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9528da5 Aug 27, 2018
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#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
import tensorlayer as tl
tf.logging.set_verbosity(tf.logging.DEBUG)
tl.logging.set_verbosity(tl.logging.DEBUG)
sess = tf.InteractiveSession()
# prepare data
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
# define placeholder
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
y_ = tf.placeholder(tf.int64, shape=[None], name='y_')
# define the network
network = tl.layers.InputLayer(x, name='input')
network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
network = tl.layers.DenseLayer(network, 800, tf.nn.relu, name='relu1')
network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
network = tl.layers.DenseLayer(network, 800, tf.nn.relu, name='relu2')
network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
# the softmax is implemented internally in tl.cost.cross_entropy(y, y_) to
# speed up computation, so we use identity here.
# see tf.nn.sparse_softmax_cross_entropy_with_logits()
network = tl.layers.DenseLayer(network, n_units=10, act=None, name='output')
# define cost function and metric.
y = network.outputs
cost = tl.cost.cross_entropy(y, y_, name='cost')
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
y_op = tf.argmax(tf.nn.softmax(y), 1)
# define the optimizer
train_params = network.all_params
train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost, var_list=train_params)
# initialize all variables in the session
tl.layers.initialize_global_variables(sess)
# print network information
network.print_params()
network.print_layers()
# train the network
tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_, acc=acc, batch_size=500, \
n_epoch=500, print_freq=5, X_val=X_val, y_val=y_val, eval_train=False)
# evaluation
tl.utils.test(sess, network, acc, X_test, y_test, x, y_, batch_size=None, cost=cost)
# save the network to .npz file
tl.files.save_npz(network.all_params, name='model.npz')
sess.close()