Lesson 3-2 MNIST Digit Recognition (Simplified).ipynb

{
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   "metadata": {},
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   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "Iter0, Testing Accuracy: 0.4027\n",
      "Iter1, Testing Accuracy: 0.5035\n",
      "Iter2, Testing Accuracy: 0.5738\n",
      "Iter3, Testing Accuracy: 0.615\n",
      "Iter4, Testing Accuracy: 0.6338\n",
      "Iter5, Testing Accuracy: 0.6469\n",
      "Iter6, Testing Accuracy: 0.6565\n",
      "Iter7, Testing Accuracy: 0.6667\n",
      "Iter8, Testing Accuracy: 0.6729\n",
      "Iter9, Testing Accuracy: 0.6773\n",
      "Iter10, Testing Accuracy: 0.6827\n",
      "Iter11, Testing Accuracy: 0.6865\n",
      "Iter12, Testing Accuracy: 0.6899\n",
      "Iter13, Testing Accuracy: 0.6932\n",
      "Iter14, Testing Accuracy: 0.7092\n",
      "Iter15, Testing Accuracy: 0.7299\n",
      "Iter16, Testing Accuracy: 0.7444\n",
      "Iter17, Testing Accuracy: 0.7535\n",
      "Iter18, Testing Accuracy: 0.7605\n",
      "Iter19, Testing Accuracy: 0.7676\n",
      "Iter20, Testing Accuracy: 0.7711\n",
      "Iter21, Testing Accuracy: 0.7745\n",
      "Iter22, Testing Accuracy: 0.7771\n",
      "Iter23, Testing Accuracy: 0.7802\n",
      "Iter24, Testing Accuracy: 0.7816\n",
      "Iter25, Testing Accuracy: 0.7848\n",
      "Iter26, Testing Accuracy: 0.7869\n",
      "Iter27, Testing Accuracy: 0.7884\n",
      "Iter28, Testing Accuracy: 0.7895\n",
      "Iter29, Testing Accuracy: 0.7912\n",
      "Iter30, Testing Accuracy: 0.7925\n",
      "Iter31, Testing Accuracy: 0.7929\n",
      "Iter32, Testing Accuracy: 0.7955\n",
      "Iter33, Testing Accuracy: 0.7962\n",
      "Iter34, Testing Accuracy: 0.7969\n",
      "Iter35, Testing Accuracy: 0.7979\n",
      "Iter36, Testing Accuracy: 0.7983\n",
      "Iter37, Testing Accuracy: 0.7996\n",
      "Iter38, Testing Accuracy: 0.8009\n",
      "Iter39, Testing Accuracy: 0.8017\n",
      "Iter40, Testing Accuracy: 0.8019\n",
      "Iter41, Testing Accuracy: 0.8027\n",
      "Iter42, Testing Accuracy: 0.8031\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py\u001b[0m in \u001b[0;36mnext_batch\u001b[0;34m(self, batch_size, fake_data, shuffle)\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0mperm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_examples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m         \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m       \u001b[0;31m# Start next epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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   "source": [
    "#load dataset\n",
    "mnist = input_data.read_data_sets(\"MNIST_data\",one_hot=True)\n",
    "\n",
    "#define batch size\n",
    "batch_size = 100\n",
    "#calculate number of batches\n",
    "n_batch = mnist.train.num_examples // batch_size\n",
    "\n",
    "#define placeholders\n",
    "x = tf.placeholder(tf.float32, [None,784])\n",
    "y = tf.placeholder(tf.float32, [None,10])\n",
    "\n",
    "#create simple NeuroNet\n",
    "W = tf.Variable(tf.zeros([784,10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "prediction = tf.nn.softmax(tf.matmul(x,W)+b)\n",
    "\n",
    "#quadratic cost function\n",
    "loss = tf.reduce_mean(tf.square(y-prediction))\n",
    "#train with gradient descent\n",
    "train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)\n",
    "\n",
    "#initialize variables\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "#find accuracy of trained model\n",
    "correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))   #convert a list of booleans into a single boolean of accuracy\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for epoch in range(21):\n",
    "        for batch in range(n_batch):\n",
    "            batch_xs,batch_ys = mnist.train.next_batch(batch_size)\n",
    "            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})\n",
    "        \n",
    "        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})\n",
    "        print(\"Iter\" + str(epoch) + \", Testing Accuracy: \" + str(acc))\n",
    "        \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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