{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import tensorflow as tf\n", "sess = tf.Session()\n", "\n", "from keras import backend as K\n", "K.set_session(sess)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this placeholder will contain our input digits, as flat vectors\n", "img = tf.placeholder(tf.float32, shape=(None, 784))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.layers import Dense\n", "\n", "# Keras layers can be called on TensorFlow tensors:\n", "x = Dense(128, activation='relu')(img) # fully-connected layer with 128 units and ReLU activation\n", "x = Dense(128, activation='relu')(x)\n", "preds = Dense(10, activation='softmax')(x) # output layer with 10 units and a softmax activation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels = tf.placeholder(tf.float32, shape=(None, 10))\n", "\n", "from keras.objectives import categorical_crossentropy\n", "loss = tf.reduce_mean(categorical_crossentropy(labels, preds))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "URLError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m----------------------------------------------------------------\u001b[0m", "\u001b[0;31mTimeoutError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/home/jhoward/anaconda3/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1253\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m 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"\u001b[0;31mURLError\u001b[0m: " ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)\n", "\n", "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n", "with sess.as_default():\n", " for i in range(100):\n", " batch = mnist_data.train.next_batch(50)\n", " train_step.run(feed_dict={img: batch[0],\n", " labels: batch[1]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }