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layers.py~
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layers.py~
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# tf_unet is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# tf_unet is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with tf_unet. If not, see <http://www.gnu.org/licenses/>.
'''
Created on Aug 19, 2016
author: jakeret
'''
from __future__ import print_function, division, absolute_import, unicode_literals
import tensorflow as tf
def weight_variable(shape, stddev=0.1):
initial = tf.truncated_normal(shape, stddev=stddev)
return tf.Variable(initial)
def weight_variable_devonc(shape, stddev=0.1):
return tf.Variable(tf.truncated_normal(shape, stddev=stddev))
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W,keep_prob_):
conv_2d = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')
return tf.layerss.dropout(conv_2d, keep_prob_)
def deconv2d(x, W,stride):
x_shape = tf.shape(x)
output_shape = tf.stack([x_shape[0], x_shape[1]*2, x_shape[2]*2, x_shape[3]//2])
return tf.nn.conv2d_transpose(x, W, output_shape, strides=[1, stride, stride, 1], padding='VALID')
def max_pool(x,n):
return tf.nn.max_pool(x, ksize=[1, n, n, 1], strides=[1, n, n, 1], padding='VALID')
def crop_and_concat(x1,x2):
x1_shape = tf.shape(x1)
x2_shape = tf.shape(x2)
# offsets for the top left corner of the crop
offsets = [0, (x1_shape[1] - x2_shape[1]) // 2, (x1_shape[2] - x2_shape[2]) // 2, 0]
size = [-1, x2_shape[1], x2_shape[2], -1]
x1_crop = tf.slice(x1, offsets, size)
return tf.concat([x1_crop, x2], 3)
def pixel_wise_softmax(output_map):
exponential_map = tf.exp(output_map)
evidence = tf.add(exponential_map,tf.reverse(exponential_map,[False,False,False,True]))
return tf.div(exponential_map,evidence, name="pixel_wise_softmax")
def pixel_wise_softmax_2(output_map):
exponential_map = tf.exp(output_map)
sum_exp = tf.reduce_sum(exponential_map, 3, keep_dims=True)
tensor_sum_exp = tf.tile(sum_exp, tf.stack([1, 1, 1, tf.shape(output_map)[3]]))
return tf.div(exponential_map,tensor_sum_exp)
def cross_entropy(y_,output_map):
return -tf.reduce_mean(y_*tf.log(tf.clip_by_value(output_map,1e-10,1.0)), name="cross_entropy")
# return tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(output_map), reduction_indices=[1]))