{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "-ZAknlBpEnJl" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import pickle\n", "import keras\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from skimage import transform\n", "from sklearn.metrics import accuracy_score\n", "from keras import backend as K\n", "\n", "plt.rcParams['image.cmap'] = 'viridis'\n", "plt.rcParams['axes.grid'] = False\n", "plt.rcParams['figure.figsize'] = (7, 4)\n", "plt.rcParams['font.family'] = 'serif'\n", "plt.rcParams['font.serif'] = 'FreeSerif'\n", "plt.rcParams['lines.linewidth'] = 1.4\n", "plt.rcParams['lines.markersize'] = 8\n", "plt.rcParams['xtick.labelsize'] = 16\n", "plt.rcParams['ytick.labelsize'] = 16\n", "plt.rcParams['legend.fontsize'] = 16\n", "plt.rcParams['axes.titlesize'] = 22\n", "plt.rcParams['axes.labelsize'] = 16" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from IPython.display import HTML, display\n", "\n", "# Progress bar drawer\n", "def progress(percent, info):\n", " return HTML(\"\"\"\n", " \n", "
{info}
\n", " \"\"\".format(percent=100*percent, info=info))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o5fF6xM3EnJ1" }, "source": [ "### Данные:\n", "Будем работать с датасетом [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist). " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "id": "tgFHpSctEnJ2" }, "outputs": [], "source": [ "from keras.datasets import fashion_mnist\n", "\n", "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n", "del y_train, y_test" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 364 }, "colab_type": "code", "id": "IZHAXqcqEnJ9", "outputId": "dbd1d9e5-6e16-47ec-e874-5fa18470be7e" }, "outputs": [ { "data": { "text/plain": [ "