FilesExpand file tree

 master

/

tutorial_mnist_float16.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

105 lines (85 loc) · 4.05 KB

 master

/

tutorial_mnist_float16.py

Copy path

Top

File metadata and controls

• Code
• Blame

105 lines (85 loc) · 4.05 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

#! /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))