notebook.ipynb

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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib.training.python.training import hparam\n",
    "import numpy as np\n",
    "import trainer.data as data\n",
    "import trainer.model as model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solution(features, labels, mode):\n",
    "    \"\"\"Returns an EstimatorSpec that is constructed using the solution that you have to write below.\"\"\"\n",
    "    # Input Layer (a batch of images that have 64x64 pixels and are RGB colored (3)\n",
    "    input_layer = tf.reshape(features[\"x\"], [-1, 64, 64, 3])\n",
    "    # TODO: Code of your solution\n",
    "    # Convolutional Layer #1\n",
    "    conv1 = tf.layers.conv2d(\n",
    "        inputs=input_layer,\n",
    "        filters=32,\n",
    "        kernel_size=[5, 5],\n",
    "        padding=\"same\",\n",
    "        activation=tf.nn.relu)\n",
    "\n",
    "    # Pooling Layer #1\n",
    "    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)\n",
    "    print(pool1.shape)\n",
    "    # Convolutional Layer #2 and Pooling Layer #2\n",
    "    conv2 = tf.layers.conv2d(\n",
    "        inputs=pool1,\n",
    "        filters=64,\n",
    "        kernel_size=[5, 5],\n",
    "        padding=\"same\",\n",
    "        activation=tf.nn.relu)\n",
    "    print(conv2.shape)\n",
    "    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)\n",
    "\n",
    "    # Dense Layer\n",
    "    pool2_flat = tf.reshape(pool2, [-1, 8 * 8 * 64])\n",
    "    print(pool2_flat.shape)\n",
    "    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)\n",
    "    print(dense.shape)\n",
    "    dropout = tf.layers.dropout(\n",
    "        inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)\n",
    "\n",
    "    # Logits Layer\n",
    "    logits = tf.layers.dense(inputs=dropout, units=4)\n",
    "\n",
    "    predictions = {\n",
    "        # Generate predictions (for PREDICT and EVAL mode)\n",
    "        \"classes\": tf.argmax(input=logits, axis=1),\n",
    "        # Add `softmax_tensor` to the graph. It is used for PREDICT and by the\n",
    "        # `logging_hook`.\n",
    "        \"probabilities\": tf.nn.softmax(logits, name=\"softmax_tensor\")\n",
    "    }\n",
    "\n",
    "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
    "        # TODO: return tf.estimator.EstimatorSpec with prediction values of all classes\n",
    "        return tf.estimator.EstimatorSpec(mode, predictions=predictions)\n",
    "\n",
    "     # Calculate Loss (for both TRAIN and EVAL modes)\n",
    "    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n",
    "\n",
    "    if mode == tf.estimator.ModeKeys.TRAIN:\n",
    "        # TODO: Let the model train here\n",
    "        # TODO: return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)\n",
    "        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)\n",
    "        train_op = optimizer.minimize(\n",
    "            loss=loss,\n",
    "            global_step=tf.train.get_global_step())\n",
    "        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)\n",
    "\n",
    "    if mode == tf.estimator.ModeKeys.EVAL:\n",
    "        # The classes variable below exists of an tensor that contains all the predicted classes in a batch\n",
    "        # TODO: eval_metric_ops = {\"accuracy\": tf.metrics.accuracy(labels=labels, predictions=classes)}\n",
    "        # TODO: return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)\n",
    "        eval_metric_ops = {\n",
    "            \"accuracy\": tf.metrics.accuracy(\n",
    "            labels=labels, predictions=predictions[\"classes\"])}\n",
    "        return tf.estimator.EstimatorSpec(\n",
    "            mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 64, 64, 3)\n",
      "(2000,)\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/tc/08g7j299597fg1rmqgyd0z1h0000gn/T/tmpao5ylhln\n",
      "INFO:tensorflow:Using config: {'_model_dir': '/var/folders/tc/08g7j299597fg1rmqgyd0z1h0000gn/T/tmpao5ylhln', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x153c21be0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "(train_data, train_labels) = data.create_data_with_labels(\"data/train/\")\n",
    "\n",
    "# mnist = tf.contrib.learn.datasets.load_dataset(\"mnist\")\n",
    "# train_data = mnist.train.images # Returns np.array\n",
    "# train_labels = np.asarray(mnist.train.labels, dtype=np.int32)\n",
    "\n",
    "print(train_data.shape)\n",
    "print(train_labels.shape)\n",
    "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={\"x\": train_data},\n",
    "    y=train_labels,\n",
    "    batch_size=100,\n",
    "    num_epochs=None,\n",
    "    shuffle=True)\n",
    "\n",
    "estimator = tf.estimator.Estimator(model_fn=solution)\n",
    "eval_steps = 5\n",
    "steps_per_eval = int(get_training_steps() / eval_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# estimator.train(train_input_fn, steps=steps_per_eval)"
   ]
  }
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