experiment_4_learning_rate_low.ipynb

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   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tflearn\n",
    "from tflearn.data_utils import shuffle, to_categorical\n",
    "from tflearn.layers.core import input_data, dropout, fully_connected\n",
    "from tflearn.layers.conv import conv_2d, max_pool_2d\n",
    "from tflearn.layers.estimator import regression\n",
    "from tflearn.data_preprocessing import ImagePreprocessing\n",
    "from tflearn.data_augmentation import ImageAugmentation\n",
    "from load_data import load_data\n",
    "\n",
    "# Data loading and preprocessing\n",
    "(X, Y), (X_test, Y_test) = load_data()\n",
    "X, Y = shuffle(X, Y)\n",
    "Y = to_categorical(Y, 10)\n",
    "Y_test = to_categorical(Y_test, 10)\n",
    "\n",
    "# Real-time data preprocessing\n",
    "img_prep = ImagePreprocessing()\n",
    "img_prep.add_featurewise_zero_center()\n",
    "img_prep.add_featurewise_stdnorm()\n",
    "\n",
    "# Real-time data augmentation\n",
    "img_aug = ImageAugmentation()\n",
    "img_aug.add_random_flip_leftright()\n",
    "img_aug.add_random_rotation(max_angle=25.)\n",
    "\n",
    "# Convolutional network building\n",
    "network = input_data(shape=[None, 32, 32, 3],\n",
    "                     data_preprocessing=img_prep,\n",
    "                     data_augmentation=img_aug)\n",
    "network = conv_2d(network, 32, 3, activation='relu')\n",
    "network = max_pool_2d(network, 2)\n",
    "network = conv_2d(network, 64, 3, activation='relu')\n",
    "network = conv_2d(network, 64, 3, activation='relu')\n",
    "network = max_pool_2d(network, 2)\n",
    "network = fully_connected(network, 512, activation='relu')\n",
    "network = dropout(network, 0.5)\n",
    "network = fully_connected(network, 10, activation='softmax')\n",
    "network = regression(network, optimizer='adam',\n",
    "                     loss='categorical_crossentropy',\n",
    "                     learning_rate=0.0001)\n",
    "\n",
    "# Train using classifier\n",
    "model = tflearn.DNN(network, tensorboard_verbose=0, tensorboard_dir='/output')\n",
    "model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test),\n",
    "          show_metric=True, batch_size=96, run_id='cifar_learning_rate_low')"
   ]
  }
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