Permalink
Fetching contributors…
Cannot retrieve contributors at this time
106 lines (85 sloc) 4.05 KB
#! /usr/bin/python
# -*- coding: utf-8 -*-
import time
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
tf.logging.set_verbosity(tf.logging.DEBUG)
tl.logging.set_verbosity(tl.logging.DEBUG)
LayersConfig.tf_dtype = tf.float16 # tf.float32 tf.float16
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
sess = tf.InteractiveSession()
batch_size = 128
x = tf.placeholder(LayersConfig.tf_dtype, shape=[batch_size, 28, 28, 1])
y_ = tf.placeholder(tf.int64, shape=[batch_size])
def model(x, is_train=True, reuse=False):
with tf.variable_scope("model", reuse=reuse):
n = InputLayer(x, name='input')
# cnn
n = Conv2d(n, 32, (5, 5), (1, 1), padding='SAME', name='cnn1')
n = BatchNormLayer(n, act=tf.nn.relu, is_train=is_train, name='bn1')
n = MaxPool2d(n, (2, 2), (2, 2), padding='SAME', name='pool1')
n = Conv2d(n, 64, (5, 5), (1, 1), padding='SAME', name='cnn2')
n = BatchNormLayer(n, act=tf.nn.relu, is_train=is_train, name='bn2')
n = MaxPool2d(n, (2, 2), (2, 2), padding='SAME', name='pool2')
# mlp
n = FlattenLayer(n, name='flatten')
n = DropoutLayer(n, 0.5, True, is_train, name='drop1')
n = DenseLayer(n, 256, act=tf.nn.relu, name='relu1')
n = DropoutLayer(n, 0.5, True, is_train, name='drop2')
n = DenseLayer(n, 10, act=None, name='output')
return n
# define inferences
net_train = model(x, is_train=True, reuse=False)
net_test = model(x, is_train=False, reuse=True)
net_train.print_params(False)
# 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, LayersConfig.tf_dtype))
# define the optimizer
train_params = tl.layers.get_variables_with_name('model', train_only=True, printable=False)
# for float16 epsilon=1e-4 see https://stackoverflow.com/questions/42064941/tensorflow-float16-support-is-broken
# for float32 epsilon=1e-08
train_op = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999, epsilon=1e-4,
use_locking=False).minimize(cost, var_list=train_params)
# initialize all variables in the session
tl.layers.initialize_global_variables(sess)
# train the network
n_epoch = 500
print_freq = 1
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))