{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Auto-encoder with Conv2D\n", "\n", "This notebook demonstrates using a Conv2D network in an autoencoding task with the MNIST dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n", "ConX, version 3.7.5\n" ] } ], "source": [ "import conx as cx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we make a network. We will work with a 3 dimensional input from MNIST, but a flat target vector." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "net = cx.Network(\"Auto-Encoding with Conv\")\n", "net.add(cx.Layer(\"input\", (28,28,1)),\n", " cx.Conv2DLayer(\"Conv2D-1\", 16, (5,5), colormap=\"gray\", activation=\"relu\"),\n", " cx.MaxPool2DLayer(\"maxpool1\", (2,2)),\n", " cx.Conv2DLayer(\"Conv2D-2\", 132, (5,5), activation=\"relu\"),\n", " cx.MaxPool2DLayer(\"maxpool2\", (2,2)),\n", " cx.FlattenLayer(\"flatten\"))\n", "net.add(cx.Layer(\"output\", 28 * 28, vshape=(28,28), activation='sigmoid'))\n", "net.connect()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "net.compile(error=\"mse\", optimizer=\"adam\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load the MNIST dataset and examine the shapes of the inputs and targets." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "net.get_dataset(\"mnist\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**Dataset**: MNIST\n", "\n", "\n", "Original source: http://yann.lecun.com/exdb/mnist/\n", "\n", "The MNIST dataset contains 70,000 images of handwritten digits (zero\n", "to nine) that have been size-normalized and centered in a square grid\n", "of pixels. Each image is a 28 × 28 × 1 array of floating-point numbers\n", "representing grayscale intensities ranging from 0 (black) to 1\n", "(white). The target data consists of one-hot binary vectors of size\n", "10, corresponding to the digit classification categories zero through\n", "nine. Some example MNIST images are shown below:\n", "\n", "![MNIST Images](https://github.com/Calysto/conx/raw/master/data/mnist_images.png)\n", "\n", "**Information**:\n", " * name : MNIST\n", " * length : 70000\n", "\n", "**Input Summary**:\n", " * shape : (28, 28, 1)\n", " * range : (0.0, 1.0)\n", "\n", "**Target Summary**:\n", " * shape : (10,)\n", " * range : (0.0, 1.0)\n", "\n" ], "text/plain": [ "**Dataset**: MNIST\n", "\n", "\n", "Original source: http://yann.lecun.com/exdb/mnist/\n", "\n", "The MNIST dataset contains 70,000 images of handwritten digits (zero\n", "to nine) that have been size-normalized and centered in a square grid\n", "of pixels. Each image is a 28 × 28 × 1 array of floating-point numbers\n", "representing grayscale intensities ranging from 0 (black) to 1\n", "(white). The target data consists of one-hot binary vectors of size\n", "10, corresponding to the digit classification categories zero through\n", "nine. Some example MNIST images are shown below:\n", "\n", "![MNIST Images](https://github.com/Calysto/conx/raw/master/data/mnist_images.png)\n", "\n", "**Information**:\n", " * name : MNIST\n", " * length : 70000\n", "\n", "**Input Summary**:\n", " * shape : (28, 28, 1)\n", " * range : (0.0, 1.0)\n", "\n", "**Target Summary**:\n", " * shape : (10,)\n", " * range : (0.0, 1.0)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.dataset.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because this is an auto-encoding task, we wish that the targets were the same as the inputs:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: network 'Auto-Encoding with Conv' target bank #0 has a multi-dimensional shape; is this correct?\n" ] } ], "source": [ "net.dataset.set_targets_from_inputs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, that gives a warning. ConX does not allow targets to have a shape, so we flatten them:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "net.dataset.targets.reshape(28 * 28)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(784,)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.dataset.targets.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "35f9df111fa44e8ca24a2202d44df588", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.dashboard()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just to test our design, we chop the majority of patterns, leaving only 100." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "net.dataset.chop(69900)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And save 10% for testing/validation:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "net.dataset.split(0.1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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