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- {
- "nbformat_minor": 1,
- "cells": [
- {
- "execution_count": 3,
- "cell_type": "code",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": "Extracting MNIST_data/train-images-idx3-ubyte.gz\nExtracting MNIST_data/train-labels-idx1-ubyte.gz\nExtracting MNIST_data/t10k-images-idx3-ubyte.gz\nExtracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
- }
- ],
- "source": "from tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
- },
- {
- "execution_count": 12,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "import tensorflow as tf"
- },
- {
- "execution_count": 5,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "%matplotlib inline\nimport matplotlib.pyplot as plt"
- },
- {
- "execution_count": 26,
- "cell_type": "code",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": "[[ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n"
- },
- {
- "execution_count": 26,
- "metadata": {},
- "data": {
- "text/plain": "<matplotlib.image.AxesImage at 0x7f3252144fd0>"
- },
- "output_type": "execute_result"
- },
- {
- "output_type": "display_data",
- "data": {
- "image/png": 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b/EWld88AAAAASUVORK5CYII=\n",
- "text/plain": "<matplotlib.figure.Figure at 0x7f325219c128>"
- },
- "metadata": {}
- }
- ],
- "source": "batch_xs, batch_ys = mnist.train.next_batch(1)\nX = batch_xs\nX = X.reshape([28,28])\nplt.gray()\nprint(batch_ys)\nplt.imshow(X)"
- },
- {
- "execution_count": 31,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "x = tf.placeholder(tf.float32, [None, 784]) # add data during training\nW = tf.Variable(tf.zeros([784,10])) # Tensorflow variable to hold training data, one for each softmax regression model\nb = tf.Variable(tf.zeros([10])) # variable to hold bias, one for each regression model\ny = tf.nn.softmax(tf.matmul(x,W) + b) # create a model (no computation is happening at this stage), launching nodes together to form a computational graph"
- },
- {
- "execution_count": 32,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "y_ = tf.placeholder(tf.float32, [None,10]) #variable for hot-encoded vector"
- },
- {
- "execution_count": 38,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "cross_entropy = tf.reduce_mean( -tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) # cost function in a form of cross-entropy\n\ntrain_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
- },
- {
- "execution_count": 40,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "sess = tf.InteractiveSession()"
- },
- {
- "execution_count": 42,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "tf.global_variables_initializer().run()"
- },
- {
- "execution_count": 46,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "for _ in range(1000):\n batch_xs, batch_ys = mnist.train.next_batch(100)\n sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})"
- },
- {
- "execution_count": 48,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))"
- },
- {
- "execution_count": 50,
- "cell_type": "code",
- "metadata": {},
- "outputs": [],
- "source": "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
- },
- {
- "execution_count": 53,
- "cell_type": "code",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": "0.919\n"
- }
- ],
- "source": "print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.5 with Spark 2.1",
- "name": "python3-spark21",
- "language": "python"
- },
- "language_info": {
- "mimetype": "text/x-python",
- "nbconvert_exporter": "python",
- "version": "3.5.4",
- "name": "python",
- "file_extension": ".py",
- "pygments_lexer": "ipython3",
- "codemirror_mode": {
- "version": 3,
- "name": "ipython"
- }
- }
- },
- "nbformat": 4
- }
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