Created
July 22, 2019 08:49
-
-
Save yum-dev/64f75affc25ba3f4ea05572e2c51a1c9 to your computer and use it in GitHub Desktop.
CNN on Tensorflow MNIST data.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import tensorflow as tf" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 49, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from tensorflow.examples.tutorials.mnist import input_data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", | |
| "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", | |
| "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", | |
| "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "mnist = input_data.read_data_sets(\"MNIST_data/\",one_hot=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 51, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# HELPER" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# INIT WEIGHTS\n", | |
| "def init_weights(shape):\n", | |
| " init_random_dist = tf.truncated_normal(shape, stddev=0.1)\n", | |
| " return tf.Variable(init_random_dist)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# INIT BIAS\n", | |
| "def init_bias(shape):\n", | |
| " init_bias_vals = tf.constant(0.1, shape=shape)\n", | |
| " return tf.Variable(init_bias_vals)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# CONV2D\n", | |
| "def conv2d(x,W):\n", | |
| " # x --> input layer [batch, H(height), W(width), Channels]\n", | |
| " # W --> [filter H, filter W, Channels IN, Channels OUT]\n", | |
| " \n", | |
| " return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# POOLING\n", | |
| "def max_pool_2by2(x):\n", | |
| " # x --> [batch, h, w, c]\n", | |
| " return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 56, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Convolutional layer\n", | |
| "def convolutional_layer(input_x, shape):\n", | |
| " W = init_weights(shape)\n", | |
| " b = init_bias([shape[3]])\n", | |
| " return tf.nn.relu(conv2d(input_x,W)+b)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 57, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Normal fully connected layer\n", | |
| "def normal_full_layer(input_layer, size):\n", | |
| " input_size = int(input_layer.get_shape()[1])\n", | |
| " W = init_weights([input_size, size])\n", | |
| " b = init_bias([size])\n", | |
| " return tf.matmul(input_layer, W) + b" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 58, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Placeholders\n", | |
| "x = tf.placeholder(tf.float32, shape=[None,784]) # 784 because there are 784 pixels in each image\n", | |
| "y_true = tf.placeholder(tf.float32, shape=[None,10]) # 10 because lables are one-hot encoded between 0-9" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 59, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Layer\n", | |
| "x_image = tf.reshape(x,[-1,28,28,1]) # Converting flattended out image back to original grayscale image" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 60, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "convo_1 = convolutional_layer(x_image,shape=[5,5,1,32])\n", | |
| "convo_1_pooling = max_pool_2by2(convo_1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 61, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "convo_2 = convolutional_layer(convo_1, shape=[5,5,32,64])\n", | |
| "convo_2_pooling = max_pool_2by2(convo_2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 62, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "convo_2_flat = tf.reshape(convo_2_pooling,[-1,7*7*64])\n", | |
| "full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat,1024))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 63, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Dropout\n", | |
| "hold_prob = tf.placeholder(tf.float32)\n", | |
| "full_one_dropout = tf.nn.dropout(full_layer_one, keep_prob=hold_prob)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 64, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "y_pred = normal_full_layer(full_one_dropout,10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 65, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Loss function\n", | |
| "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_pred))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 66, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Optimizer \n", | |
| "optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)\n", | |
| "train = optimizer.minimize(cross_entropy)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 67, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "init = tf.global_variables_initializer()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 68, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "ename": "InvalidArgumentError", | |
| "evalue": "logits and labels must be same size: logits_size=[200,10] labels_size=[50,10]\n\t [[Node: SoftmaxCrossEntropyWithLogits_2 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Reshape_13, Reshape_14)]]\n\nCaused by op 'SoftmaxCrossEntropyWithLogits_2', defined at:\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\n ioloop.IOLoop.instance().start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n super(ZMQIOLoop, self).start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\n handler_func(fd_obj, events)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n self._handle_recv()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n self._run_callback(callback, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n callback(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n handler(stream, idents, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2802, in run_ast_nodes\n if self.run_code(code, result):\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-65-ebc1ce0b66fd>\", line 2, in <module>\n cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_pred))\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 1597, in softmax_cross_entropy_with_logits\n precise_logits, labels, name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2385, in _softmax_cross_entropy_with_logits\n features=features, labels=labels, name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 767, in apply_op\n op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 2630, in create_op\n original_op=self._default_original_op, op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1204, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[200,10] labels_size=[50,10]\n\t [[Node: SoftmaxCrossEntropyWithLogits_2 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Reshape_13, Reshape_14)]]\n", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1328\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m 467\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mInvalidArgumentError\u001b[0m: logits and labels must be same size: logits_size=[200,10] labels_size=[50,10]\n\t [[Node: SoftmaxCrossEntropyWithLogits_2 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Reshape_13, Reshape_14)]]", | |
| "\nDuring handling of the above exception, another exception occurred:\n", | |
| "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-68-53c2650b9d08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mbatch_x\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbatch_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhold_prob\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# PRINT OUT A MESSAGE EVERY 100 STEPS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1123\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1125\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1320\u001b[0m return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m options, run_metadata)\n\u001b[0m\u001b[1;32m 1322\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1323\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1338\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1339\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1340\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1342\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mInvalidArgumentError\u001b[0m: logits and labels must be same size: logits_size=[200,10] labels_size=[50,10]\n\t [[Node: SoftmaxCrossEntropyWithLogits_2 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Reshape_13, Reshape_14)]]\n\nCaused by op 'SoftmaxCrossEntropyWithLogits_2', defined at:\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\n ioloop.IOLoop.instance().start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n super(ZMQIOLoop, self).start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\n handler_func(fd_obj, events)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n self._handle_recv()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n self._run_callback(callback, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n callback(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n handler(stream, idents, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2802, in run_ast_nodes\n if self.run_code(code, result):\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-65-ebc1ce0b66fd>\", line 2, in <module>\n cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_pred))\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 1597, in softmax_cross_entropy_with_logits\n precise_logits, labels, name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2385, in _softmax_cross_entropy_with_logits\n features=features, labels=labels, name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 767, in apply_op\n op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 2630, in create_op\n original_op=self._default_original_op, op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1204, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[200,10] labels_size=[50,10]\n\t [[Node: SoftmaxCrossEntropyWithLogits_2 = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Reshape_13, Reshape_14)]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "steps = 5000\n", | |
| "\n", | |
| "with tf.Session() as sess:\n", | |
| " \n", | |
| " sess.run(init)\n", | |
| " \n", | |
| " for i in range(steps):\n", | |
| " \n", | |
| " batch_x , batch_y = mnist.train.next_batch(50)\n", | |
| " \n", | |
| " sess.run(train,feed_dict={x:batch_x,y_true:batch_y,hold_prob:0.5})\n", | |
| " \n", | |
| " # PRINT OUT A MESSAGE EVERY 100 STEPS\n", | |
| " if i%100 == 0:\n", | |
| " \n", | |
| " print('Currently on step {}'.format(i))\n", | |
| " print('Accuracy is:')\n", | |
| " # Test the Train Model\n", | |
| " matches = tf.equal(tf.argmax(y_pred,1),tf.argmax(y_true,1))\n", | |
| "\n", | |
| " acc = tf.reduce_mean(tf.cast(matches,tf.float32))\n", | |
| "\n", | |
| " print(sess.run(acc,feed_dict={x:mnist.test.images,y_true:mnist.test.labels,hold_prob:1.0}))\n", | |
| " print('\\n')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.5.4" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment