CNN for my project

CNN.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(X, w, w3, w4, w_o, p_keep_conv, p_keep_hidden):\n",
    "    l1a = tf.nn.relu(tf.nn.conv2d(X, w, strides=[1, 1, 1, 1], padding='SAME') + b1)\n",
    "    l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "    l1 = tf.nn.dropout(l1, p_keep_conv)\n",
    "\n",
    "    l3a = tf.nn.relu(tf.nn.conv2d(l1, w3, strides=[1, 1, 1, 1], padding='SAME') + b3)\n",
    "    l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "    l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]])\n",
    "    l3 = tf.nn.dropout(l3, p_keep_conv)\n",
    "\n",
    "    l4 = tf.nn.relu(tf.matmul(l3, w4) + b4)\n",
    "    l4 = tf.nn.dropout(l4, p_keep_hidden)\n",
    "\n",
    "    pyx = tf.matmul(l4, w_o) + b5\n",
    "    return pyx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "init_op = tf.global_variables_initializer()\n",
    "\n",
    "X = tf.placeholder(\"float\", [None, 28, 28, 1])\n",
    "Y = tf.placeholder(\"float\", [None, 10])\n",
    "\n",
    "w = tf.get_variable(\"w\", shape=[4, 4, 1, 16], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b1 = tf.get_variable(name=\"b1\", shape=[16], initializer=tf.zeros_initializer())\n",
    "w3 = tf.get_variable(\"w3\", shape=[4, 4, 16, 32], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b3 = tf.get_variable(name=\"b3\", shape=[32], initializer=tf.zeros_initializer())\n",
    "w4 = tf.get_variable(\"w4\", shape=[32 * 7 * 7, 625], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b4 = tf.get_variable(name=\"b4\", shape=[625], initializer=tf.zeros_initializer())\n",
    "w_o = tf.get_variable(\"w_o\", shape=[625, 10], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b5 = tf.get_variable(name=\"b5\", shape=[10], initializer=tf.zeros_initializer())\n",
    "\n",
    "p_keep_conv = tf.placeholder(\"float\")\n",
    "p_keep_hidden = tf.placeholder(\"float\")\n",
    "py_x = model(X, w, w3, w4, w_o, p_keep_conv, p_keep_hidden)\n",
    "\n",
    "reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
    "reg_constant = 0.01\n",
    "\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y) + reg_constant * sum(reg_losses))\n",
    "\n",
    "train_op = tf.train.RMSPropOptimizer(0.0001, 0.9).minimize(cost)\n",
    "predict_op = tf.argmax(py_x, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Training\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "test_acc = []\n",
    "train_loss = []\n",
    "val_loss = []\n",
    "test_loss = []\n",
    "with tf.Session() as sess:\n",
    "    tf.global_variables_initializer().run()\n",
    "\n",
    "    for i in range(256):\n",
    "        training_batch = zip(range(0, len(trX), batch_size),\n",
    "                             range(batch_size, len(trX)+1, batch_size))\n",
    "        for start, end in training_batch:\n",
    "            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],\n",
    "                                          p_keep_conv: 0.8, p_keep_hidden: 0.5})\n",
    "\n",
    "        train_acc = np.mean(np.argmax(trY, axis=1) ==\n",
    "                         sess.run(predict_op, feed_dict={X: trX,\n",
    "                                                         Y: trY,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0}))\n",
    "        train_acc.append(train_acc)\n",
    "        \n",
    "        val_acc = np.mean(np.argmax(teY, axis=1) ==\n",
    "                         sess.run(predict_op, feed_dict={X: teX,\n",
    "                                                         Y: teY,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0}))\n",
    "        val_acc.append(val_acc)\n",
    "        test_acc = np.mean(np.argmax(mnist.test.labels, axis=1) ==\n",
    "                         sess.run(predict_op, feed_dict={X: mnist_test_images,\n",
    "                                                         Y: mnist.test.labels,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0}))\n",
    "        test_acc.append(test_acc)\n",
    "        print('Step {0}. Train accuracy: {3}. Validation accuracy: {1}. Test accuracy: {2}.'.format(i, val_acc, test_acc, train_acc))\n",
    "        \n",
    "        _, loss_train = sess.run([predict_op, cost],\n",
    "                                                         feed_dict={X: trX,\n",
    "                                                         Y: trY,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0})\n",
    "        train_loss.append(loss_train)\n",
    "        _, loss_val = sess.run([predict_op, cost],\n",
    "                                                         feed_dict={X: teX,\n",
    "                                                         Y: teY,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0})\n",
    "        val_loss.append(loss_val)\n",
    "        _, loss_test = sess.run([predict_op, cost],\n",
    "                                                         feed_dict={X: mnist_test_images,\n",
    "                                                         Y: mnist.test.labels,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0})\n",
    "        test_loss.append(loss_test)\n",
    "        print('Train loss: {0}. Validation loss: {1}. Test loss: {2}.'.format(loss_train, loss_val, loss_test))\n",
    "        \n",
    "    all_saver = tf.train.Saver() \n",
    "    all_saver.save(sess, '/resources/data.chkp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Predicting\n",
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess, \"./data.chkp\")\n",
    "    pr = sess.run(predict_op, feed_dict={X: mnist_test_images,\n",
    "                                                         Y: mnist.test.labels,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0})\n",
    "    print(np.mean(np.argmax(mnist.test.labels, axis=1) ==\n",
    "                         sess.run(predict_op, feed_dict={X: mnist_test_images,\n",
    "                                                         Y: mnist.test.labels,\n",
    "                                                         p_keep_conv: 1.0,\n",
    "                                                         p_keep_hidden: 1.0})))"
   ]
  }
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