex2-pose-audio.zip .\n", "# !unzip ensemble-scenex2-pose-audio.zip\n", "\n", "X_train = np.load(\"ensemble-scenex2-pose-audio/X_train.npy\")\n", "y_train = np.load(\"ensemble-scenex2-pose-audio/y_train.npy\")\n", "X_valid = np.load(\"ensemble-scenex2-pose-audio/X_valid.npy\")\n", "y_valid = np.load(\"ensemble-scenex2-pose-audio/y_valid.npy\")\n", "X_train.shape" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(2661, 260)" ] }, "metadata": { "tags": [] }, "execution_count": 125 } ] }, { "cell_type": "code", "metadata": { "id": "FjCmDW339sqn", "colab_type": "code", "outputId": "cedefdcd-7a83-4420-ac96-b16592293136", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "\n", "MODEL_NAME = \"ensemble-fc-v3-scenex2\"\n", "Path(f\"drive/My Drive/cs231n-project/models/{MODEL_NAME}\").mkdir(parents=True, exist_ok=True)\n", "checkpoint_path = \"drive/My Drive/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1,\n", " save_weights_only=False,\n", " save_best_only=True,\n", " monitor='val_accuracy',\n", " period=1\n", ")\n", "\n", "def create_model(inputs):\n", " x = tf.keras.layers.Dense(32, activation='relu')(inputs)\n", " x = tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())(x)\n", "\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", "inputs = tf.keras.Input(shape=(X_train.shape[1]))\n", "model = create_model(inputs)\n", "\n", "import pickle\n", "history = model.fit(\n", " x=X_train,\n", " y=y_train,\n", " epochs=30,\n", " callbacks=[cp_callback],\n", " validation_data=(X_valid, y_valid)\n", ")\n", "\n", "acc = history.history['accuracy']\n", "val_acc = history.history['val_accuracy']\n", "\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(acc, label='Training Accuracy')\n", "plt.plot(val_acc, label='Validation Accuracy')\n", "plt.legend(loc='lower right')\n", "plt.ylabel('Accuracy')\n", "plt.title('Training and Validation Accuracy')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(loss, label='Training Loss')\n", "plt.plot(val_loss, label='Validation Loss')\n", "plt.legend(loc='upper right')\n", "plt.ylabel('Cross Entropy')\n", "plt.title('Training and Validation Loss')\n", "plt.xlabel('epoch')\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n", "Epoch 1/30\n", "79/84 [===========================>..] - ETA: 0s - loss: 0.6713 - accuracy: 0.7551\n", "Epoch 00001: val_accuracy improved from -inf to 0.61619, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-scenex2/cp-0001.h5\n", "84/84 [==============================] - 0s 5ms/step - loss: 0.6680 - accuracy: 0.7576 - val_loss: 0.9056 - val_accuracy: 0.6162\n", "Epoch 2/30\n", "76/84 [==========================>...] - ETA: 0s - loss: 0.4944 - accuracy: 0.8273\n", "Epoch 00002: val_accuracy did not improve from 0.61619\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4981 - accuracy: 0.8253 - val_loss: 0.9775 - val_accuracy: 0.6110\n", "Epoch 3/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4764 - accuracy: 0.8331\n", "Epoch 00003: val_accuracy did not improve from 0.61619\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4777 - accuracy: 0.8309 - val_loss: 0.9310 - val_accuracy: 0.6136\n", "Epoch 4/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4685 - accuracy: 0.8313\n", "Epoch 00004: val_accuracy did not improve from 0.61619\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4682 - accuracy: 0.8313 - val_loss: 0.9578 - val_accuracy: 0.5966\n", "Epoch 5/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4599 - accuracy: 0.8410\n", "Epoch 00005: val_accuracy did not improve from 0.61619\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4637 - accuracy: 0.8380 - val_loss: 0.9723 - val_accuracy: 0.6044\n", "Epoch 6/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4575 - accuracy: 0.8375\n", "Epoch 00006: val_accuracy did not improve from 0.61619\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4551 - accuracy: 0.8384 - val_loss: 0.9640 - val_accuracy: 0.6123\n", "Epoch 7/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4373 - accuracy: 0.8477\n", "Epoch 00007: val_accuracy improved from 0.61619 to 0.62141, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-scenex2/cp-0007.h5\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4476 - accuracy: 0.8425 - val_loss: 0.9665 - val_accuracy: 0.6214\n", "Epoch 8/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4382 - accuracy: 0.8375\n", "Epoch 00008: val_accuracy did not improve from 0.62141\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4429 - accuracy: 0.8365 - val_loss: 0.9731 - val_accuracy: 0.6136\n", "Epoch 9/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4385 - accuracy: 0.8421\n", "Epoch 00009: val_accuracy did not improve from 0.62141\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4389 - accuracy: 0.8414 - val_loss: 0.9495 - val_accuracy: 0.6175\n", "Epoch 10/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4348 - accuracy: 0.8422\n", "Epoch 00010: val_accuracy improved from 0.62141 to 0.62533, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-scenex2/cp-0010.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4353 - accuracy: 0.8422 - val_loss: 0.9630 - val_accuracy: 0.6253\n", "Epoch 11/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4355 - accuracy: 0.8398\n", "Epoch 00011: val_accuracy did not improve from 0.62533\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4308 - accuracy: 0.8429 - val_loss: 0.9892 - val_accuracy: 0.6175\n", "Epoch 12/30\n", "78/84 [==========================>...] - ETA: 0s - loss: 0.4263 - accuracy: 0.8494\n", "Epoch 00012: val_accuracy did not improve from 0.62533\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4297 - accuracy: 0.8486 - val_loss: 0.9593 - val_accuracy: 0.6031\n", "Epoch 13/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4244 - accuracy: 0.8426\n", "Epoch 00013: val_accuracy improved from 0.62533 to 0.62924, saving model to drive/My Drive/cs231n-project/models/ensemble-fc-v3-scenex2/cp-0013.h5\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4239 - accuracy: 0.8429 - val_loss: 0.9531 - val_accuracy: 0.6292\n", "Epoch 14/30\n", "83/84 [============================>.] - ETA: 0s - loss: 0.4215 - accuracy: 0.8479\n", "Epoch 00014: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4216 - accuracy: 0.8478 - val_loss: 0.9511 - val_accuracy: 0.6162\n", "Epoch 15/30\n", "68/84 [=======================>......] - ETA: 0s - loss: 0.4226 - accuracy: 0.8433\n", "Epoch 00015: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4215 - accuracy: 0.8452 - val_loss: 0.9976 - val_accuracy: 0.6136\n", "Epoch 16/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4123 - accuracy: 0.8457\n", "Epoch 00016: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4211 - accuracy: 0.8459 - val_loss: 0.9676 - val_accuracy: 0.6188\n", "Epoch 17/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4129 - accuracy: 0.8511\n", "Epoch 00017: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4143 - accuracy: 0.8482 - val_loss: 0.9615 - val_accuracy: 0.6084\n", "Epoch 18/30\n", "79/84 [===========================>..] - ETA: 0s - loss: 0.4141 - accuracy: 0.8513\n", "Epoch 00018: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4138 - accuracy: 0.8516 - val_loss: 1.0045 - val_accuracy: 0.6044\n", "Epoch 19/30\n", "80/84 [===========================>..] - ETA: 0s - loss: 0.4106 - accuracy: 0.8516\n", "Epoch 00019: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4116 - accuracy: 0.8512 - val_loss: 0.9494 - val_accuracy: 0.6162\n", "Epoch 20/30\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.4049 - accuracy: 0.8538\n", "Epoch 00020: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4085 - accuracy: 0.8527 - val_loss: 0.9851 - val_accuracy: 0.6123\n", "Epoch 21/30\n", "76/84 [==========================>...] - ETA: 0s - loss: 0.3971 - accuracy: 0.8540\n", "Epoch 00021: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.4062 - accuracy: 0.8486 - val_loss: 0.9720 - val_accuracy: 0.6149\n", "Epoch 22/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.4026 - accuracy: 0.8544\n", "Epoch 00022: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4039 - accuracy: 0.8538 - val_loss: 0.9728 - val_accuracy: 0.6070\n", "Epoch 23/30\n", "64/84 [=====================>........] - ETA: 0s - loss: 0.4176 - accuracy: 0.8467\n", "Epoch 00023: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4058 - accuracy: 0.8527 - val_loss: 1.0183 - val_accuracy: 0.6070\n", "Epoch 24/30\n", "63/84 [=====================>........] - ETA: 0s - loss: 0.4048 - accuracy: 0.8517\n", "Epoch 00024: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.4018 - accuracy: 0.8534 - val_loss: 0.9786 - val_accuracy: 0.6044\n", "Epoch 25/30\n", "79/84 [===========================>..] - ETA: 0s - loss: 0.4054 - accuracy: 0.8525\n", "Epoch 00025: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3991 - accuracy: 0.8553 - val_loss: 0.9864 - val_accuracy: 0.6227\n", "Epoch 26/30\n", "84/84 [==============================] - ETA: 0s - loss: 0.3987 - accuracy: 0.8523\n", "Epoch 00026: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3987 - accuracy: 0.8523 - val_loss: 0.9900 - val_accuracy: 0.6084\n", "Epoch 27/30\n", "82/84 [============================>.] - ETA: 0s - loss: 0.3931 - accuracy: 0.8563\n", "Epoch 00027: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3946 - accuracy: 0.8549 - val_loss: 0.9798 - val_accuracy: 0.6123\n", "Epoch 28/30\n", "76/84 [==========================>...] - ETA: 0s - loss: 0.3950 - accuracy: 0.8540\n", "Epoch 00028: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3946 - accuracy: 0.8561 - val_loss: 1.0090 - val_accuracy: 0.6188\n", "Epoch 29/30\n", "76/84 [==========================>...] - ETA: 0s - loss: 0.3883 - accuracy: 0.8573\n", "Epoch 00029: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 4ms/step - loss: 0.3911 - accuracy: 0.8546 - val_loss: 0.9889 - val_accuracy: 0.6097\n", "Epoch 30/30\n", "65/84 [======================>.......] - ETA: 0s - loss: 0.3935 - accuracy: 0.8572\n", "Epoch 00030: val_accuracy did not improve from 0.62924\n", "84/84 [==============================] - 0s 3ms/step - loss: 0.3895 - accuracy: 0.8572 - val_loss: 0.9897 - val_accuracy: 0.6123\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "x1d7caEo_ssu", "colab_type": "code", "colab": {} }, "source": [ "model = tf.keras.models.load_model(\"drive/My Drive/cs231n-project/models/ensemble-fc-v3-scenex2/cp-0013.h5\")\n", "\n", "y_pred = model.predict(X_valid)\n", "y_valid_labels = np.argmax(y_valid, axis=1) + 1\n", "y_pred_labels = np.argmax(y_pred, axis=1) + 1\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "3p3wuWgk9w2C", "colab_type": "code", "outputId": "6a87c64c-df35-4846-e1f7-c1b4b8d52a3a", "colab": { "base_uri": "https://localhost:8080/", "height": 740 } }, "source": [ "from sklearn import metrics \n", "import pandas as pd\n", "import seaborn as sn\n", "cm=metrics.confusion_matrix(y_valid_labels,y_pred_labels)\n", "import matplotlib.pyplot as plt\n", "\n", "classes=['Pos' , 'Neu' , 'Neg'] \n", "y_pred_final , y_true_final = y_pred_labels - 1 , y_valid_labels - 1\n", "con_mat = tensorflow.math.confusion_matrix(labels=y_true_final, predictions=y_pred_final).numpy()\n", "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n", "con_mat_df = pd.DataFrame(con_mat_norm,\n", " index = classes, \n", " columns = classes)\n", "\n", "figure = plt.figure(figsize=(11, 9))\n", "plt.title(\"FC Voting Confusion Matrix with Scene (x2), Pose, and Audio\")\n", "sn.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues)\n", "\n", "\n", "accuracy = (y_pred_labels == y_valid_labels).mean()\n", "print(f\"Accuracy: {accuracy}\")\n", "from sklearn.metrics import classification_report\n", "target_names = [\"Positive\" , \"Neutral\" , \"Negative\"]\n", "print(classification_report(y_valid_labels, y_pred_labels, target_names=target_names, digits=3))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 0.6292428198433421\n", " precision recall f1-score support\n", "\n", " Positive 0.695 0.513 0.590 302\n", " Neutral 0.581 0.679 0.626 280\n", " Negative 0.634 0.745 0.685 184\n", "\n", " accuracy 0.629 766\n", " macro avg 0.637 0.645 0.634 766\n", "weighted avg 0.639 0.629 0.626 766\n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "zN0JlXSkDTif", "colab_type": "text" }, "source": [ "### Figure out why faces is bad?" ] }, { "cell_type": "code", "metadata": { "id": "fupy5bTw93Vg", "colab_type": "code", "outputId": "7c047124-b186-44c1-ea62-36013b649603", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "!ls val-final-fer" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "faces-fer-X.npy faces-fer-Y.npy\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Gm6FeDO1DdBf", "colab_type": "code", "colab": {} }, "source": [ "fer_x = np.load(\"train-final-fer/faces-fer-X.npy\")\n", "fer_y = np.load(\"train-final-fer/faces-fer-Y.npy\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "jV7z4UUaFP4j", "colab_type": "code", "colab": {} }, "source": [ "index = 0" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "JzOe4CkvDltK", "colab_type": "code", "outputId": "cf8c883b-2c63-4738-ad7b-ab1f944d2710", "colab": { "base_uri": "https://localhost:8080/", "height": 646 } }, "source": [ "# 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral\n", "\n", "with np.printoptions(precision=3, suppress=True):\n", " print(fer_x[index,:,0:7])\n", " print(fer_x[index,:,7:14])\n", " print(fer_x[index,:,14:21])\n", "np.argmax(fer_x[index,:,14:21], axis=1)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "[[0.168 0. 0.699 0.101 0.18 0.016 0.162]\n", " [0.222 0. 0.29 0.725 0.108 0.051 0.508]\n", " [0.466 0. 0.453 0.047 0.004 0.024 0.005]\n", " [0. 0. 0. 0. 0. 0. 0. ]\n", " [0.583 0. 0.768 0.016 0.022 0.056 0.019]\n", " [0.315 0. 0.24 0.636 0.088 0.017 0.414]\n", " [0.614 0. 0.027 0.252 0.011 0.004 0.092]\n", " [0.407 0. 0.583 0.009 0. 0.001 0. ]\n", " [0.729 0.002 0.446 0.735 0.026 0.098 0.012]\n", " [0.857 0. 0.868 0.095 0.051 0.121 0.013]\n", " [0.752 0. 0.299 0.009 0.004 0.004 0.001]\n", " [0.386 0. 0.902 0.742 0.024 0.098 0.017]]\n", "[[0.082 0. 0.471 0.059 0.052 0.005 0.006]\n", " [0.093 0. 0.034 0.063 0.007 0.008 0.025]\n", " [0.466 0. 0.453 0.047 0.004 0.024 0.005]\n", " [0. 0. 0. 0. 0. 0. 0. ]\n", " [0.119 0. 0.403 0.008 0.005 0.001 0.001]\n", " [0.076 0. 0.167 0.001 0.013 0.004 0.039]\n", " [0.614 0. 0.027 0.252 0.011 0.004 0.092]\n", " [0.407 0. 0.583 0.009 0. 0.001 0. ]\n", " [0.08 0. 0.168 0.013 0.003 0. 0. ]\n", " [0.083 0. 0.108 0.002 0.001 0. 0.001]\n", " [0.7 0. 0.229 0. 0.001 0. 0. ]\n", " [0.052 0. 0.078 0. 0.001 0.001 0.001]]\n", "[[0.125 0. 0.585 0.08 0.116 0.01 0.084]\n", " [0.162 0. 0.18 0.395 0.043 0.024 0.196]\n", " [0.466 0. 0.453 0.047 0.004 0.024 0.005]\n", " [0. 0. 0. 0. 0. 0. 0. ]\n", " [0.351 0. 0.585 0.012 0.013 0.028 0.01 ]\n", " [0.192 0. 0.209 0.358 0.041 0.011 0.188]\n", " [0.614 0. 0.027 0.252 0.011 0.004 0.092]\n", " [0.407 0. 0.583 0.009 0. 0.001 0. ]\n", " [0.433 0. 0.28 0.243 0.01 0.03 0.003]\n", " [0.437 0. 0.46 0.036 0.017 0.045 0.005]\n", " [0.726 0. 0.264 0.005 0.002 0.002 0.001]\n", " [0.164 0. 0.484 0.29 0.01 0.044 0.007]]\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "array([2, 3, 0, 0, 2, 3, 0, 2, 0, 2, 0, 2])" ] }, "metadata": { "tags": [] }, "execution_count": 158 } ] }, { "cell_type": "code", "metadata": { "id": "47m7xtSyFNo2", "colab_type": "code", "outputId": "eb521034-3476-41c3-c9ac-54a8d4e45620", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "# Num people\n", "fer_x[index, :, -1]" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([2., 3., 1., 0., 2., 3., 1., 1., 4., 4., 2., 4.])" ] }, "metadata": { "tags": [] }, "execution_count": 153 } ] }, { "cell_type": "code", "metadata": { "id": "UR7xEstqDmUf", "colab_type": "code", "outputId": "ad994e13-ac5a-4f23-f84f-4f9688e95ba3", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "fer_y[index]" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array(['204_1', '2'], dtype='\n", " \n", "\n", "\"\"\" % data_url)" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 156 } ] }, { "cell_type": "code", "metadata": { "id": "BCjy5UlsHov8", "colab_type": "code", "colab": {} }, "source": [ "from tensorflow.keras.models import load_model\n", "from sklearn.preprocessing import Normalizer\n", "import os, cv2\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras import layers\n", "from pathlib import Path\n", "\n", "\n", "class FaceClassifierV2:\n", " \"\"\"\n", " Classifies sentiment based on facial expressions extracted from videos\n", " \"\"\"\n", " def __init__(self, face_folder, is_test, model_location=None, batch_size=32):\n", " \"\"\"\n", " @param face_folder The folder where the arrays of processed facial expression embeddings are stored. If\n", " ends with .zip, this should be a single zip\n", " file containing the embeddings. Paths can either be accessed by a local\n", " folder or a GDrive mounted path.\n", " @param model_location The pre-trained model to perform predictions of labels\n", " @param is_test If set to True, we assume that we are testing . If false, the evaluate\n", " function will return a score.\n", " @param batch_size The batch size used to feed into the model evaluation\n", " \"\"\"\n", " self.face_folder = face_folder\n", " self.is_test = is_test\n", " self.model_location = model_location\n", " self.batch_size = batch_size\n", "\n", " self.load_data()\n", "\n", " print(f\"FacesClassifier created with face_folder = {face_folder} , is_test = {is_test} , model_location = {model_location}\")\n", " if self.model_location is not None:\n", " if \"https://\" in self.model_location or \"http://\" in self.model_location:\n", " downloaded_model_path = tf.keras.utils.get_file(\"face-classifier\", self.model_location)\n", " self.model = load_model(downloaded_model_path)\n", " else:\n", " self.model = load_model(self.model_location)\n", " else:\n", " self.model = self.init_model()\n", "\n", " def load_data(self):\n", " print(\"load data\")\n", " # self.X1 = np.load(os.path.join(self.face_folder, \"faces-fer-X.npy\"))[:, :, 0:7]\n", " # self.X2 = np.load(os.path.join(self.face_folder, \"faces-fer-X.npy\"))[:, :, -1]\n", " # self.X = np.concatenate((self.X1, np.expand_dims(self.X2, axis=2)), axis=2)\n", " # print(self.X.shape)\n", " self.X = np.load(os.path.join(self.face_folder, \"faces-fer-X.npy\"))\n", "\n", " if os.path.exists(os.path.join(self.face_folder, \"faces-fer-Y.npy\")):\n", " self.Y = np.load(os.path.join(self.face_folder, \"faces-fer-Y.npy\")).astype(int)[:, 1]\n", " self.Y = tf.keras.utils.to_categorical(self.Y, num_classes=3)\n", "\n", " def predict(self, layer=None):\n", " if layer is not None:\n", " print(f\"Customizing model by returning layer {layer}\")\n", " model = tf.keras.models.Model(self.model.input, self.model.get_layer(layer).output)\n", " else:\n", " model = self.model\n", " path = os.path.join(self.face_folder, \"faces-fer-Y.npy\")\n", " y = np.load(path, allow_pickle=True)\n", " samples = map(lambda x: str(x), y[:, 0])\n", " return model.predict(self.X, batch_size=self.batch_size), list(samples)\n", "\n", " def summary(self):\n", " self.model.summary()\n", "\n", " def load_model(self, best_model_filepath):\n", " return load_model(best_model_filepath)\n", "\n", " # Inputs (N,22): min, max, mean of all facial hidden features, # of faces in frame\n", " # Ouputs (3): frame sentiment\n", " def init_model(self):\n", " def create_model(inputs):\n", " x = inputs\n", " x = layers.Bidirectional(layers.LSTM(32, return_sequences=False))(inputs)\n", " x = tf.keras.layers.Dense(8, activation='relu')(x)\n", " x = tf.keras.layers.Dense(3, activation='softmax')(x)\n", "\n", " # optimizer = tf.keras.optimizers.SGD(learning_rate=1e-4)\n", "\n", " optimizer = tf.keras.optimizers.Adam(lr=0.005)\n", " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(optimizer=optimizer,\n", " loss = 'categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", " inputs = tf.keras.Input(shape=(self.X.shape[1], self.X.shape[2]))\n", " model = create_model(inputs)\n", " return model\n", "\n", " def train(self, faces_val_folder, epochs=20, save_path=None):\n", " \"\"\"\n", " - Outputs\n", " 1. Trained model -- saves the model as an .h5 file to the specified path\n", " \"\"\"\n", " # self.X_val1 = np.load(os.path.join(faces_val_folder, \"faces-fer-X.npy\"))[:, :, 0:7]\n", " # self.X_val2 = np.load(os.path.join(faces_val_folder, \"faces-fer-X.npy\"))[:, :, -1]\n", " # self.X_val = np.concatenate((self.X_val1, np.expand_dims(self.X_val2, axis=2)), axis=2)\n", "\n", " self.X_val = np.load(os.path.join(faces_val_folder, \"faces-fer-X.npy\")).astype(int)\n", " self.Y_val = np.load(os.path.join(faces_val_folder, \"faces-fer-Y.npy\")).astype(int)[:, 1]\n", " self.Y_val = tf.keras.utils.to_categorical(self.Y_val, num_classes=3)\n", "\n", " es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=300)\n", " mc = tf.keras.callbacks.ModelCheckpoint(save_path, monitor='val_loss', verbose=1, save_best_only=True)\n", "\n", " history = self.model.fit(self.X, self.Y, \n", " epochs=epochs,\n", " batch_size=self.batch_size, \n", " callbacks=[es, mc],\n", " validation_data=(self.X_val, self.Y_val))\n", "\n", " return self.model, history\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "R6-2DvDFFqwN", "colab_type": "code", "outputId": "ef383b1a-ca8b-41ac-e177-43b2afea99cb", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "from pathlib import Path\n", "import matplotlib.pyplot as plt\n", "\n", "MODEL_NAME = \"face-classifier-playground\"\n", "save_path = \"/content/drive/My Drive/cs231n-project/models/\" + MODEL_NAME + \"/cp-{epoch:04d}.h5\"\n", "Path(save_path).mkdir(parents=True, exist_ok=True)\n", "\n", "face_classifier = FaceClassifierV2(\"train-final-fer\", is_test=False)\n", "\n", "_, history = face_classifier.train(\"val-final-fer\", epochs=20, save_path=save_path)\n", "\n", "\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "load data\n", "FacesClassifier created with face_folder = train-final-fer , is_test = False , model_location = None\n", "Epoch 1/20\n", "80/84 [===========================>..] - ETA: 0s - loss: 1.0947 - accuracy: 0.3578\n", "Epoch 00001: val_loss improved from inf to 1.10639, saving model to /content/drive/My Drive/cs231n-project/models/face-classifier-playground/cp-0001.h5\n", "84/84 [==============================] - 1s 17ms/step - loss: 1.0949 - accuracy: 0.3596 - val_loss: 1.1064 - val_accuracy: 0.4125\n", "Epoch 2/20\n", "81/84 [===========================>..] - ETA: 0s - loss: 1.0814 - accuracy: 0.4059\n", "Epoch 00002: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 7ms/step - loss: 1.0822 - accuracy: 0.4059 - val_loss: 1.1160 - val_accuracy: 0.3995\n", "Epoch 3/20\n", "77/84 [==========================>...] - ETA: 0s - loss: 1.0690 - accuracy: 0.4030\n", "Epoch 00003: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 7ms/step - loss: 1.0698 - accuracy: 0.4059 - val_loss: 1.1454 - val_accuracy: 0.3342\n", "Epoch 4/20\n", "79/84 [===========================>..] - ETA: 0s - loss: 1.0613 - accuracy: 0.4343\n", "Epoch 00004: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 7ms/step - loss: 1.0653 - accuracy: 0.4269 - val_loss: 1.1105 - val_accuracy: 0.4125\n", "Epoch 5/20\n", "84/84 [==============================] - ETA: 0s - loss: 1.0551 - accuracy: 0.4397\n", "Epoch 00005: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 7ms/step - loss: 1.0551 - accuracy: 0.4397 - val_loss: 1.1596 - val_accuracy: 0.3433\n", "Epoch 6/20\n", "82/84 [============================>.] - ETA: 0s - loss: 1.0441 - accuracy: 0.4516\n", "Epoch 00006: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 1.0441 - accuracy: 0.4521 - val_loss: 1.1573 - val_accuracy: 0.2846\n", "Epoch 7/20\n", "79/84 [===========================>..] - ETA: 0s - loss: 1.0328 - accuracy: 0.4751\n", "Epoch 00007: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 1.0315 - accuracy: 0.4761 - val_loss: 1.1426 - val_accuracy: 0.3512\n", "Epoch 8/20\n", "82/84 [============================>.] - ETA: 0s - loss: 1.0253 - accuracy: 0.4733\n", "Epoch 00008: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 7ms/step - loss: 1.0265 - accuracy: 0.4728 - val_loss: 1.1543 - val_accuracy: 0.3590\n", "Epoch 9/20\n", "74/84 [=========================>....] - ETA: 0s - loss: 1.0183 - accuracy: 0.4721\n", "Epoch 00009: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 1.0179 - accuracy: 0.4739 - val_loss: 1.1628 - val_accuracy: 0.3538\n", "Epoch 10/20\n", "74/84 [=========================>....] - ETA: 0s - loss: 1.0093 - accuracy: 0.4962\n", "Epoch 00010: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 1.0092 - accuracy: 0.4919 - val_loss: 1.1691 - val_accuracy: 0.3499\n", "Epoch 11/20\n", "84/84 [==============================] - ETA: 0s - loss: 1.0027 - accuracy: 0.4976\n", "Epoch 00011: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 1.0027 - accuracy: 0.4976 - val_loss: 1.1378 - val_accuracy: 0.3486\n", "Epoch 12/20\n", "82/84 [============================>.] - ETA: 0s - loss: 0.9870 - accuracy: 0.5057\n", "Epoch 00012: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.9877 - accuracy: 0.5047 - val_loss: 1.1502 - val_accuracy: 0.3721\n", "Epoch 13/20\n", "82/84 [============================>.] - ETA: 0s - loss: 0.9845 - accuracy: 0.5057\n", "Epoch 00013: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.9837 - accuracy: 0.5066 - val_loss: 1.1352 - val_accuracy: 0.3747\n", "Epoch 14/20\n", "82/84 [============================>.] - ETA: 0s - loss: 0.9717 - accuracy: 0.5091\n", "Epoch 00014: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.9718 - accuracy: 0.5092 - val_loss: 1.1315 - val_accuracy: 0.4230\n", "Epoch 15/20\n", "82/84 [============================>.] - ETA: 0s - loss: 0.9528 - accuracy: 0.5278\n", "Epoch 00015: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.9522 - accuracy: 0.5291 - val_loss: 1.1155 - val_accuracy: 0.3786\n", "Epoch 16/20\n", "77/84 [==========================>...] - ETA: 0s - loss: 0.9394 - accuracy: 0.5345\n", "Epoch 00016: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 7ms/step - loss: 0.9383 - accuracy: 0.5333 - val_loss: 1.1881 - val_accuracy: 0.3956\n", "Epoch 17/20\n", "82/84 [============================>.] - ETA: 0s - loss: 0.9258 - accuracy: 0.5389\n", "Epoch 00017: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.9252 - accuracy: 0.5404 - val_loss: 1.1225 - val_accuracy: 0.3903\n", "Epoch 18/20\n", "74/84 [=========================>....] - ETA: 0s - loss: 0.9063 - accuracy: 0.5570\n", "Epoch 00018: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.9081 - accuracy: 0.5536 - val_loss: 1.1706 - val_accuracy: 0.4099\n", "Epoch 19/20\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.8911 - accuracy: 0.5629\n", "Epoch 00019: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.8907 - accuracy: 0.5614 - val_loss: 1.1722 - val_accuracy: 0.3760\n", "Epoch 20/20\n", "81/84 [===========================>..] - ETA: 0s - loss: 0.8742 - accuracy: 0.5748\n", "Epoch 00020: val_loss did not improve from 1.10639\n", "84/84 [==============================] - 1s 6ms/step - loss: 0.8776 - accuracy: 0.5723 - val_loss: 1.1985 - val_accuracy: 0.3956\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "zEJ1IMupIgFY", "colab_type": "code", "outputId": "a53583c2-6392-4eba-9766-1bd35492ea4d", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "len(history.history[\"accuracy\"])" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "30" ] }, "metadata": { "tags": [] }, "execution_count": 68 } ] }, { "cell_type": "code", "metadata": { "id": "xB1_1tSlTQB3", "colab_type": "code", "colab": {} }, "source": [ "model = tf.keras.models.load_model(\"/content/drive/My Drive/cs231n-project/models/face-classifier-playground/cp-0001.h5\")\n", "\n", "X_valid = np.load(\"val-final-fer/faces-fer-X.npy\")\n", "y_valid = np.array(np.load(\"val-final-fer/faces-fer-Y.npy\")[:, 1], dtype=int) + 1\n", "\n", "y_pred = model.predict(X_valid)\n", "y_valid_labels = y_valid\n", "y_pred_labels = np.argmax(y_pred, axis=1) + 1\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Pj7os2LPS6kL", "colab_type": "code", "outputId": "917b735f-8165-4e8a-ed8f-96db8adb4783", "colab": { "base_uri": "https://localhost:8080/", "height": 763 } }, "source": [ "from sklearn import metrics \n", "import pandas as pd\n", "import seaborn as sn\n", "cm=metrics.confusion_matrix(y_valid_labels,y_pred_labels)\n", "import matplotlib.pyplot as plt\n", "\n", "classes=['Pos' , 'Neu' , 'Neg'] \n", "y_pred_final , y_true_final = y_pred_labels - 1 , y_valid_labels - 1\n", "con_mat = tensorflow.math.confusion_matrix(labels=y_true_final, predictions=y_pred_final).numpy()\n", "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n", "con_mat_df = pd.DataFrame(con_mat_norm,\n", " index = classes, \n", " columns = classes)\n", "\n", "figure = plt.figure(figsize=(11, 9))\n", "plt.tick_params(labelsize=24)\n", "plt.title(\"FER Model Confusion Matrix\", fontsize=24)\n", "sn.set(font_scale=3.0)\n", "sn.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues, vmin=0, vmax=0.8)\n", "\n", "\n", "accuracy = (y_pred_labels == y_valid_labels).mean()\n", "print(f\"Accuracy: {accuracy}\")\n", "from sklearn.metrics import classification_report\n", "target_names = [\"Positive\" , \"Neutral\" , \"Negative\"]\n", "print(classification_report(y_valid_labels, y_pred_labels, target_names=target_names, digits=3))\n", "\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 0.4007832898172324\n", " precision recall f1-score support\n", "\n", " Positive 0.426 0.096 0.157 302\n", " Neutral 0.418 0.764 0.540 280\n", " Negative 0.344 0.348 0.346 184\n", "\n", " accuracy 0.401 766\n", " macro avg 0.396 0.403 0.348 766\n", "weighted avg 0.404 0.401 0.342 766\n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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