deepLearning_scratch_mb042119.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T15:09:06.557867Z",
     "start_time": "2019-04-21T15:09:03.939593Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T15:09:16.657866Z",
     "start_time": "2019-04-21T15:09:16.629469Z"
    }
   },
   "outputs": [],
   "source": [
    "x1 = tf.constant(5)\n",
    "x2 = tf.constant(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T15:11:46.610135Z",
     "start_time": "2019-04-21T15:11:46.604531Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Mul_2:0\", shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "result = tf.multiply(x1, x2)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T15:12:30.195413Z",
     "start_time": "2019-04-21T15:12:30.153780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "print(sess.run(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T15:13:36.890209Z",
     "start_time": "2019-04-21T15:13:36.881998Z"
    }
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T15:17:25.223265Z",
     "start_time": "2019-04-21T15:17:25.216315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    output = sess.run(result)\n",
    "    print(sess.run(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T15:18:49.099213Z",
     "start_time": "2019-04-21T15:18:49.095022Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Neural Net\n",
    "\n",
    "- A neural net is 1 (sometimes 2) layers\n",
    "\n",
    "- A Deep NN/Learning is 2+ layers\n",
    "\n",
    "### Simple Architecture\n",
    "\n",
    "**Feed Forward**\n",
    "\n",
    "1) *Input >> Weight >> Hidden Layer 1 (Activation Function >>\n",
    "         Weigths >> Hidden Layer 2 (Activation Function)*\n",
    "         \n",
    "2) **Cost/Loss Function (i.e. Cross Entropy)** - compare output to intended output\n",
    "\n",
    "3) **Optimization Function (Optimizer)** - minimize the loss/cost (i.e. AdamOptimizer... SGD, AdaGrad)\n",
    "\n",
    "4) **Backpropagation**\n",
    "\n",
    "5) **Epoch** = feedfoward + backprop\n",
    "\n",
    "\n",
    "**One_Hot** = means target is \"1\" and the remaining outcomes are \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T16:44:42.169501Z",
     "start_time": "2019-04-21T16:44:41.510427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/data/train-images-idx3-ubyte.gz\n",
      "Extracting ./data/data/train-labels-idx1-ubyte.gz\n",
      "Extracting ./data/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./data/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "mnist = input_data.read_data_sets(\"./data/data\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T16:44:42.816307Z",
     "start_time": "2019-04-21T16:44:42.812915Z"
    }
   },
   "outputs": [],
   "source": [
    "n_nodes_hl1 = 500\n",
    "n_nodes_hl2 = 500\n",
    "n_nodes_hl3 = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T16:44:43.289794Z",
     "start_time": "2019-04-21T16:44:43.286871Z"
    }
   },
   "outputs": [],
   "source": [
    "n_classes = 10\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T16:44:43.628691Z",
     "start_time": "2019-04-21T16:44:43.623506Z"
    }
   },
   "outputs": [],
   "source": [
    "# height x width\n",
    "x = tf.placeholder('float', [None, 784])\n",
    "y = tf.placeholder('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Input Data * Weights) + Baises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T16:44:44.320821Z",
     "start_time": "2019-04-21T16:44:44.311339Z"
    }
   },
   "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",
    "\n",
    "\n",
    "    l1 = tf.add(tf.multiply(data,hidden_1_layer['weights']), hidden_1_layer['biases'])\n",
    "    l1 = tf.nn.relu(l1)\n",
    "\n",
    "    l2 = tf.add(tf.multiply(l1,hidden_2_layer['weights']), hidden_2_layer['biases'])\n",
    "    l2 = tf.nn.relu(l2)\n",
    "\n",
    "    l3 = tf.add(tf.multiply(l2,hidden_3_layer['weights']), hidden_3_layer['biases'])\n",
    "    l3 = tf.nn.relu(l3)\n",
    "\n",
    "    output = tf.multiply(l3,output_layer['weights']) + output_layer['biases']\n",
    "\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T16:44:44.658750Z",
     "start_time": "2019-04-21T16:44:44.650260Z"
    }
   },
   "outputs": [],
   "source": [
    "def train_neural_network(x):\n",
    "    prediction = neural_network_model(x)\n",
    "    # OLD VERSION:\n",
    "    #cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )\n",
    "    # NEW:\n",
    "    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
    "    \n",
    "    hm_epochs = 10\n",
    "    with tf.Session() as sess:\n",
    "        # OLD:\n",
    "        #sess.run(tf.initialize_all_variables())\n",
    "        # NEW:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "\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",
    "\n",
    "        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n",
    "\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": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-21T16:44:45.444124Z",
     "start_time": "2019-04-21T16:44:45.324737Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Dimensions must be equal, but are 784 and 500 for 'Mul_10' (op: 'Mul') with input shapes: [?,784], [784,500].",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1658\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1659\u001b[0;31m     \u001b[0mc_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_FinishOperation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_desc\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   1660\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\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: Dimensions must be equal, but are 784 and 500 for 'Mul_10' (op: 'Mul') with input shapes: [?,784], [784,500].",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-e776a1e8e4f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_neural_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-69-ab9e0ce59b48>\u001b[0m in \u001b[0;36mtrain_neural_network\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_neural_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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----> 2\u001b[0;31m     \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneural_network_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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      3\u001b[0m     \u001b[0;31m# OLD VERSION:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m#cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# NEW:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-68-33c1a2a0c223>\u001b[0m in \u001b[0;36mneural_network_model\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     \u001b[0ml1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhidden_1_layer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weights'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_1_layer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'biases'\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[0m\u001b[1;32m     16\u001b[0m     \u001b[0ml1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\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[0mkwargs\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    181\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\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[1;32m    182\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36mmultiply\u001b[0;34m(x, y, name)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_dispatch_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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--> 248\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgen_math_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mmul\u001b[0;34m(x, y, name)\u001b[0m\n\u001b[1;32m   5858\u001b[0m   \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5859\u001b[0m   _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[0;32m-> 5860\u001b[0;31m         \"Mul\", x=x, y=y, name=name)\n\u001b[0m\u001b[1;32m   5861\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\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[1;32m   5862\u001b[0m   \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    786\u001b[0m         op = g.create_op(op_type_name, inputs, output_types, name=scope,\n\u001b[1;32m    787\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 788\u001b[0;31m                          op_def=op_def)\n\u001b[0m\u001b[1;32m    789\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_stateful\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m                 \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\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[1;32m    506\u001b[0m                 instructions)\n\u001b[0;32m--> 507\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mkwargs\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    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m     doc = _add_deprecated_arg_notice_to_docstring(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   3298\u001b[0m           \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3299\u001b[0m           \u001b[0moriginal_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_original_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3300\u001b[0;31m           op_def=op_def)\n\u001b[0m\u001b[1;32m   3301\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_op_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3302\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)\u001b[0m\n\u001b[1;32m   1821\u001b[0m           op_def, inputs, node_def.attr)\n\u001b[1;32m   1822\u001b[0m       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,\n\u001b[0;32m-> 1823\u001b[0;31m                                 control_input_ops)\n\u001b[0m\u001b[1;32m   1824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1825\u001b[0m     \u001b[0;31m# Initialize self._outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1660\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1661\u001b[0m     \u001b[0;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1662\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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[0m\u001b[1;32m   1663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1664\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mc_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Dimensions must be equal, but are 784 and 500 for 'Mul_10' (op: 'Mul') with input shapes: [?,784], [784,500]."
     ]
    }
   ],
   "source": [
    "train_neural_network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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