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
|
- import math
- import numpy as np
- import tensorflow as tf
- from tensorflow.python.framework import ops
- from utils import *
- def batch_norm(x, name="batch_norm"):
- eps = 1e-6
- with tf.variable_scope(name):
- nchannels = x.get_shape()[3]
- scale = tf.get_variable("scale", [nchannels], initializer=tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32))
- center = tf.get_variable("center", [nchannels], initializer=tf.constant_initializer(0.0, dtype = tf.float32))
- ave, dev = tf.nn.moments(x, axes=[1,2], keep_dims=True)
- inv_dev = tf.rsqrt(dev + eps)
- normalized = (x-ave)*inv_dev * scale + center
- return normalized
- def conv2d(input_, output_dim,
- k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
- name="conv2d"):
- with tf.variable_scope(name):
- w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
- initializer=tf.truncated_normal_initializer(stddev=stddev))
- conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
- biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
- conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
- return conv
- def deconv2d(input_, output_shape,
- k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
- name="deconv2d", with_w=False):
- with tf.variable_scope(name):
- # filter : [height, width, output_channels, in_channels]
- w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
- initializer=tf.random_normal_initializer(stddev=stddev))
- try:
- deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
- strides=[1, d_h, d_w, 1])
- # Support for verisons of TensorFlow before 0.7.0
- except AttributeError:
- deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
- strides=[1, d_h, d_w, 1])
- biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
- deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
- if with_w:
- return deconv, w, biases
- else:
- return deconv
-
- def lrelu(x, leak=0.2, name="lrelu"):
- return tf.maximum(x, leak*x)
- def celoss(logits, labels):
- return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))
-
|