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# -*- coding: utf-8 -*-
""" Deep Neural Network for MNIST dataset classification task using
a highway network
[MNIST Dataset]
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,
# Training
model = tflearn.DNN(net, tensorboard_verbose=0), Y, n_epoch=20, validation_set=(testX, testY),
show_metric=True, run_id="highway_dense_model")
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