Tensorflow - mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creates nodes in a graph\n",
    "# \"construction phase\"\n",
    "x1 = tf.constant(5)\n",
    "x2 = tf.constant(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Mul:0\", shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "result = tf.multiply(x1,x2)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "# defines our session and launches graph\n",
    "sess = tf.Session()\n",
    "# runs result\n",
    "print(sess.run(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "output = sess.run(result)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    output = sess.run(result)\n",
    "    print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-10-0328e0b4fafb>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /home/dheeraj/work/env1/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /home/dheeraj/work/env1/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /home/dheeraj/work/env1/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /home/dheeraj/work/env1/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /home/dheeraj/work/env1/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_nodes_hl1 = 500\n",
    "n_nodes_hl2 = 500\n",
    "n_nodes_hl3 = 500\n",
    "n_classes = 10\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder('float', [None, 784])\n",
    "y = tf.placeholder('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neural_network_model(data):\n",
    "    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([784, n_nodes_hl1])),\n",
    "                      'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}\n",
    "\n",
    "    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),\n",
    "                      'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}\n",
    "\n",
    "    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),\n",
    "                      'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}\n",
    "\n",
    "    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),\n",
    "                    'biases':tf.Variable(tf.random_normal([n_classes]))}\n",
    "    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])\n",
    "    l1 = tf.nn.relu(l1)\n",
    "    \n",
    "    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']), hidden_2_layer['biases'])\n",
    "    l2 = tf.nn.relu(l2)\n",
    "\n",
    "    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']), hidden_3_layer['biases'])\n",
    "    l3 = tf.nn.relu(l3)\n",
    "\n",
    "    output = tf.matmul(l3,output_layer['weights']) + output_layer['biases']\n",
    "\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train_neural_network(x):\n",
    "    prediction = neural_network_model(x)\n",
    "    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
    "    hm_epochs = 10\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        for epoch in range(hm_epochs):\n",
    "            epoch_loss = 0\n",
    "            for _ in range(int(mnist.train.num_examples/batch_size)):\n",
    "                epoch_x, epoch_y = mnist.train.next_batch(batch_size)\n",
    "                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})\n",
    "                epoch_loss += c\n",
    "\n",
    "            print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)\n",
    "        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))\n",
    "        print('Accuracy:',accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-14-3a0d1ffb9a1b>:4: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
      "\n",
      "Epoch 0 completed out of 10 loss: 1643313.7794647217\n",
      "Epoch 1 completed out of 10 loss: 396503.5139026642\n",
      "Epoch 2 completed out of 10 loss: 220492.84287905693\n",
      "Epoch 3 completed out of 10 loss: 135167.35769414902\n",
      "Epoch 4 completed out of 10 loss: 79832.484457843\n",
      "Epoch 5 completed out of 10 loss: 53347.53369822372\n",
      "Epoch 6 completed out of 10 loss: 34356.25161682349\n",
      "Epoch 7 completed out of 10 loss: 25537.962947773747\n",
      "Epoch 8 completed out of 10 loss: 19754.98678703824\n",
      "Epoch 9 completed out of 10 loss: 19155.250205606222\n",
      "Accuracy: 0.9512\n"
     ]
    }
   ],
   "source": [
    "train_neural_network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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