CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-7-6504edc5681f>:63: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "Epoch 1 completed out of 3 loss: 0 success rate: 0.0\n",
      "Epoch 2 completed out of 3 loss: 0 success rate: 0.0\n",
      "Epoch 3 completed out of 3 loss: 0 success rate: 0.0\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot feed value of shape (100, 20, 50, 50) for Tensor 'Placeholder_18:0', which has shape '(?, 784)'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-6504edc5681f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     84\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Accuracy:'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m \u001b[0mtrain_neural_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-7-6504edc5681f>\u001b[0m in \u001b[0;36mtrain_neural_network\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[0maccuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorrect\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'float'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Accuracy:'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[0mtrain_neural_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36meval\u001b[1;34m(self, feed_dict, session)\u001b[0m\n\u001b[0;32m    567\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    568\u001b[0m     \"\"\"\n\u001b[1;32m--> 569\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[1;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[0;32m   3739\u001b[0m                        \u001b[1;34m\"the tensor's graph is different from the session's \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3740\u001b[0m                        \"graph.\")\n\u001b[1;32m-> 3741\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3742\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3743\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    776\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 778\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    779\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    959\u001b[0m                 \u001b[1;34m'Cannot feed value of shape %r for Tensor %r, '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    960\u001b[0m                 \u001b[1;34m'which has shape %r'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 961\u001b[1;33m                 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))\n\u001b[0m\u001b[0;32m    962\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    963\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Tensor %s may not be fed.'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot feed value of shape (100, 20, 50, 50) for Tensor 'Placeholder_18:0', which has shape '(?, 784)'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "IMG_SIZE_PX = 50\n",
    "SLICE_COUNT = 20\n",
    "\n",
    "\n",
    "n_classes = 2 \n",
    "\n",
    "x = tf.placeholder('float', [None, 784])\n",
    "y = tf.placeholder('float')\n",
    "\n",
    "keep_rate = 0.8\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "def conv3d(x, W):\n",
    "    return tf.nn.conv3d(x, W, strides=[1,1,1,1,1], padding='SAME')\n",
    "\n",
    "def maxpool3d(x):\n",
    "    #                        size of window         movement of window\n",
    "    return tf.nn.max_pool3d(x, ksize=[1,2,2,2,1], strides=[1,2,2,2,1], padding='SAME')\n",
    "\n",
    "\n",
    "\n",
    "def convolutional_neural_network(x):\n",
    "    weights = {'W_conv1':tf.Variable(tf.random_normal([3,3,3,1,32])),\n",
    "               'W_conv2':tf.Variable(tf.random_normal([3,3,3,32,64])),\n",
    "               'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])),\n",
    "               'out':tf.Variable(tf.random_normal([1024, n_classes]))}\n",
    "\n",
    "    biases = {'b_conv1':tf.Variable(tf.random_normal([32])),\n",
    "               'b_conv2':tf.Variable(tf.random_normal([64])),\n",
    "               'b_fc':tf.Variable(tf.random_normal([1024])),\n",
    "               'out':tf.Variable(tf.random_normal([n_classes]))}\n",
    "\n",
    "    x = tf.reshape(x, shape=[-1, IMG_SIZE_PX, IMG_SIZE_PX, SLICE_COUNT, 1])\n",
    "\n",
    "    conv1 = tf.nn.relu(conv3d(x, weights['W_conv1']) + biases['b_conv1'])\n",
    "    conv1 = maxpool3d(conv1)\n",
    "    \n",
    "    conv2 = tf.nn.relu(conv3d(conv1, weights['W_conv2']) + biases['b_conv2'])\n",
    "    conv2 = maxpool3d(conv2)\n",
    "\n",
    "    fc = tf.reshape(conv2,[-1, 7*7*64])\n",
    "    fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])\n",
    "    fc = tf.nn.dropout(fc, keep_rate)\n",
    "\n",
    "    output = tf.matmul(fc, weights['out'])+biases['out']\n",
    "\n",
    "    return output\n",
    "\n",
    "def train_neural_network(x):\n",
    "    much_Data = np.load('muchdata-{}-{}-{}.npy'.format(IMG_SIZE_PX,IMG_SIZE_PX, SLICE_COUNT))\n",
    "    train_data = much_Data[:-100]\n",
    "    validation_data = much_Data[-100:]\n",
    "                      \n",
    "    prediction = convolutional_neural_network(x)\n",
    "    #error function\n",
    "    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y) )\n",
    "    #gradient decend\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
    "    \n",
    "    hm_epochs = 3\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.initialize_all_variables())\n",
    "        success_total = 0\n",
    "        attempts_total = 0\n",
    "        for epoch in range(hm_epochs):\n",
    "            epoch_loss = 0\n",
    "            for data in train_data:\n",
    "                attempts_total+=1\n",
    "                try:\n",
    "                    X = data[0] #data\n",
    "                    Y = data[1] #result\n",
    "                    _, c = sess.run([optimizer, cost], feed_dict={x: X, y: Y})\n",
    "                    epoch_loss += c\n",
    "                    success_total+=1\n",
    "                except Exception as e:\n",
    "                    pass\n",
    "                \n",
    "            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss,'success rate:',success_total/attempts_total)\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:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))\n",
    "\n",
    "train_neural_network(x)"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-1-71fc4ff25ce8>:67: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
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      "Epoch 3 completed out of 15 loss: 7825582538.06\n",
      "Accuracy: 0.57\n",
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      "Epoch 6 completed out of 15 loss: 2153694054.81\n",
      "Accuracy: 0.51\n",
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      "Epoch 7 completed out of 15 loss: 1274002820.25\n",
      "Accuracy: 0.59\n",
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      "Epoch 8 completed out of 15 loss: 813193488.066\n",
      "Accuracy: 0.6\n",
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      "Epoch 9 completed out of 15 loss: 466148642.692\n",
      "Accuracy: 0.66\n",
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      "Epoch 10 completed out of 15 loss: 262123537.552\n",
      "Accuracy: 0.71\n",
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      "Epoch 11 completed out of 15 loss: 250274904.474\n",
      "Accuracy: 0.68\n",
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      "Epoch 12 completed out of 15 loss: 138339594.228\n",
      "Accuracy: 0.57\n",
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      "Epoch 13 completed out of 15 loss: 65612251.8804\n",
      "Accuracy: 0.61\n",
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      "Epoch 14 completed out of 15 loss: 66366115.5118\n",
      "Accuracy: 0.67\n",
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      "Epoch 15 completed out of 15 loss: 50386532.5134\n",
      "Accuracy: 0.54\n",
      "Done. Finishing accuracy:\n",
      "Accuracy: 0.56\n",
      "fitment percent: 0.9992289899768697\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "IMG_SIZE_PX = 50\n",
    "SLICE_COUNT = 20\n",
    "\n",
    "n_classes = 2\n",
    "batch_size = 10\n",
    "\n",
    "x = tf.placeholder('float')\n",
    "y = tf.placeholder('float')\n",
    "\n",
    "keep_rate = 0.8\n",
    "\n",
    "def conv3d(x, W):\n",
    "    return tf.nn.conv3d(x, W, strides=[1,1,1,1,1], padding='SAME')\n",
    "\n",
    "def maxpool3d(x):\n",
    "    #                        size of window         movement of window as you slide about\n",
    "    return tf.nn.max_pool3d(x, ksize=[1,2,2,2,1], strides=[1,2,2,2,1], padding='SAME')\n",
    "\n",
    "\n",
    "def convolutional_neural_network(x):\n",
    "    #                # 5 x 5 x 5 patches, 1 channel, 32 features to compute.\n",
    "    weights = {'W_conv1':tf.Variable(tf.random_normal([3,3,3,1,32])),\n",
    "               #       5 x 5 x 5 patches, 32 channels, 64 features to compute.\n",
    "               'W_conv2':tf.Variable(tf.random_normal([3,3,3,32,64])),\n",
    "               #                                  64 features\n",
    "               'W_fc':tf.Variable(tf.random_normal([54080,1024])),\n",
    "               'out':tf.Variable(tf.random_normal([1024, n_classes]))}\n",
    "\n",
    "    biases = {'b_conv1':tf.Variable(tf.random_normal([32])),\n",
    "               'b_conv2':tf.Variable(tf.random_normal([64])),\n",
    "               'b_fc':tf.Variable(tf.random_normal([1024])),\n",
    "               'out':tf.Variable(tf.random_normal([n_classes]))}\n",
    "\n",
    "    #                            image X      image Y        image Z\n",
    "    x = tf.reshape(x, shape=[-1, IMG_SIZE_PX, IMG_SIZE_PX, SLICE_COUNT, 1])\n",
    "\n",
    "    conv1 = tf.nn.relu(conv3d(x, weights['W_conv1']) + biases['b_conv1'])\n",
    "    conv1 = maxpool3d(conv1)\n",
    "\n",
    "\n",
    "    conv2 = tf.nn.relu(conv3d(conv1, weights['W_conv2']) + biases['b_conv2'])\n",
    "    conv2 = maxpool3d(conv2)\n",
    "\n",
    "    fc = tf.reshape(conv2,[-1, 54080])\n",
    "    fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])\n",
    "    fc = tf.nn.dropout(fc, keep_rate)\n",
    "\n",
    "    output = tf.matmul(fc, weights['out'])+biases['out']\n",
    "\n",
    "    return output\n",
    "\n",
    "much_data = np.load('muchdata-50-50-20.npy')\n",
    "# If you are working with the basic sample data, use maybe 2 instead of 100 here... you don't have enough data to really do this\n",
    "train_data = much_data[:-100]\n",
    "validation_data = much_data[-100:]\n",
    "\n",
    "\n",
    "def train_neural_network(x):\n",
    "    prediction = convolutional_neural_network(x)\n",
    "    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y) )\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(cost)\n",
    "    \n",
    "    hm_epochs = 15\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.initialize_all_variables())\n",
    "        \n",
    "        successful_runs = 0\n",
    "        total_runs = 0\n",
    "        \n",
    "        for epoch in range(hm_epochs):\n",
    "            epoch_loss = 0\n",
    "            for data in train_data:\n",
    "                total_runs += 1\n",
    "                print(total_runs)\n",
    "                try:\n",
    "                    X = data[0]# Actual Reduced image form much date\n",
    "                    Y = data[1]# [0,1] or [1,0] cancer or not cancer form much data\n",
    "                    _, c = sess.run([optimizer, cost], feed_dict={x: X, y: Y})\n",
    "                    epoch_loss += c\n",
    "                    successful_runs += 1\n",
    "                except Exception as e:\n",
    "                    # I am passing for the sake of notebook space, but we are getting 1 shaping issue from one \n",
    "                    # input tensor. Not sure why, will have to look into it. Guessing it's\n",
    "                    # one of the depths that doesn't come to 20.\n",
    "                    pass\n",
    "                    #print(str(e))\n",
    "            \n",
    "            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)\n",
    "\n",
    "            correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n",
    "            accuracy = tf.reduce_mean(tf.cast(correct, 'float'))\n",
    "\n",
    "            print('Accuracy:',accuracy.eval({x:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))\n",
    "            \n",
    "        print('Done. Finishing accuracy:')\n",
    "        print('Accuracy:',accuracy.eval({x:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))\n",
    "        \n",
    "        print('fitment percent:',successful_runs/total_runs)\n",
    "\n",
    "# Run this locally:\n",
    "# train_neural_network(x)\n",
    "train_neural_network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "much_data = np.load('muchdata-50-50-20.npy')\n",
    "# If you are working with the basic sample data, use maybe 2 instead of 100 here... you don't have enough data to really do this\n",
    "train_data = much_data[:-100]\n",
    "validation_data = much_data[-100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-26-885e27554746>:36: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "Shape [2924646400,1024] is too large (more than 1099511627776 entries)\n\t [[Node: Variable_4 = VariableV2[container=\"\", dtype=DT_FLOAT, shape=[2924646400,1024], shared_name=\"\", _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'Variable_4', defined at:\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2683, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2793, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2847, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-17-8ef4fded17dd>\", line 142, in <module>\n    train_neural_network(x)\n  File \"<ipython-input-17-8ef4fded17dd>\", line 99, in train_neural_network\n    prediction = convolutional_neural_network(x)\n  File \"<ipython-input-17-8ef4fded17dd>\", line 33, in convolutional_neural_network\n    'W_fc':tf.Variable(tf.random_normal([2924646400,1024])),\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\variables.py\", line 197, in __init__\n    expected_shape=expected_shape)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\variables.py\", line 294, in _init_from_args\n    name=name)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\state_ops.py\", line 128, in variable_op_v2\n    shared_name=shared_name)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\gen_state_ops.py\", line 708, in _variable_v2\n    name=name)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 768, in apply_op\n    op_def=op_def)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2336, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1228, in __init__\n    self._traceback = _extract_stack()\n\nInvalidArgumentError (see above for traceback): Shape [2924646400,1024] is too large (more than 1099511627776 entries)\n\t [[Node: Variable_4 = VariableV2[container=\"\", dtype=DT_FLOAT, shape=[2924646400,1024], shared_name=\"\", _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1038\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1039\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1040\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1021\u001b[1;33m                                  status, run_metadata)\n\u001b[0m\u001b[0;32m   1022\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m                 \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[1;34m()\u001b[0m\n\u001b[0;32m    465\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[0;32m    467\u001b[0m   \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m: Shape [2924646400,1024] is too large (more than 1099511627776 entries)\n\t [[Node: Variable_4 = VariableV2[container=\"\", dtype=DT_FLOAT, shape=[2924646400,1024], shared_name=\"\", _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-885e27554746>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m     \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitialize_all_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvolutional_neural_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    776\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 778\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    779\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    980\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m--> 982\u001b[1;33m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[0;32m    983\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1030\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[1;32m-> 1032\u001b[1;33m                            target_list, options, run_metadata)\n\u001b[0m\u001b[0;32m   1033\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1050\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1051\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1052\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1054\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m: Shape [2924646400,1024] is too large (more than 1099511627776 entries)\n\t [[Node: Variable_4 = VariableV2[container=\"\", dtype=DT_FLOAT, shape=[2924646400,1024], shared_name=\"\", _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'Variable_4', defined at:\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2683, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2793, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2847, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-17-8ef4fded17dd>\", line 142, in <module>\n    train_neural_network(x)\n  File \"<ipython-input-17-8ef4fded17dd>\", line 99, in train_neural_network\n    prediction = convolutional_neural_network(x)\n  File \"<ipython-input-17-8ef4fded17dd>\", line 33, in convolutional_neural_network\n    'W_fc':tf.Variable(tf.random_normal([2924646400,1024])),\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\variables.py\", line 197, in __init__\n    expected_shape=expected_shape)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\variables.py\", line 294, in _init_from_args\n    name=name)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\state_ops.py\", line 128, in variable_op_v2\n    shared_name=shared_name)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\gen_state_ops.py\", line 708, in _variable_v2\n    name=name)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 768, in apply_op\n    op_def=op_def)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2336, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1228, in __init__\n    self._traceback = _extract_stack()\n\nInvalidArgumentError (see above for traceback): Shape [2924646400,1024] is too large (more than 1099511627776 entries)\n\t [[Node: Variable_4 = VariableV2[container=\"\", dtype=DT_FLOAT, shape=[2924646400,1024], shared_name=\"\", _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "IMG_SIZE_PX = 50\n",
    "SLICE_COUNT = 20\n",
    "\n",
    "n_classes = 2\n",
    "batch_size = 10\n",
    "\n",
    "\n",
    "def convolutional_neural_network(x):\n",
    "    #                # 5 x 5 x 5 patches, 1 channel, 32 features to compute.\n",
    "    weights = {'W_conv1':tf.Variable(tf.random_normal([3,3,3,1,32])),\n",
    "               #       5 x 5 x 5 patches, 32 channels, 64 features to compute.\n",
    "               'W_conv2':tf.Variable(tf.random_normal([3,3,3,32,64])),\n",
    "               #                                  64 features\n",
    "               'W_fc':tf.Variable(tf.random_normal([54080,1024])),\n",
    "               'out':tf.Variable(tf.random_normal([1024, n_classes]))}\n",
    "\n",
    "    biases = {'b_conv1':tf.Variable(tf.random_normal([32])),\n",
    "               'b_conv2':tf.Variable(tf.random_normal([64])),\n",
    "               'b_fc':tf.Variable(tf.random_normal([1024])),\n",
    "               'out':tf.Variable(tf.random_normal([n_classes]))}\n",
    "\n",
    "    #                            image X      image Y        image Z\n",
    "    x = tf.reshape(x, shape=[-1, IMG_SIZE_PX, IMG_SIZE_PX, SLICE_COUNT, 1])\n",
    "\n",
    "    return x\n",
    "\n",
    "much_data = np.load('lessdata-50-50-20.npy')\n",
    "# If you are working with the basic sample data, use maybe 2 instead of 100 here... you don't have enough data to really do this\n",
    "train_data = much_data[:-2]\n",
    "validation_data = much_data[-2:]\n",
    " \n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "    print(convolutional_neural_network(train_data[0]))\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"random_normal_29:0\", shape=(3, 3, 3, 32, 64), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    print(tf.random_normal([3,3,3,32,64]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ array([[[   23.        ,    25.11111111,    26.        , ...,\n",
      "            25.33333333,    24.66666667,    21.33333333],\n",
      "        [   25.11111111,    22.88888889,    20.22222222, ...,\n",
      "            20.44444444,    24.11111111,    21.22222222],\n",
      "        [   17.55555556,    17.22222222,    25.66666667, ...,\n",
      "            24.55555556,    22.88888889,    28.11111111],\n",
      "        ..., \n",
      "        [  115.11111111,   119.        ,    92.33333333, ...,\n",
      "          1008.88888889,   142.11111111,   111.11111111],\n",
      "        [  107.88888889,   115.77777778,   124.66666667, ...,\n",
      "           118.44444444,   125.        ,   121.44444444],\n",
      "        [  116.77777778,   120.        ,   121.33333333, ...,\n",
      "           118.        ,   103.        ,   112.77777778]],\n",
      "\n",
      "       [[   24.22222222,    25.88888889,    17.33333333, ...,\n",
      "            26.22222222,    22.22222222,    25.55555556],\n",
      "        [   22.44444444,    29.77777778,    19.        , ...,\n",
      "            22.        ,    27.        ,    21.33333333],\n",
      "        [   20.22222222,    22.11111111,    20.11111111, ...,\n",
      "            21.44444444,    27.55555556,    24.33333333],\n",
      "        ..., \n",
      "        [  117.55555556,   121.22222222,    95.77777778, ...,\n",
      "           904.        ,   114.66666667,   114.        ],\n",
      "        [  115.11111111,   109.55555556,   115.88888889, ...,\n",
      "           118.11111111,   116.77777778,   118.55555556],\n",
      "        [  112.66666667,   113.55555556,   114.44444444, ...,\n",
      "           119.44444444,   114.77777778,   120.        ]],\n",
      "\n",
      "       [[   20.77777778,    23.22222222,    17.22222222, ...,\n",
      "            29.22222222,    21.66666667,    26.        ],\n",
      "        [   21.88888889,    20.22222222,    17.77777778, ...,\n",
      "            24.88888889,    27.88888889,    25.77777778],\n",
      "        [   23.22222222,    24.77777778,    30.77777778, ...,\n",
      "            28.        ,    24.77777778,    29.88888889],\n",
      "        ..., \n",
      "        [  110.77777778,   120.44444444,   108.55555556, ...,\n",
      "           821.55555556,   106.66666667,   102.33333333],\n",
      "        [  120.22222222,   121.33333333,   122.44444444, ...,\n",
      "           120.88888889,   120.88888889,   113.        ],\n",
      "        [  111.        ,   117.        ,   110.55555556, ...,\n",
      "           117.11111111,   106.88888889,   124.55555556]],\n",
      "\n",
      "       ..., \n",
      "       [[   17.33333333,    15.        ,    16.88888889, ...,\n",
      "            16.88888889,    18.        ,    19.33333333],\n",
      "        [    9.77777778,    12.44444444,    16.77777778, ...,\n",
      "            21.        ,    19.55555556,    22.88888889],\n",
      "        [   20.11111111,    21.11111111,    23.44444444, ...,\n",
      "            18.11111111,    16.44444444,    19.33333333],\n",
      "        ..., \n",
      "        [  107.55555556,   130.        ,   113.66666667, ...,\n",
      "           133.55555556,   105.77777778,   113.11111111],\n",
      "        [   92.44444444,   102.55555556,   114.11111111, ...,\n",
      "           106.22222222,   116.11111111,   106.55555556],\n",
      "        [  123.44444444,   110.77777778,   119.44444444, ...,\n",
      "           116.55555556,   106.88888889,   103.33333333]],\n",
      "\n",
      "       [[   28.66666667,    21.        ,    21.66666667, ...,\n",
      "            29.        ,    21.22222222,    26.44444444],\n",
      "        [   19.        ,    18.55555556,    19.77777778, ...,\n",
      "            29.33333333,    23.44444444,    24.44444444],\n",
      "        [   19.22222222,    29.33333333,    23.55555556, ...,\n",
      "            32.66666667,    21.11111111,    14.44444444],\n",
      "        ..., \n",
      "        [  114.22222222,   115.55555556,   112.77777778, ...,\n",
      "           121.        ,   102.44444444,   108.66666667],\n",
      "        [  114.88888889,   106.        ,   113.66666667, ...,\n",
      "           117.11111111,   123.44444444,   117.        ],\n",
      "        [  121.11111111,   117.77777778,   112.22222222, ...,\n",
      "           103.44444444,   100.        ,   111.66666667]],\n",
      "\n",
      "       [[   40.5       ,    26.        ,    19.        , ...,\n",
      "            27.        ,    14.5       ,    23.        ],\n",
      "        [   18.5       ,    17.5       ,    18.        , ...,\n",
      "            31.        ,    21.5       ,    23.        ],\n",
      "        [   14.5       ,    39.5       ,     3.5       , ...,\n",
      "             5.5       ,    20.5       ,    42.        ],\n",
      "        ..., \n",
      "        [  112.5       ,   109.5       ,   115.        , ...,\n",
      "           128.        ,    96.        ,   114.        ],\n",
      "        [   89.5       ,   119.5       ,    99.        , ...,\n",
      "           126.        ,   127.5       ,   108.        ],\n",
      "        [  125.        ,   121.5       ,   114.        , ...,\n",
      "           117.        ,   106.5       ,   112.5       ]]])\n",
      " array([1, 0]) 'eeb1659b92b00e20bd8b6e7eef6b5c9d']\n"
     ]
    }
   ],
   "source": [
    "print(validation_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    abc=sess.run(tf.reshape(validation_data[0][0], shape=[-1, 50, 50, 20, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[[   23.        ]\n",
      "    [   25.11111111]\n",
      "    [   26.        ]\n",
      "    ..., \n",
      "    [   13.11111111]\n",
      "    [   29.22222222]\n",
      "    [   22.        ]]\n",
      "\n",
      "   [[   28.        ]\n",
      "    [   24.33333333]\n",
      "    [   34.88888889]\n",
      "    ..., \n",
      "    [   27.88888889]\n",
      "    [   28.55555556]\n",
      "    [   28.        ]]\n",
      "\n",
      "   [[   27.77777778]\n",
      "    [   17.88888889]\n",
      "    [   22.55555556]\n",
      "    ..., \n",
      "    [   30.55555556]\n",
      "    [   26.33333333]\n",
      "    [   20.66666667]]\n",
      "\n",
      "   ..., \n",
      "   [[  979.22222222]\n",
      "    [ 1011.55555556]\n",
      "    [  965.        ]\n",
      "    ..., \n",
      "    [ 1086.11111111]\n",
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      "\n",
      "   [[ 1071.22222222]\n",
      "    [ 1083.33333333]\n",
      "    [ 1084.44444444]\n",
      "    ..., \n",
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      "\n",
      "   [[ 1041.22222222]\n",
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      "    ..., \n",
      "    [   23.22222222]\n",
      "    [   34.77777778]\n",
      "    [   20.77777778]]]\n",
      "\n",
      "\n",
      "  [[[   23.33333333]\n",
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      "    ..., \n",
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      "    [ 1086.77777778]]\n",
      "\n",
      "   [[ 1093.55555556]\n",
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      "    [ 1078.33333333]\n",
      "    ..., \n",
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      "    [ 1055.55555556]]\n",
      "\n",
      "   [[ 1020.88888889]\n",
      "    [ 1010.11111111]\n",
      "    [  957.33333333]\n",
      "    ..., \n",
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      "\n",
      "   ..., \n",
      "   [[  910.77777778]\n",
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      "\n",
      "   [[  944.55555556]\n",
      "    [ 1011.55555556]\n",
      "    [ 1056.44444444]\n",
      "    ..., \n",
      "    [ 1048.55555556]\n",
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      "\n",
      "   [[ 1066.77777778]\n",
      "    [ 1078.44444444]\n",
      "    [ 1052.88888889]\n",
      "    ..., \n",
      "    [   30.33333333]\n",
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      "    [   39.22222222]]]\n",
      "\n",
      "\n",
      "  [[[  166.        ]\n",
      "    [   64.44444444]\n",
      "    [   25.55555556]\n",
      "    ..., \n",
      "    [ 1050.66666667]\n",
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      "    [ 1056.11111111]]\n",
      "\n",
      "   [[ 1066.77777778]\n",
      "    [ 1081.22222222]\n",
      "    [ 1053.55555556]\n",
      "    ..., \n",
      "    [  937.66666667]\n",
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      "    [  905.55555556]]\n",
      "\n",
      "   [[  911.        ]\n",
      "    [  955.11111111]\n",
      "    [   24.22222222]\n",
      "    ..., \n",
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      "    [   84.55555556]\n",
      "    [  608.88888889]]\n",
      "\n",
      "   ..., \n",
      "   [[   22.44444444]\n",
      "    [   26.33333333]\n",
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      "    ..., \n",
      "    [   19.33333333]\n",
      "    [   91.22222222]\n",
      "    [   19.        ]]\n",
      "\n",
      "   [[  174.55555556]\n",
      "    [  443.77777778]\n",
      "    [  689.        ]\n",
      "    ..., \n",
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      "    [  952.22222222]]\n",
      "\n",
      "   [[  953.        ]\n",
      "    [  937.77777778]\n",
      "    [  937.33333333]\n",
      "    ..., \n",
      "    [   18.22222222]\n",
      "    [   25.44444444]\n",
      "    [   29.66666667]]]\n",
      "\n",
      "\n",
      "  ..., \n",
      "  [[[   61.88888889]\n",
      "    [  601.88888889]\n",
      "    [  171.11111111]\n",
      "    ..., \n",
      "    [   67.33333333]\n",
      "    [   75.77777778]\n",
      "    [   77.        ]]\n",
      "\n",
      "   [[   56.55555556]\n",
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      "    [   69.33333333]\n",
      "    ..., \n",
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      "\n",
      "   [[   75.88888889]\n",
      "    [   83.11111111]\n",
      "    [   84.22222222]\n",
      "    ..., \n",
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      "\n",
      "   ..., \n",
      "   [[   26.5       ]\n",
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      "    [   17.5       ]]\n",
      "\n",
      "   [[   22.        ]\n",
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      "    [  419.        ]\n",
      "    ..., \n",
      "    [ 1078.5       ]\n",
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      "\n",
      "   [[ 2277.        ]\n",
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      "    ..., \n",
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      "\n",
      "\n",
      "  [[[   31.        ]\n",
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      "    ..., \n",
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      "\n",
      "   [[ 1007.5       ]\n",
      "    [ 1034.5       ]\n",
      "    [ 1070.5       ]\n",
      "    ..., \n",
      "    [   16.        ]\n",
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      "\n",
      "   [[   47.5       ]\n",
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      "    [    3.        ]\n",
      "    ..., \n",
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      "\n",
      "   ..., \n",
      "   [[ 1362.5       ]\n",
      "    [ 1119.        ]\n",
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      "    ..., \n",
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      "\n",
      "   [[ 1055.5       ]\n",
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      "    ..., \n",
      "    [  963.5       ]\n",
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      "\n",
      "   [[ 1009.5       ]\n",
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      "    ..., \n",
      "    [  881.        ]\n",
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      "\n",
      "\n",
      "  [[[  929.5       ]\n",
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      "    ..., \n",
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      "\n",
      "   [[  956.5       ]\n",
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      "    ..., \n",
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      "\n",
      "   [[ 1026.        ]\n",
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      "    ..., \n",
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      "\n",
      "   ..., \n",
      "   [[   89.        ]\n",
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      "    ..., \n",
      "    [  122.5       ]\n",
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      "\n",
      "   [[  416.        ]\n",
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      "    [  997.5       ]\n",
      "    ..., \n",
      "    [   67.5       ]\n",
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      "    [   72.        ]]\n",
      "\n",
      "   [[   73.5       ]\n",
      "    [   71.5       ]\n",
      "    [  255.5       ]\n",
      "    ..., \n",
      "    [  117.        ]\n",
      "    [  106.5       ]\n",
      "    [  112.5       ]]]]]\n"
     ]
    }
   ],
   "source": [
    "print(abc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Shape must be rank 2 but is rank 5 for 'MatMul_3' (op: 'MatMul') with input shapes: [64,5,13,13,?], [?,54080].",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36m_call_cpp_shape_fn_impl\u001b[1;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, debug_python_shape_fn, require_shape_fn)\u001b[0m\n\u001b[0;32m    670\u001b[0m           \u001b[0mgraph_def_version\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_def_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_shapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_tensors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 671\u001b[1;33m           input_tensors_as_shapes, status)\n\u001b[0m\u001b[0;32m    672\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m                 \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[1;34m()\u001b[0m\n\u001b[0;32m    465\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[0;32m    467\u001b[0m   \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m: Shape must be rank 2 but is rank 5 for 'MatMul_3' (op: 'MatMul') with input shapes: [64,5,13,13,?], [?,54080].",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-381aa47364e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[1;31m# Run this locally:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m \u001b[1;31m# train_neural_network(x)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m \u001b[0mtrain_neural_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-22-381aa47364e8>\u001b[0m in \u001b[0;36mtrain_neural_network\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtrain_neural_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m     \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvolutional_neural_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    100\u001b[0m     \u001b[0mcost\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msoftmax_cross_entropy_with_logits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprediction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m     \u001b[0moptimizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAdamOptimizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-22-381aa47364e8>\u001b[0m in \u001b[0;36mconvolutional_neural_network\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[0mouter2_T\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconv2_2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m     \u001b[0mfc_temp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouter2_T\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mouter1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\u001b[0m in \u001b[0;36mmatmul\u001b[1;34m(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)\u001b[0m\n\u001b[0;32m   1799\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1800\u001b[0m       return gen_math_ops._mat_mul(\n\u001b[1;32m-> 1801\u001b[1;33m           a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n\u001b[0m\u001b[0;32m   1802\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1803\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\u001b[0m in \u001b[0;36m_mat_mul\u001b[1;34m(a, b, transpose_a, transpose_b, name)\u001b[0m\n\u001b[0;32m   1261\u001b[0m   \"\"\"\n\u001b[0;32m   1262\u001b[0m   result = _op_def_lib.apply_op(\"MatMul\", a=a, b=b, transpose_a=transpose_a,\n\u001b[1;32m-> 1263\u001b[1;33m                                 transpose_b=transpose_b, name=name)\n\u001b[0m\u001b[0;32m   1264\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36mapply_op\u001b[1;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    766\u001b[0m         op = g.create_op(op_type_name, inputs, output_types, name=scope,\n\u001b[0;32m    767\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 768\u001b[1;33m                          op_def=op_def)\n\u001b[0m\u001b[0;32m    769\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    770\u001b[0m           \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)\u001b[0m\n\u001b[0;32m   2336\u001b[0m                     original_op=self._default_original_op, op_def=op_def)\n\u001b[0;32m   2337\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcompute_shapes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2338\u001b[1;33m       \u001b[0mset_shapes_for_outputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2339\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2340\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_record_op_seen_by_control_dependencies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mset_shapes_for_outputs\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m   1717\u001b[0m       \u001b[0mshape_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1718\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1719\u001b[1;33m   \u001b[0mshapes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshape_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1720\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mshapes\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1721\u001b[0m     raise RuntimeError(\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mcall_with_requiring\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m   1667\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1668\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1669\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcall_cpp_shape_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequire_shape_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1670\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1671\u001b[0m   \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36mcall_cpp_shape_fn\u001b[1;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, debug_python_shape_fn, require_shape_fn)\u001b[0m\n\u001b[0;32m    608\u001b[0m     res = _call_cpp_shape_fn_impl(op, input_tensors_needed,\n\u001b[0;32m    609\u001b[0m                                   \u001b[0minput_tensors_as_shapes_needed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m                                   debug_python_shape_fn, require_shape_fn)\n\u001b[0m\u001b[0;32m    611\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m       \u001b[1;31m# Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\hunbl\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36m_call_cpp_shape_fn_impl\u001b[1;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, debug_python_shape_fn, require_shape_fn)\u001b[0m\n\u001b[0;32m    674\u001b[0m       \u001b[0mmissing_shape_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    675\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mmissing_shape_fn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Shape must be rank 2 but is rank 5 for 'MatMul_3' (op: 'MatMul') with input shapes: [64,5,13,13,?], [?,54080]."
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "IMG_SIZE_PX = 50\n",
    "SLICE_COUNT = 20\n",
    "\n",
    "n_classes = 2\n",
    "batch_size = 10\n",
    "\n",
    "x = tf.placeholder('float')\n",
    "y = tf.placeholder('float')\n",
    "\n",
    "keep_rate = 0.8\n",
    "\n",
    "def conv3d(x, W):\n",
    "    return tf.nn.conv3d(x, W, strides=[1,1,1,1,1], padding='SAME')\n",
    "\n",
    "def maxpool3d(x):\n",
    "    #                        size of window         movement of window as you slide about\n",
    "    return tf.nn.max_pool3d(x, ksize=[1,2,2,2,1], strides=[1,2,2,2,1], padding='SAME')\n",
    "\n",
    "\n",
    "def convolutional_neural_network(x):\n",
    "    #                # 5 x 5 x 5 patches, 1 channel, 32 features to compute.\n",
    "    weights = {'W_conv1_1':tf.Variable(tf.random_normal([3,3,3,1,32])),\n",
    "               #       5 x 5 x 5 patches, 32 channels, 64 features to compute.\n",
    "               'W_conv2_1':tf.Variable(tf.random_normal([3,3,3,32,64])),\n",
    "                    #                # 5 x 5 x 5 patches, 1 channel, 32 features to compute.\n",
    "               'W_conv1_2':tf.Variable(tf.random_normal([3,3,3,1,32])),\n",
    "               #       5 x 5 x 5 patches, 32 channels, 64 features to compute.\n",
    "               'W_conv2_2':tf.Variable(tf.random_normal([3,3,3,32,64])),\n",
    "               #                                  64 features\n",
    "               'W_fc':tf.Variable(tf.random_normal([2924646400,1024])),\n",
    "               'out':tf.Variable(tf.random_normal([1024, n_classes]))}\n",
    "\n",
    "    biases = {'b_conv1_1':tf.Variable(tf.random_normal([32])),\n",
    "               'b_conv2_1':tf.Variable(tf.random_normal([64])),\n",
    "              'b_conv1_2':tf.Variable(tf.random_normal([32])),\n",
    "               'b_conv2_2':tf.Variable(tf.random_normal([64])),\n",
    "              \n",
    "               'b_fc':tf.Variable(tf.random_normal([1024])),\n",
    "               'out':tf.Variable(tf.random_normal([n_classes]))}\n",
    "\n",
    "    \n",
    "    \n",
    "    #                            image X      image Y        image Z\n",
    "    x = tf.reshape(x, shape=[-1, IMG_SIZE_PX, IMG_SIZE_PX, SLICE_COUNT, 1])\n",
    "\n",
    "    conv1_1 = tf.nn.relu(conv3d(x, weights['W_conv1_1']) + biases['b_conv1_1'])\n",
    "    conv1_1 = maxpool3d(conv1_1)\n",
    "\n",
    "\n",
    "    conv2_1 = tf.nn.relu(conv3d(conv1_1, weights['W_conv2_1']) + biases['b_conv2_1'])\n",
    "    conv2_1 = maxpool3d(conv2_1)\n",
    "\n",
    "    \n",
    "    \n",
    "    conv1_2 = tf.nn.relu(conv3d(x, weights['W_conv1_2']) + biases['b_conv1_2'])\n",
    "    conv1_2 = maxpool3d(conv1_2)\n",
    "\n",
    "\n",
    "    conv2_2 = tf.nn.relu(conv3d(conv1_2, weights['W_conv2_2']) + biases['b_conv2_2'])\n",
    "    conv2_2 = maxpool3d(conv2_2)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #inflated result 2_1\n",
    "    outer1 = tf.reshape(conv2_1,[-1, 54080])\n",
    "    \n",
    "    \n",
    "    \n",
    "    #inflated result 2_2\n",
    "    outer2 = tf.reshape(conv2_2,[-1, 54080])\n",
    "    outer2_T=tf.transpose(conv2_2);\n",
    "    \n",
    "    fc_temp = tf.matmul(outer2_T, outer1)\n",
    "    \n",
    "    \n",
    "    fc = tf.reshape(fc_temp,[-1, 2924646400])\n",
    "    \n",
    "    \n",
    "    \n",
    "    fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])\n",
    "    fc = tf.nn.dropout(fc, keep_rate)\n",
    "\n",
    "    output = tf.matmul(fc, weights['out'])+biases['out']\n",
    "\n",
    "    return output\n",
    "\n",
    "much_data = np.load('lessdata-50-50-20.npy')\n",
    "# If you are working with the basic sample data, use maybe 2 instead of 100 here... you don't have enough data to really do this\n",
    "train_data = much_data[:-2]\n",
    "validation_data = much_data[-2:]\n",
    "\n",
    "\n",
    "def train_neural_network(x):\n",
    "    prediction = convolutional_neural_network(x)\n",
    "    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y) )\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(cost)\n",
    "    \n",
    "    hm_epochs = 2\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.initialize_all_variables())\n",
    "        \n",
    "        successful_runs = 0\n",
    "        total_runs = 0\n",
    "        \n",
    "        for epoch in range(hm_epochs):\n",
    "            epoch_loss = 0\n",
    "            for data in train_data:\n",
    "                total_runs += 1\n",
    "                print(total_runs)\n",
    "                try:\n",
    "                    X = data[0]# Actual Reduced image form much date\n",
    "                    Y = data[1]# [0,1] or [1,0] cancer or not cancer form much data\n",
    "                    _, c = sess.run([optimizer, cost], feed_dict={x: X, y: Y})\n",
    "                    epoch_loss += c\n",
    "                    successful_runs += 1\n",
    "                except Exception as e:\n",
    "                    # I am passing for the sake of notebook space, but we are getting 1 shaping issue from one \n",
    "                    # input tensor. Not sure why, will have to look into it. Guessing it's\n",
    "                    # one of the depths that doesn't come to 20.\n",
    "                    pass\n",
    "                    #print(str(e))\n",
    "            \n",
    "            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)\n",
    "\n",
    "            correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n",
    "            accuracy = tf.reduce_mean(tf.cast(correct, 'float'))\n",
    "\n",
    "            print('Accuracy:',accuracy.eval({x:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))\n",
    "            \n",
    "        print('Done. Finishing accuracy:')\n",
    "        print('Accuracy:',accuracy.eval({x:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))\n",
    "        \n",
    "        print('fitment percent:',successful_runs/total_runs)\n",
    "\n",
    "# Run this locally:\n",
    "# train_neural_network(x)\n",
    "train_neural_network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  }
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