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# Original Version: Taehoon Kim (
# + Source:
# + License: MIT
import math
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from utils import *
class batch_norm(object):
"""Code modification of"""
def __init__(self, epsilon=1e-5, momentum = 0.9, name="batch_norm"):
with tf.variable_scope(name):
self.epsilon = epsilon
self.momentum = momentum = name
def __call__(self, x, train):
return tf.contrib.layers.batch_norm(x, decay=self.momentum, updates_collections=None, epsilon=self.epsilon,
center=True, scale=True, is_training=train,
def binary_cross_entropy(preds, targets, name=None):
"""Computes binary cross entropy given `preds`.
For brevity, let `x = `, `z = targets`. The logistic loss is
loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))
preds: A `Tensor` of type `float32` or `float64`.
targets: A `Tensor` of the same type and shape as `preds`.
eps = 1e-12
with ops.op_scope([preds, targets], name, "bce_loss") as name:
preds = ops.convert_to_tensor(preds, name="preds")
targets = ops.convert_to_tensor(targets, name="targets")
return tf.reduce_mean(-(targets * tf.log(preds + eps) +
(1. - targets) * tf.log(1. - preds + eps)))
def conv_cond_concat(x, y):
"""Concatenate conditioning vector on feature map axis."""
x_shapes = x.get_shape()
y_shapes = y.get_shape()
return tf.concat(3, [x, y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])])
def conv2d(input_, output_dim,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
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())
conv = tf.nn.bias_add(conv, biases)
return conv
def conv2d_transpose(input_, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="conv2d_transpose", 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]],
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())
deconv = tf.nn.bias_add(deconv, biases)
if with_w:
return deconv, w, biases
return deconv
def lrelu(x, leak=0.2, name="lrelu"):
with tf.variable_scope(name):
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * abs(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,
bias = tf.get_variable("bias", [output_size],
if with_w:
return tf.matmul(input_, matrix) + bias, matrix, bias
return tf.matmul(input_, matrix) + bias