examples/images/convnet_highway_mnist.py

# -*- coding: utf-8 -*-

""" Convolutional Neural Network for MNIST dataset classification task.

References:
    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based
    learning applied to document recognition." Proceedings of the IEEE,
    86(11):2278-2324, November 1998.

Links:
    [MNIST Dataset] http://yann.lecun.com/exdb/mnist/

"""

from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import highway_conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization, batch_normalization
from tflearn.layers.estimator import regression

# Data loading and preprocessing
import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])

# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
#highway convolutions with pooling and dropout
for i in range(3):
    for j in [3, 2, 1]: 
        network = highway_conv_2d(network, 16, j, activation='elu')
    network = max_pool_2d(network, 2)
    network = batch_normalization(network)
    
network = fully_connected(network, 128, activation='elu')
network = fully_connected(network, 256, activation='elu')
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
                     loss='categorical_crossentropy', name='target')

# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X, Y, n_epoch=20, validation_set=(testX, testY),
          show_metric=True, run_id='convnet_highway_mnist')