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- import math
- import numpy as np
- import tensorflow as tf
- from tensorflow.python.framework import ops
- from utils import *
- # h1 = lrelu(tf.contrib.layers.batch_norm(conv2d(h0, self.df_dim*2, name='d_h1_conv'),decay=0.9,updates_collections=None,epsilon=0.00001,scale=True,scope="d_h1_conv"))
- def batch_norm(x, train=True, name = "batch_norm"):
- return tf.contrib.layers.batch_norm(x, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, scope=name)
- 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 linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
- shape = input_.get_shape().as_list()
- with tf.variable_scope(scope or "Linear"):
- matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
- tf.random_normal_initializer(stddev=stddev))
- bias = tf.get_variable("bias", [output_size],
- initializer=tf.constant_initializer(bias_start))
- if with_w:
- return tf.matmul(input_, matrix) + bias, matrix, bias
- else:
- return tf.matmul(input_, matrix) + bias
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