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ops_.py
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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 *
from sklearn import preprocessing
class batch_norm(object):
# 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 __init__(self, epsilon=1e-5, momentum = 0.9, name="batch_norm"):
with tf.variable_scope(name):
self.epsilon = epsilon
self.momentum = momentum
self.name = name
def __call__(self, x, train=True):
return tf.contrib.layers.batch_norm(x, decay=self.momentum, updates_collections=None, epsilon=self.epsilon, scale=True, scope=self.name)
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([x, y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])], 3)
def conv2d(input_, output_dim,
k_h=5, k_w=5, d_h=3, d_w=3, 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 e2e(input_,output_dim,k_h=50, d_h=1, d_w=1, stddev=0.01,
name="e2e"):
with tf.variable_scope(name):
w1 = tf.get_variable('w1', [k_h, k_h, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv1 = tf.nn.conv2d(input_, w1[0:1,:,:,:], strides=[1, d_h, d_w, 1], padding='VALID')
biases1 = tf.get_variable('biases1', [output_dim], initializer=tf.constant_initializer(0.0))
conv1 = tf.reshape(tf.nn.bias_add(conv1, biases1), conv1.get_shape())
w2 = tf.get_variable('w2', [k_h,k_h, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv2 = tf.nn.conv2d(input_, w2[:,0:1,:,:], strides=[1, d_h, d_w, 1], padding='VALID')
biases2 = tf.get_variable('biases2', [output_dim], initializer=tf.constant_initializer(0.0))
conv2 = tf.reshape(tf.nn.bias_add(conv2, biases2), conv2.get_shape())
m1 = tf.tile(conv1,[1,1,k_h,1])
m2 = tf.tile(conv2,[1,k_h,1,1])
conv = tf.add(m1, m2)/2
return conv
def e2n(input_,output_dim,k_h=50, d_h=1, d_w=1, stddev=0.01,
name="e2n"):
with tf.variable_scope(name):
w = tf.get_variable('w', [1, k_h, 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='VALID')
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 n2g(input_,output_dim,k_h=50, d_h=1, d_w=1, stddev=0.01,
name="e2n"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, 1, 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='VALID')
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 de_n2g(input_, output_shape,
k_h=50, d_h=1, d_w=1, stddev=0.01,
name="de_n2g", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h,1, 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='VALID')
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 de_e2n(input_, output_shape,
k_h=50, d_h=1, d_w=1, stddev=0.01,
name="de_n2g", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [1,k_h, 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='VALID')
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 de_e2e(input_, output_shape,
k_h=50, d_h=1, d_w=1, stddev=0.01,
name="de_n2g", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
input_1=tf.reshape(tf.reduce_sum(input_,axis=1),(int(input_.shape[0]),k_h,1,int(input_.shape[3])))
input_2=tf.reshape(tf.reduce_sum(input_,axis=2),(int(input_.shape[0]),1,k_h,int(input_.shape[3])))
w1 = tf.get_variable('w1', [1,k_h, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
deconv1 = tf.nn.conv2d_transpose(input_1, w1, output_shape=output_shape,
strides=[1, d_h, d_w, 1],padding='VALID')
biases1 = tf.get_variable('biases1', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv1 = tf.reshape(tf.nn.bias_add(deconv1, biases1), deconv1.get_shape())
w2 = tf.get_variable('w2', [k_h,1, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
deconv2 = tf.nn.conv2d_transpose(input_2, w2, output_shape=output_shape,
strides=[1, d_h, d_w, 1],padding='VALID')
biases2 = tf.get_variable('biases2', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv2 = tf.reshape(tf.nn.bias_add(deconv2, biases2), deconv2.get_shape())
deconv=tf.add(deconv1,deconv2)/2
if with_w:
return deconv, w1, biases1
else:
return deconv
def deconv2d(input_, output_shape,
k_h=5, k_w=5, d_h=3, d_w=3, 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 upper_triang(input):
ones = tf.ones_like(input) #https://stackoverflow.com/questions/41514722/convert-the-strictly-upper-triangular-part-of-a-matrix-into-an-array-in-tensorfl
mask_a = tf.matrix_band_part(ones, 0, -1) # Upper triangular matrix of 0s and 1s
mask_b = tf.matrix_band_part(ones, 0, 0) # Diagonal matrix of 0s and 1s
mask = tf.cast(mask_a - mask_b, dtype=tf.bool) # Make a bool mask
upper_triangular_flat = tf.boolean_mask(input, mask)
return upper_triangular_flat
def normalize_data(X):
for i in range(len(X)):
if i == 0:
vv = X[i:i + 1, :, :, :]
vv = np.reshape(vv, (35, 35))
min_max_scaler = preprocessing.MinMaxScaler()
vv = min_max_scaler.fit_transform(vv)
vv2 = np.reshape(vv, (1, 35, 35))
else:
vv = X[i:i + 1, :, :]
vv = np.reshape(vv, (35, 35))
min_max_scaler = preprocessing.MinMaxScaler()
vv = min_max_scaler.fit_transform(vv)
vv1 = np.reshape(vv, (1, 35, 35))
vv2 = np.concatenate((vv2, vv1), axis=0)
return vv2
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
def linear_mask(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_mask"):
matrix = np.ones((shape[1], output_size[1])).astype('float32')
mask_ = np.loadtxt('mask.csv',delimiter=',')
mask = mask_.astype('float32')
output=tf.matmul(input_, matrix*mask)
return tf.reshape(output,[shape[0],54,1,1])