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cnn.py
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cnn.py
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# Ref: https://github.com/arashsaber/Deep-Convolutional-AutoEncoder/blob/master/ConvolutionalAutoEncoder.py
import tensorflow as tf
import numpy as np
# ---------------------------------
def conv2d(input, name, kshape, strides=[1, 1, 1, 1], pad='SAME'):
with tf.variable_scope(name):
W = tf.get_variable(name='w_'+name,
shape=kshape,
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
b = tf.get_variable(name='b_' + name,
shape=[kshape[3]],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
out = tf.nn.conv2d(input,W,strides=strides, padding=pad)
out = tf.nn.bias_add(out, b)
out = tf.nn.relu(out)
return out
# ---------------------------------
def deconv2d(input, name, kshape, n_outputs, strides=[1, 1], pad='SAME'):
with tf.variable_scope(name):
out = tf.contrib.layers.conv2d_transpose(input,
num_outputs= n_outputs,
kernel_size=kshape,
stride=strides,
padding=pad,
weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False),
biases_initializer=tf.contrib.layers.xavier_initializer(uniform=False),
activation_fn=tf.nn.relu)
return out
# ---------------------------------
def maxpool2d(x,name,kshape=[1, 2, 2, 1], strides=[1, 2, 2, 1]):
with tf.variable_scope(name):
out = tf.nn.max_pool(x,
ksize=kshape, #size of window
strides=strides,
padding='SAME')
return out
# ---------------------------------
def upsample(input, name, factor=[2,2]):
size = [int(input.shape[1] * factor[0]), int(input.shape[2] * factor[1])]
with tf.variable_scope(name):
out = tf.image.resize_bilinear(input, size=size, align_corners=None, name=None)
return out
# ---------------------------------
def fullyConnected(input, name, output_size):
with tf.variable_scope(name):
input_size = input.shape[1:]
input_size = int(np.prod(input_size))
W = tf.get_variable(name='w_'+name,
shape=[input_size, output_size],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
b = tf.get_variable(name='b_'+name,
shape=[output_size],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
input = tf.reshape(input, [-1, input_size])
out = tf.add(tf.matmul(input, W), b)
return out
# ---------------------------------
def dropout(input, name, keep_rate):
with tf.variable_scope(name):
out = tf.nn.dropout(input, keep_rate)
return out
# ---------------------------------