1- Softmax.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from six.moves import cPickle as pickle\n",
    "from six.moves import range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set (200000, 28, 28) (200000,)\n",
      "Validation set (10000, 28, 28) (10000,)\n",
      "Test set (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'data1/notMNIST.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    train_dataset = save['train_dataset']\n",
    "    train_labels = save['train_labels']\n",
    "    valid_dataset = save['valid_dataset']\n",
    "    valid_labels = save['valid_labels']\n",
    "    test_dataset = save['test_dataset']\n",
    "    test_labels = save['test_labels']\n",
    "    del save  # hint to help gc free up memory\n",
    "    print('Training set', train_dataset.shape, train_labels.shape)\n",
    "    print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "    print('Test set', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set (200000, 784) (200000, 10)\n",
      "Validation set (10000, 784) (10000, 10)\n",
      "Test set (10000, 784) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "image_size=28\n",
    "num_labels=10\n",
    "\n",
    "def reformat(dataset, labels):\n",
    "    dataset = dataset.reshape((-1,image_size * image_size)).astype(np.float32)\n",
    "    labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
    "    return dataset,labels\n",
    "\n",
    "train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
    "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
    "test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
    "print('Training set', train_dataset.shape, train_labels.shape)\n",
    "print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "print('Test set', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_subset = 10000\n",
    "graph = tf.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    \n",
    "    #tf constant\n",
    "    tf_train_dataset = tf.constant(train_dataset[:train_subset,:])\n",
    "    tf_train_labels = tf.constant(train_labels[:train_subset])\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "    \n",
    "    #tf variable\n",
    "    weights = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "    biases = tf.Variable(tf.zeros(num_labels))\n",
    "    \n",
    "    # Training computation\n",
    "    \n",
    "    logits = tf.matmul(tf_train_dataset , weights) + biases\n",
    "    loss = tf.reduce_mean(\n",
    "        tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels,logits=logits)\n",
    "    )\n",
    "    \n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "    #These are not part of the training , but can help us in judging the accuracy figures\n",
    "    train_prediction = tf.nn.softmax(logits)\n",
    "    valid_prediction  = tf.nn.softmax(tf.matmul(valid_dataset, weights)+biases)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(test_dataset,weights) + biases)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyper params initialized..\n",
      "Loss at step 0: 18.056057\n",
      "Training accuracy: 7.0%\n",
      "Validation accuracy: 10.0%\n",
      "Loss at step 100: 2.286180\n",
      "Training accuracy: 71.0%\n",
      "Validation accuracy: 71.0%\n",
      "Loss at step 200: 1.828773\n",
      "Training accuracy: 75.0%\n",
      "Validation accuracy: 73.0%\n",
      "Loss at step 300: 1.590837\n",
      "Training accuracy: 76.0%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 400: 1.430886\n",
      "Training accuracy: 77.0%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 500: 1.312023\n",
      "Training accuracy: 78.0%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 600: 1.218273\n",
      "Training accuracy: 79.0%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 700: 1.141401\n",
      "Training accuracy: 79.0%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 800: 1.076732\n",
      "Training accuracy: 80.0%\n",
      "Validation accuracy: 76.0%\n",
      "Test accuracy: 83.0%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 801\n",
    "def accuracy(predictions,labels):\n",
    "    return 100* (np.sum(np.argmax(predictions,1)== np.argmax(labels,1)))/ predictions.shape[0]\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.global_variables_initializer().run()\n",
    "    print('Hyper params initialized..')\n",
    "    for step in range(num_steps):\n",
    "        _,l,predictions = session.run([optimizer,loss,train_prediction])\n",
    "        if (step % 100 == 0):\n",
    "            print('Loss at step %d: %f' % (step, l))\n",
    "            print('Training accuracy: %.1f%%' % accuracy(predictions, train_labels[:train_subset, :]))\n",
    "            print('Validation accuracy: %.1f%%' % accuracy(valid_prediction.eval(), valid_labels))\n",
    "    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data. For the training data, we use a placeholder that will be fed\n",
    "  # at run time with a training minibatch.\n",
    "  tf_train_dataset = tf.placeholder(tf.float32,\n",
    "                                    shape=(batch_size, image_size * image_size))\n",
    "  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "  tf_valid_dataset = tf.constant(valid_dataset)\n",
    "  tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "  # Variables.\n",
    "  weights = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "  biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "  # Training computation.\n",
    "  logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\n",
    "  \n",
    "  # Optimizer.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "  # Predictions for the training, validation, and test data.\n",
    "  train_prediction = tf.nn.softmax(logits)\n",
    "  valid_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 16.011055\n",
      "Minibatch accuracy: 5.0%\n",
      "Validation accuracy: 10.0%\n",
      "Minibatch loss at step 500: 1.382227\n",
      "Minibatch accuracy: 76.0%\n",
      "Validation accuracy: 77.0%\n",
      "Minibatch loss at step 1000: 1.132931\n",
      "Minibatch accuracy: 78.0%\n",
      "Validation accuracy: 77.0%\n",
      "Minibatch loss at step 1500: 0.972798\n",
      "Minibatch accuracy: 81.0%\n",
      "Validation accuracy: 78.0%\n",
      "Minibatch loss at step 2000: 1.125831\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 79.0%\n",
      "Minibatch loss at step 2500: 0.766437\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 79.0%\n",
      "Minibatch loss at step 3000: 1.118462\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 79.0%\n",
      "Test accuracy: 86.0%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 3001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print(\"Initialized\")\n",
    "  for step in range(num_steps):\n",
    "    # Pick an offset within the training data, which has been randomized.\n",
    "    # Note: we could use better randomization across epochs.\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "    # Generate a minibatch.\n",
    "    batch_data = train_dataset[offset:(offset + batch_size), :]\n",
    "    batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "    # Prepare a dictionary telling the session where to feed the minibatch.\n",
    "    # The key of the dictionary is the placeholder node of the graph to be fed,\n",
    "    # and the value is the numpy array to feed to it.\n",
    "    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "    _, l, predictions = session.run(\n",
    "      [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    if (step % 500 == 0):\n",
    "      print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "      print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "      print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
    "        valid_prediction.eval(), valid_labels))\n",
    "  print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size=128\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    tf_train_dataset = tf.placeholder(np.float32,shape=(batch_size,image_size * image_size))\n",
    "    tf_train_labels = tf.placeholder(np.float32,shape=(batch_size,num_labels))\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "    \n",
    "    # Variables & Layer 1\n",
    "    \n",
    "    weight_layer_inp = tf.Variable(tf.truncated_normal([image_size*image_size , 80]))\n",
    "    bias_layer_inp = tf.Variable(tf.zeros(80))\n",
    "    logits_relu_layer_inp = tf.nn.relu(tf.matmul(tf_train_dataset,weight_layer_inp)+bias_layer_inp)\n",
    "    \n",
    "    #  Layer 2 - Final Layer\n",
    "    \n",
    "    weights = tf.Variable(tf.truncated_normal([80,num_labels]))\n",
    "    biases = tf.Variable(tf.zeros(num_labels))\n",
    "    logits = tf.matmul(logits_relu_layer_inp,weights) + biases\n",
    "    \n",
    "    loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\n",
    "    \n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.nn.softmax(logits)\n",
    "    valid_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf.nn.relu(tf.matmul(tf_valid_dataset,weight_layer_inp)+bias_layer_inp), weights) + biases)\n",
    "    test_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset,weight_layer_inp)+bias_layer_inp), weights) + biases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 98.286636\n",
      "Minibatch accuracy: 10.0%\n",
      "Validation accuracy: 26.0%\n",
      "Minibatch loss at step 500: 1.607130\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 72.0%\n",
      "Minibatch loss at step 1000: 0.897938\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 75.0%\n",
      "Minibatch loss at step 1500: 0.893911\n",
      "Minibatch accuracy: 80.0%\n",
      "Validation accuracy: 79.0%\n",
      "Minibatch loss at step 2000: 1.112662\n",
      "Minibatch accuracy: 71.0%\n",
      "Validation accuracy: 78.0%\n",
      "Minibatch loss at step 2500: 0.666939\n",
      "Minibatch accuracy: 83.0%\n",
      "Validation accuracy: 80.0%\n",
      "Minibatch loss at step 3000: 1.262158\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 78.0%\n",
      "Minibatch loss at step 3500: 0.547273\n",
      "Minibatch accuracy: 87.0%\n",
      "Validation accuracy: 81.0%\n",
      "Minibatch loss at step 4000: 0.857965\n",
      "Minibatch accuracy: 78.0%\n",
      "Validation accuracy: 80.0%\n",
      "Minibatch loss at step 4500: 0.546081\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 81.0%\n",
      "Minibatch loss at step 5000: 1.643272\n",
      "Minibatch accuracy: 71.0%\n",
      "Validation accuracy: 81.0%\n",
      "Minibatch loss at step 5500: 1.016934\n",
      "Minibatch accuracy: 74.0%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 6000: 0.745458\n",
      "Minibatch accuracy: 81.0%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 6500: 0.709442\n",
      "Minibatch accuracy: 79.0%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 7000: 0.760316\n",
      "Minibatch accuracy: 78.0%\n",
      "Validation accuracy: 81.0%\n",
      "Minibatch loss at step 7500: 0.559880\n",
      "Minibatch accuracy: 87.0%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 8000: 0.789977\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 75.0%\n",
      "Minibatch loss at step 8500: 0.664641\n",
      "Minibatch accuracy: 83.0%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 9000: 0.741798\n",
      "Minibatch accuracy: 78.0%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 9500: 0.551342\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 10000: 0.950800\n",
      "Minibatch accuracy: 81.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 10500: 0.442917\n",
      "Minibatch accuracy: 86.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 11000: 0.693282\n",
      "Minibatch accuracy: 79.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 11500: 0.423579\n",
      "Minibatch accuracy: 87.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 12000: 0.527726\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 12500: 0.882506\n",
      "Minibatch accuracy: 76.0%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 13000: 0.380051\n",
      "Minibatch accuracy: 91.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 13500: 0.613062\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 14000: 0.643091\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 14500: 0.395902\n",
      "Minibatch accuracy: 87.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 15000: 0.497457\n",
      "Minibatch accuracy: 87.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 15500: 0.446957\n",
      "Minibatch accuracy: 89.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 16000: 0.517294\n",
      "Minibatch accuracy: 83.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 16500: 0.761138\n",
      "Minibatch accuracy: 78.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 17000: 0.453760\n",
      "Minibatch accuracy: 89.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 17500: 0.497337\n",
      "Minibatch accuracy: 88.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 18000: 0.528926\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 18500: 0.566766\n",
      "Minibatch accuracy: 80.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 19000: 0.485518\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 19500: 0.572679\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 20000: 0.444293\n",
      "Minibatch accuracy: 87.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 20500: 0.432467\n",
      "Minibatch accuracy: 88.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 21000: 0.552083\n",
      "Minibatch accuracy: 81.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 21500: 0.536178\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 22000: 0.615970\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 22500: 0.511646\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 23000: 0.524742\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 23500: 0.604540\n",
      "Minibatch accuracy: 83.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 24000: 0.360608\n",
      "Minibatch accuracy: 88.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 24500: 0.507298\n",
      "Minibatch accuracy: 85.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 25000: 0.567909\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 25500: 0.434246\n",
      "Minibatch accuracy: 88.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 26000: 0.610261\n",
      "Minibatch accuracy: 86.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 26500: 0.546956\n",
      "Minibatch accuracy: 81.0%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 27000: 0.609237\n",
      "Minibatch accuracy: 79.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 27500: 0.729117\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 28000: 0.528358\n",
      "Minibatch accuracy: 83.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 28500: 0.704860\n",
      "Minibatch accuracy: 80.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 29000: 0.573056\n",
      "Minibatch accuracy: 84.0%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 29500: 0.441822\n",
      "Minibatch accuracy: 86.0%\n",
      "Validation accuracy: 85.0%\n",
      "Test accuracy: 91.0%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 30000\n",
    "\n",
    "def accuracy(predictions,labels):\n",
    "    return 100* (np.sum(np.argmax(predictions,1)== np.argmax(labels,1)))/ predictions.shape[0]\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print(\"Initialized\")\n",
    "  for step in range(num_steps):\n",
    "    # Pick an offset within the training data, which has been randomized.\n",
    "    # Note: we could use better randomization across epochs.\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "    # Generate a minibatch.\n",
    "    batch_data = train_dataset[offset:(offset + batch_size), :]\n",
    "    batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "    # Prepare a dictionary telling the session where to feed the minibatch.\n",
    "    # The key of the dictionary is the placeholder node of the graph to be fed,\n",
    "    # and the value is the numpy array to feed to it.\n",
    "    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "    _, l, predictions = session.run(\n",
    "      [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    if (step % 500 == 0):\n",
    "      print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "      print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "      print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
    "        valid_prediction.eval(), valid_labels))\n",
    "  print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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