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| -rw-r--r-- | computer_vision.ipynb | 311 |
1 files changed, 311 insertions, 0 deletions
diff --git a/computer_vision.ipynb b/computer_vision.ipynb new file mode 100644 index 0000000..584b19d --- /dev/null +++ b/computer_vision.ipynb @@ -0,0 +1,311 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "computer_vision.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "id": "o8rKg5jPF_aa", + "colab_type": "code", + "outputId": "9569d1de-a4e6-42b0-ab60-713b627ec02d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "import os\n", + "import csv\n", + "import numpy as np\n", + "\n", + "repo_location = os.path.join('/content', 'e4-gan')\n", + "print(repo_location)\n", + "if not os.path.exists(repo_location):\n", + " !git clone https://git.skozl.com/e4-gan /content/e4-gan\n", + " \n", + "os.chdir(repo_location)\n", + "!cd /content/e4-gan\n", + "!git pull\n" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/content/e4-gan\n", + "Already up to date.\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "Mci7b38-bDjf", + "colab_type": "code", + "outputId": "0ec49551-a260-4469-f656-146b6a3bb226", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 161 + } + }, + "cell_type": "code", + "source": [ + "imgfolder = os.path.join(repo_location, 'images')\n", + "print(imgfolder)\n", + "if not os.path.exists(imgfolder):\n", + " !mkdir images\n", + " print('Make image directory')\n", + " \n", + "from dcgan import DCGAN\n", + "from cgan import CGAN\n", + "from cdcgan import CDCGAN\n", + "from lenet import *\n", + " \n", + "#vbn_dcgan = DCGAN(virtual_batch_normalization=True)\n", + "#utils = os.path.join('/content', 'utils')\n", + "cgan = CGAN()\n", + "cdcgan = CDCGAN()\n", + "\n", + "#dcgan.train(epochs=4000, batch_size=32, save_interval=1000)\n", + "#cgan.train(epochs=20000, batch_size=32, sample_interval=1000, graph=True)" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/content/e4-gan/images\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "LcifrT3feO6P", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#cdcgan.discriminator.save_weights('disc_weights.h5')\n", + "#cdcgan.generator.save_weights('gen_weights.h5')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "X_-PBBXitdui", + "colab_type": "code", + "outputId": "b49313cf-54b3-44ee-9afb-3dfe1d16906d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 125 + } + }, + "cell_type": "code", + "source": [ + "cdcgan.train(epochs=10001, batch_size=128, sample_interval=200, graph=True, smooth_real=0.9)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\r 0%| | 0/10001 [00:00<?, ?it/s]" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n", + " 'Discrepancy between trainable weights and collected trainable'\n", + " 7%|▋ | 728/10001 [01:38<19:14, 8.03it/s]" + ], + "name": "stderr" + } + ] + }, + { + "metadata": { + "id": "a56uNnvlwZgt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#cgan.train(epochs=10000, batch_size=32, sample_interval=1000, graph=True, smooth_real=0.9)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZYR97BHmMuQE", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "X_train, y_train, X_validation, y_validation, X_test, y_test = import_mnist()\n", + "train_gen, test_gen, val_gen, tr_labels_gen, te_labels_gen, val_labels_gen = cdcgan.generate_data()\n", + "\n", + "# If split = 0 use only original mnist set\n", + "train_data, train_labels, val_data, val_labels = mix_data(X_train, y_train, X_validation, y_validation, train_gen, tr_labels_gen, val_gen, val_labels_gen, split=0.3)\n", + "print(val_data.shape, val_labels.shape, train_data.shape, train_labels.shape)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zbrG6Uk8Tfqd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cdcgan.generator.save('gen.h5')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Lfd0uuM0m98s", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "model = train_classifier(train_data, train_labels, X_validation, y_validation, batch_size=128, epochs=100)\n", + "#For further steps of fine tuning use:\n", + "#model.fit(train_data, train_labels, batch_size=128, epochs=100, verbose=1, validation_data = (X_validation, y_validation))\n", + "model.save_weights('lenet.h5')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8e-UgoZ7et9D", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "model = get_lenet_icp((32,32,1))\n", + "model.load_weights('lenet.h5')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "CiGcNvjeNOjp", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "accuracy_mnist, inception_mnist = test_classifier(model, X_test, y_test)\n", + "print('Accuracy', accuracy_mnist)\n", + "print('Inception Score', inception_mnist)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mWPYOjK3X3cS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "accuracy_gen, inception_gen = test_classifier(model, test_gen, te_labels_gen)\n", + "print('Accuracy', accuracy_gen)\n", + "print('Inception Score', inception_gen)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZxTMGlwuj9vu", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "'''\n", + "import matplotlib.pyplot as plt \n", + "\n", + "precision_mnist = np.array(11)\n", + "inception_score = np.array(11)\n", + "\n", + "for i in range(11):\n", + " split = float(i)/10\n", + " train_data, train_labels, val_data, val_labels = mix_data(X_train, y_train, X_validation, y_validation, train_gen, tr_labels_gen, val_gen, val_labels_gen, split=split)\n", + " model = train_classifier(train_data, train_labels, X_validation, y_validation, batch_size=128, epochs=100)\n", + " precision_mnist[i] = test_classifier(model, X_test, y_test)\n", + " inception_score[i] = test_classifier(model, test_gen, te_labels_gen)\n", + " \n", + "xgrid = 100*np.arange(11)\n", + "plt.plot(xgrid, 100*precision_mnist)\n", + "plt.plot(xgrid, 100*inception_score)\n", + "plt.ylabel('Classification Accuracy (%)')\n", + "plt.xlabel('Amount of generated data used for training')\n", + "plt.legend(('MNIST Test Set', 'CGAN Generated Test Set'), loc='best')\n", + "plt.show()\n", + "'''" + ], + "execution_count": 0, + "outputs": [] + } + ] +}
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