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tflearn/examples/images/highway_dnn.py
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# -*- coding: utf-8 -*- | |
""" Deep Neural Network for MNIST dataset classification task using | |
a highway network | |
References: | |
Links: | |
[MNIST Dataset] http://yann.lecun.com/exdb/mnist/ | |
[https://arxiv.org/abs/1505.00387](https://arxiv.org/abs/1505.00387) | |
""" | |
from __future__ import division, print_function, absolute_import | |
import tflearn | |
# Data loading and preprocessing | |
import tflearn.datasets.mnist as mnist | |
X, Y, testX, testY = mnist.load_data(one_hot=True) | |
# Building deep neural network | |
input_layer = tflearn.input_data(shape=[None, 784]) | |
dense1 = tflearn.fully_connected(input_layer, 64, activation='elu', | |
regularizer='L2', weight_decay=0.001) | |
#install a deep network of highway layers | |
highway = dense1 | |
for i in range(10): | |
highway = tflearn.highway(highway, 64, activation='elu', | |
regularizer='L2', weight_decay=0.001, transform_dropout=0.8) | |
softmax = tflearn.fully_connected(highway, 10, activation='softmax') | |
# Regression using SGD with learning rate decay and Top-3 accuracy | |
sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000) | |
top_k = tflearn.metrics.Top_k(3) | |
net = tflearn.regression(softmax, optimizer=sgd, metric=top_k, | |
loss='categorical_crossentropy') | |
# Training | |
model = tflearn.DNN(net, tensorboard_verbose=0) | |
model.fit(X, Y, n_epoch=20, validation_set=(testX, testY), | |
show_metric=True, run_id="highway_dense_model") |