{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0498f089", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "keras.__version__\n", "from sklearn import neighbors, datasets\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 2, "id": "20ad2a0d", "metadata": {}, "outputs": [], "source": [ "(X_train, y_train), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "20780205", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dtype('uint8')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train = X_train.reshape(X_train.shape[0], -1)\n", "X_test = X_test.reshape(X_test.shape[0], -1)\n", "\n", "assert X_train.shape == (60000, 784) #checks if it's been reshaped - flattended here.Execution stops if not true\n", "assert X_test.shape == (10000, 784)\n", "X_train.dtype\n", "#X_train[1]\n", "#y_train[1:100]\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "f0447be0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 1, 0, 0, 13, 73, 0, 0, 1,\n", " 4, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 3, 0, 36, 136, 127, 62,\n", " 54, 0, 0, 0, 1, 3, 4, 0, 0, 3, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 102, 204,\n", " 176, 134, 144, 123, 23, 0, 0, 0, 0, 12, 10, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 155, 236, 207, 178, 107, 156, 161, 109, 64, 23, 77, 130, 72,\n", " 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n", " 0, 69, 207, 223, 218, 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