TFLearn-MNIST.ipynb

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   "source": [
    "# Convolutional Neural Network for MNIST dataset classification task.\n",
    "References:\n",
    "    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. \"Gradient-based\n",
    "    learning applied to document recognition.\" Proceedings of the IEEE,\n",
    "    86(11):2278-2324, November 1998.\n",
    "Links:\n",
    "    [MNIST Dataset] http://yann.lecun.com/exdb/mnist/"
   ]
  },
  {
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     "name": "stdout",
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     "text": [
      "hdf5 is not supported on this machine (please install/reinstall h5py for optimal experience)\n",
      "curses is not supported on this machine (please install/reinstall curses for an optimal experience)\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tflearn\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.normalization import local_response_normalization\n",
    "from tflearn.layers.estimator import regression\n",
    "\n",
    "import tflearn.datasets.mnist as mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Extracting mnist/train-images-idx3-ubyte.gz\n",
      "Extracting mnist/train-labels-idx1-ubyte.gz\n",
      "Extracting mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting mnist/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Data loading and preprocessing\n",
    "X, Y, testX, testY = mnist.load_data(one_hot=True)\n",
    "X = X.reshape([-1, 28, 28, 1])\n",
    "testX = testX.reshape([-1, 28, 28, 1])"
   ]
  },
  {
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   "source": [
    "# Building convolutional network\n",
    "network = input_data(shape=[None, 28, 28, 1], name='input')\n",
    "\n",
    "network = conv_2d(network, 32, 3, activation='relu', regularizer=\"L2\")\n",
    "network = max_pool_2d(network, 2)\n",
    "network = local_response_normalization(network)\n",
    "\n",
    "network = conv_2d(network, 64, 3, activation='relu', regularizer=\"L2\")\n",
    "network = max_pool_2d(network, 2)\n",
    "network = local_response_normalization(network)\n",
    "\n",
    "network = fully_connected(network, 128, activation='tanh')\n",
    "network = dropout(network, 0.8)\n",
    "\n",
    "network = fully_connected(network, 256, activation='tanh')\n",
    "network = dropout(network, 0.8)\n",
    "\n",
    "network = fully_connected(network, 10, activation='softmax')\n",
    "\n",
    "network = regression(network, optimizer='adam', learning_rate=0.01,\n",
    "                     loss='categorical_crossentropy', name='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Training\n",
    "model = tflearn.DNN(network, tensorboard_verbose=0)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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     "text": [
      "Training Step: 1254  | total loss: \u001b[1m\u001b[32m0.20072\u001b[0m\u001b[0m | time: 208.282s\n",
      "\u001b[2K\r",
      "| Adam | epoch: 002 | loss: 0.20072 - acc: 0.9542 -- iter: 25216/55000\n"
     ]
    }
   ],
   "source": [
    "model.fit({'input': X}, {'target': Y}, n_epoch=20,\n",
    "           validation_set=({'input': testX}, {'target': testY}),\n",
    "           snapshot_step=100, show_metric=True, run_id='convnet_mnist')"
   ]
  },
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