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#! /usr/bin/python
# -*- coding: utf-8 -*-
import time
import tensorflow as tf
import tensorlayer as tl
tf.logging.set_verbosity(tf.logging.DEBUG)
tl.logging.set_verbosity(tl.logging.DEBUG)
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
# X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
sess = tf.InteractiveSession()
batch_size = 128
x = tf.placeholder(tf.float32, shape=[batch_size, 28, 28, 1])
y_ = tf.placeholder(tf.int64, shape=[batch_size])
def model(x, is_train=True, reuse=False):
# In BNN, all the layers inputs are binary, with the exception of the first layer.
# ref: https://github.com/itayhubara/BinaryNet.tf/blob/master/models/BNN_cifar10.py
with tf.variable_scope("binarynet", reuse=reuse):
net = tl.layers.InputLayer(x, name='input')
net = tl.layers.DorefaConv2d(net, 1, 3, 32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1') #pylint: disable=bare-except
net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1')
net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn1')
# net = tl.layers.SignLayer(net)
net = tl.layers.DorefaConv2d(net, 1, 3, 64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2') #pylint: disable=bare-except
net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2')
net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn2')
net = tl.layers.FlattenLayer(net)
# net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop1')
# net = tl.layers.SignLayer(net)
net = tl.layers.DorefaDenseLayer(net, 1, 3, 256, b_init=None, name='dense')
net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn3')
# net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop2')
# net = tl.layers.SignLayer(net)
net = tl.layers.DenseLayer(net, 10, b_init=None, name='bout')
net = tl.layers.BatchNormLayer(net, is_train=is_train, name='bno')
return net
# define inferences
net_train = model(x, is_train=True, reuse=False)
net_test = model(x, is_train=False, reuse=True)
# cost for training
y = net_train.outputs
cost = tl.cost.cross_entropy(y, y_, name='xentropy')
# cost and accuracy for evalution
y2 = net_test.outputs
cost_test = tl.cost.cross_entropy(y2, y_, name='xentropy2')
correct_prediction = tf.equal(tf.argmax(y2, 1), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# define the optimizer
train_params = tl.layers.get_variables_with_name('binarynet', True, True)
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)
net_train.print_params()
net_train.print_layers()
n_epoch = 200
print_freq = 5
# print(sess.run(net_test.all_params)) # print real values of parameters
for epoch in range(n_epoch):
start_time = time.time()
for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
train_loss, train_acc, n_batch = 0, 0, 0
for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
err, ac = sess.run([cost_test, acc], feed_dict={x: X_train_a, y_: y_train_a})
train_loss += err
train_acc += ac
n_batch += 1
print(" train loss: %f" % (train_loss / n_batch))
print(" train acc: %f" % (train_acc / n_batch))
val_loss, val_acc, n_batch = 0, 0, 0
for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True):
err, ac = sess.run([cost_test, acc], feed_dict={x: X_val_a, y_: y_val_a})
val_loss += err
val_acc += ac
n_batch += 1
print(" val loss: %f" % (val_loss / n_batch))
print(" val acc: %f" % (val_acc / n_batch))
print('Evaluation')
test_loss, test_acc, n_batch = 0, 0, 0
for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=True):
err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
test_loss += err
test_acc += ac
n_batch += 1
print(" test loss: %f" % (test_loss / n_batch))
print(" test acc: %f" % (test_acc / n_batch))