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ops.py
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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 http://stackoverflow.com/a/33950177"""
# def __init__(self, epsilon=1e-5, momentum = 0.1, name="batch_norm"):
# with tf.variable_scope(name):
# self.epsilon = epsilon
# self.momentum = momentum
#
# self.ema = tf.train.ExponentialMovingAverage(decay=self.momentum)
# self.name = name
#
# def __call__(self, x, train=True):
# shape = x.get_shape().as_list()
#
# if train:
# with tf.variable_scope(self.name) as scope:
# self.beta = tf.get_variable("beta", [shape[-1]],
# initializer=tf.constant_initializer(0.))
# self.gamma = tf.get_variable("gamma", [shape[-1]],
# initializer=tf.random_normal_initializer(1., 0.02))
#
# try:
# batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2, 3], name='moments')
# except:
# try:
# batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
# except:
# batch_mean, batch_var = tf.nn.moments(x, [0, 1], name='moments')
#
# ema_apply_op = self.ema.apply([batch_mean, batch_var])
# self.ema_mean, self.ema_var = self.ema.average(batch_mean), self.ema.average(batch_var)
#
# with tf.control_dependencies([ema_apply_op]):
# mean, var = tf.identity(batch_mean), tf.identity(batch_var)
# else:
# mean, var = self.ema_mean, self.ema_var
#
# normed = tf.nn.batch_norm_with_global_normalization(
# x, mean, var, self.beta, self.gamma, self.epsilon, scale_after_normalization=True)
#
# return normed
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]))
Args:
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=0.01,
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 conv3d(input_, output_dim,
k_t=4, k_h=4, k_w=4, d_t=2, d_h=2, d_w=2, pad_t=1, pad_h=1, pad_w=1, stddev=0.01,
name="conv3d"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_t, k_h, k_w, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv3d(input_, w, strides=[1, d_t, 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=4, k_w=4, d_h=2, d_w=2, pad_h=1, pad_w=1, stddev=0.01,
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))
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1], padding='SAME')
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 deconv3d(input_, output_shape,
k_t=4, k_h=4, k_w=4, d_t=2, d_h=2, d_w=2, pad_t=1, pad_h=1, pad_w=1, stddev=0.01,
name="deconv3d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_t, k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
deconv = tf.nn.conv3d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_t, d_h, d_w, 1], padding='SAME')
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), output_shape)
if with_w:
return deconv, w, biases
else:
return deconv
def l1Penalty(x, scale=0.1, name="L1Penalty"):
l1P = tf.contrib.layers.l1_regularizer(scale)
return l1P(x)
def lrelu(x, leak=0.2, name="lrelu"):
return tf.maximum(x, leak*x)
def linear(input_, output_size, scope=None, stddev=0.01, 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