forked from tensorlayer/TensorLayer
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtutorial_mlp_dropout2.py
More file actions
84 lines (70 loc) · 3.44 KB
/
Copy pathtutorial_mlp_dropout2.py
File metadata and controls
84 lines (70 loc) · 3.44 KB
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
import time
import tensorflow as tf
import tensorlayer as tl
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
def mlp(x, is_train=True, reuse=False):
with tf.variable_scope("MLP", reuse=reuse):
network = tl.layers.InputLayer(x, name='input')
network = tl.layers.DropoutLayer(network, keep=0.8, is_fix=True, is_train=is_train, name='drop1')
network = tl.layers.DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu1')
network = tl.layers.DropoutLayer(network, keep=0.5, is_fix=True, is_train=is_train, name='drop2')
network = tl.layers.DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu2')
network = tl.layers.DropoutLayer(network, keep=0.5, is_fix=True, is_train=is_train, name='drop3')
network = tl.layers.DenseLayer(network, n_units=10, act=tf.identity, name='output')
return network
# define inferences
net_train = mlp(x, is_train=True, reuse=False)
net_test = mlp(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('MLP', train_only=True, printable=False)
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)
n_epoch = 500
batch_size = 500
print_freq = 5
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))