def forward(self, input_tensor):
self.input_tensor = input_tensor
#pdb.set_trace()
self.check_input_shape()
self.in_N, self.in_h, self.in_w, self.in_depth = self.input_tensor.get_shape().as_list()
# init weights
self.weights_shape = [self.kernel_size, self.kernel_size, self.in_depth, self.output_depth]
self.strides = [1,self.stride_size, self.stride_size,1]
with tf.variable_scope(self.name):
self.weights = variables.weights(self.weights_shape)
self.biases = variables.biases(self.output_depth)
with tf.name_scope(self.name):
conv = tf.nn.conv2d(self.input_tensor, self.weights, strides = self.strides, padding=self.pad)
conv = tf.reshape(tf.nn.bias_add(conv, self.biases), conv.get_shape().as_list())
if isinstance(self.act, str):
self.activations = activations.apply(conv, self.act)
elif hasattr(self.act, '__call__'):
self.activations = self.act(conv)
if self.keep_prob<1.0:
self.activations = tf.nn.dropout(self.activations, keep_prob=self.keep_prob)
tf.summary.histogram('activations', self.activations)
tf.summary.histogram('weights', self.weights)
tf.summary.histogram('biases', self.biases)
return self.activations
def forward(self, input_tensor):
self.input_tensor = input_tensor
#pdb.set_trace()
self.check_input_shape()
self.in_N, self.in_h, self.in_w, self.in_c, self.in_depth = self.input_tensor.get_shape(
).as_list()
# init weights
self.weights_shape = [
self.kernel_size, self.kernel_size, self.kernel_channels,
self.in_depth, self.output_depth
]
self.strides = [
1, self.stride_size, self.stride_size, self.stride_size, 1
]
with tf.variable_scope(self.name):
self.weights = variables.weights(self.weights_shape,
initializer=self.weights_init,
name=self.name)
self.biases = variables.biases(self.output_depth,
initializer=self.bias_init,
name=self.name)
with tf.name_scope(self.name):
# convolutional layer뒤에 batch_normalization이 붙도록 코드를 수정
if self.final_layer == False:
conv = tf.nn.conv3d(self.input_tensor,
self.weights,
strides=self.strides,
padding=self.pad)
bn = tf.contrib.layers.batch_norm(conv,
decay=self.momentum,
updates_collections=None,
epsilon=self.epsilon,
scale=False,
is_training=self.training)
if isinstance(self.act, str):
self.activations = activations.apply(bn, self.act)
elif hasattr(self.act, '__call__'):
self.activations = self.act(bn)
print('not final')
#
# if self.keep_prob<1.0:
# self.activations = tf.nn.dropout(self.activations, keep_prob=self.keep_prob)
#
tf.summary.histogram('activations', self.activations)
tf.summary.histogram('weights', self.weights)
tf.summary.histogram('biases', self.biases)
else:
conv = tf.nn.conv3d(self.input_tensor,
self.weights,
strides=self.strides,
padding=self.pad)
self.activations = conv
print('final')
return self.activations
def forward(self, input_tensor):
self.input_tensor = input_tensor
self.check_input_shape()
in_N, in_h, in_w, in_depth = self.input_tensor.get_shape().as_list()
self.input_depth = in_depth
#pdb.set_trace()
inp_shape = self.input_tensor.get_shape().as_list()
if self.pad == 'SAME':
output_shape = tf.stack([
self.batch_size, inp_shape[1] * self.stride_size,
inp_shape[1] * self.stride_size, self.output_depth
])
elif self.pad == 'VALID':
output_shape = tf.stack([
self.batch_size,
(inp_shape[1] - 1) * self.stride_size + self.kernel_size,
(inp_shape[2] - 1) * self.stride_size + self.kernel_size,
self.output_depth
])
self.weights_shape = [
self.kernel_size, self.kernel_size, self.output_depth,
self.input_depth
]
self.strides = [1, self.stride_size, self.stride_size, 1]
with tf.variable_scope(self.name):
self.weights = variables.weights(self.weights_shape,
initializer=self.weights_init,
name=self.name)
self.biases = variables.biases(self.output_depth,
initializer=self.bias_init,
name=self.name)
with tf.name_scope(self.name):
#pdb.set_trace()
#deconv = tf.nn.atrous_conv2d(self.input_tensor, self.weights, rate=2, padding='SAME')
deconv = tf.nn.conv2d_transpose(self.input_tensor,
self.weights,
output_shape=output_shape,
strides=self.strides,
padding=self.pad)
deconv = tf.reshape(tf.nn.bias_add(deconv, self.biases),
[-1] + deconv.get_shape().as_list()[1:])
if isinstance(self.act, str):
self.activations = activations.apply(deconv, self.act)
elif hasattr(self.act, '__call__'):
self.activations = self.act(conv)
if self.keep_prob < 1.0:
self.activations = tf.nn.dropout(self.activations,
keep_prob=self.keep_prob)
tf.summary.histogram('activations', self.activations)
return self.activations
def forward(self, input_tensor):
self.input_tensor = input_tensor
#pdb.set_trace()
self.check_input_shape()
self.in_N, self.in_h, self.in_w, self.in_depth = self.input_tensor.get_shape(
).as_list()
# init weights
self.weights_shape = [
self.kernel_size, self.kernel_size, self.in_depth,
self.output_depth
]
self.strides = [1, self.stride_size, self.stride_size, 1]
with tf.name_scope(self.name):
self.weights = variables.weights(self.weights_shape,
initializer=self.weights_init,
name=self.name)
self.biases = variables.biases(self.output_depth,
initializer=self.bias_init,
name=self.name)
with tf.name_scope(self.name):
conv = tf.nn.conv2d(self.input_tensor,
self.weights,
strides=self.strides,
padding=self.pad)
conv = tf.reshape(tf.nn.bias_add(conv, self.biases),
conv.get_shape().as_list())
if self.batch_norm:
self.momentum = self.batch_norm_params['momentum']
self.epsilon = self.batch_norm_params['epsilon']
self.training = self.batch_norm_params['training']
self.bn_name = self.batch_norm_params['name']
conv = tf.contrib.layers.batch_norm(conv,
decay=self.momentum,
updates_collections=None,
epsilon=self.epsilon,
scale=True,
is_training=self.training,
scope=self.bn_name)
if isinstance(self.act, str):
self.activations = activations.apply(conv, self.act)
elif hasattr(self.act, '__call__'):
self.activations = self.act(conv)
# if self.keep_prob<1.0:
# self.activations = tf.nn.dropout(self.activations, keep_prob=self.keep_prob)
tf.summary.histogram('activations', self.activations)
tf.summary.histogram('weights', self.weights)
tf.summary.histogram('biases', self.biases)
return self.activations
def forward(self, input_tensor):
self.input_tensor = input_tensor
inp_shape = self.input_tensor.get_shape().as_list()
#import pdb;pdb.set_trace()
if len(inp_shape) != 2:
import numpy as np
self.input_dim = np.prod(inp_shape[1:])
self.input_tensor = tf.reshape(self.input_tensor,
[inp_shape[0], self.input_dim])
else:
self.input_dim = inp_shape[1]
self.weights_shape = [self.input_dim, self.output_dim]
#with tf.name_scope(self.name):
self.weights = variables.weights(self.weights_shape,
initializer=self.weights_init,
name=self.name)
self.biases = variables.biases(self.output_dim,
initializer=self.bias_init,
name=self.name)
#import pdb;pdb.set_trace()
with tf.name_scope(self.name):
linear = tf.nn.bias_add(tf.matmul(self.input_tensor, self.weights),
self.biases,
name=self.name)
if isinstance(self.act, str):
self.activations = activations.apply(linear, self.act)
elif hasattr(self.act, '__call__'):
self.activations = self.act(conv)
def dropout_check_false():
#print('Dropout adjusted 1.0')
return tf.constant(1.0)
def dropout_check_true():
return tf.multiply(self.keep_prob, 1)
dropout_check = self.keep_prob <= tf.constant(1.0)
#import pdb; pdb.set_trace()
dropout = tf.cond(dropout_check, dropout_check_true,
dropout_check_false)
self.activations = tf.nn.dropout(self.activations,
keep_prob=dropout)
#activations = activation_fn(conv, name='activation')
tf.summary.histogram('activations', self.activations)
tf.summary.histogram('weights', self.weights)
tf.summary.histogram('biases', self.biases)
return self.activations
def forward(self, input_tensor):
self.input_tensor = input_tensor
inp_shape = self.input_tensor.get_shape().as_list()
#import pdb;pdb.set_trace()
if len(inp_shape) != 2:
import numpy as np
self.input_dim = np.prod(inp_shape[1:])
self.input_tensor = tf.reshape(self.input_tensor,
[inp_shape[0], self.input_dim])
else:
self.input_dim = inp_shape[1]
self.weights_shape = [self.input_dim, self.output_dim]
#with tf.name_scope(self.name):
self.weights = variables.weights(self.weights_shape, name=self.name)
self.biases = variables.biases(self.output_dim, name=self.name)
#import pdb;pdb.set_trace()
with tf.name_scope(self.name):
linear = tf.nn.bias_add(tf.matmul(self.input_tensor, self.weights),
self.biases,
name=self.name)
if isinstance(self.act, str):
self.activations = activations.apply(linear, self.act)
elif hasattr(self.act, '__call__'):
self.activations = self.act(conv)
if self.keep_prob < 1.0:
self.activations = tf.nn.dropout(self.activations,
keep_prob=self.keep_prob)
#activations = activation_fn(conv, name='activation')
tf.summary.histogram('activations', self.activations)
tf.summary.histogram('weights', self.weights)
tf.summary.histogram('biases', self.biases)
return self.activations
def forward(self, input_tensor):
#input tensor shape=(samples, feturemap_h, featuremap_w, channels)
#get input sensor and its dimensions
self.input_tensor = input_tensor
inp_shape = self.input_tensor.get_shape().as_list()
#import pdb;pdb.set_trace()
#if input shape diferente from 2 (a tensor) input tensor its reshaped as [batchsize, inputs]
#this is meant to flatten incoming tensors!!!!!!!!
if len(inp_shape) != 2:
#import numpy as np
#get number of flat inputs
self.input_dim = np.prod(inp_shape[1:])
#9/10 changed inp_shape[0] to -1
self.input_tensor = tf.reshape(self.input_tensor,
[-1, self.input_dim])
#if they are already flat define dimension of input
else:
self.input_dim = inp_shape[1]
self.weights_shape = [self.input_dim, self.output_dim]
#with tf.name_scope(self.name):
with tf.name_scope(self.name):
#initialice random weights and biases
with tf.name_scope(self.name + '_params'):
if self.param_dir == None:
if self.init_DH:
self.weights_init = tf.truncated_normal_initializer(
stddev=np.sqrt(2 / (self.input_dim)))
self.bias_init = tf.constant_initializer(0.0)
#call variables.py and set parameters
self.weights = variables.weights(
self.weights_shape,
initializer=self.weights_init,
name='weights')
self.biases = variables.biases(self.output_dim,
initializer=self.bias_init,
name='biases')
#CHECK!!!!!!!!!!!!!!1 Correctly
#CHECK how to correctly pass weights to method
#9/10 tf.Variable() is added
#9/10 change from txt to np.load, see if a try can be added
else:
#self.biases = tf.Variable(tf.convert_to_tensor(np.loadtxt(self.param_dir+'-B.txt'), np.float32))
#self.weights = tf.Variable(tf.convert_to_tensor(np.loadtxt(self.param_dir+'-W.txt'), np.float32))
self.biases = tf.Variable(tf.convert_to_tensor(
np.load(self.param_dir + '-B.npy'), np.float32),
name='biases')
self.weights = tf.Variable(tf.convert_to_tensor(
np.load(self.param_dir + '-W.npy'), np.float32),
name='weights')
#WHAWT this for?
#create instance in graph model?
#with tf.name_scope(self.name):
#SCORES before activations
linear = tf.nn.bias_add(tf.matmul(self.input_tensor, self.weights),
self.biases,
name=self.name)
#IF attribute act is a string aplly activation function
#if isinstance(self.act, str):
#call activation from activations.py
#self.activations = activations.apply(linear, self.act)
if self.act == 'relu':
self.activations = tf.nn.relu(linear)
elif self.act == 'lrelu':
self.activations = tf.maximum(linear, 0.01 * linear)
else:
self.activations = linear
# Dropout
if self.use_dropout:
self.activations = tf.nn.dropout(self.activations,
keep_prob=self.keep_prob)
#Otherwise call for convolutional
#elif hasattr(self.act, '__call__'):
# self.activations = self.act(conv)
#def dropout_check_false():
#print('Dropout adjusted 1.0')
# return tf.constant(1.0)
#def dropout_check_true():
# return tf.multiply(self.keep_prob, 1)
#dropout_check = self.keep_prob<=tf.constant(1.0)
#extremly rare way to set dropout
#dropout = tf.cond(dropout_check, dropout_check_true, dropout_check_false)
#apply dropout to activations
#9/10 dropout commented
#self.activations = tf.nn.dropout(self.activations, keep_prob=self.keep_prob)
#activations = activation_fn(conv, name='activation')
#tf.summary.histogram('activations', self.activations)
#tf.summary.histogram('weights', self.weights)
#tf.summary.histogram('biases', self.biases)
return self.activations
def forward(self, input_tensor):
self.input_tensor = input_tensor
inp_shape = self.input_tensor.get_shape().as_list()
if len(inp_shape) != 2:
import numpy as np
self.input_dim = np.prod(inp_shape[1:])
self.input_tensor = tf.reshape(self.input_tensor,
[inp_shape[0], self.input_dim])
else:
self.input_dim = inp_shape[1]
self.weights_shape = [self.input_dim, self.output_dim]
with tf.name_scope(self.name):
self.weights = variables.weights(self.weights_shape,
initializer=self.weights_init,
name=self.name)
self.biases = variables.biases(self.output_dim,
initializer=self.bias_init,
name=self.name)
with tf.name_scope(self.name):
linear = tf.nn.bias_add(tf.matmul(self.input_tensor, self.weights),
self.biases,
name=self.name)
if self.batch_norm:
self.momentum = self.batch_norm_params['momentum']
self.epsilon = self.batch_norm_params['epsilon']
self.training = self.batch_norm_params['training']
self.bn_name = self.batch_norm_params['name']
linear = tf.contrib.layers.batch_norm(
linear,
decay=self.momentum,
updates_collections=None,
epsilon=self.epsilon,
scale=True,
is_training=self.training,
scope=self.bn_name)
if isinstance(self.act, str):
self.activations = activations.apply(linear, self.act)
elif hasattr(self.act, '__call__'):
self.activations = self.act(conv)
def dropout_check_false():
#print('Dropout adjusted 1.0')
return tf.constant(1.0)
def dropout_check_true():
return tf.multiply(self.keep_prob, 1)
# dropout_check = self.keep_prob<=tf.constant(1.0)
# dropout = tf.cond(dropout_check, dropout_check_true, dropout_check_false)
# self.activations = tf.nn.dropout(self.activations, keep_prob=dropout)
#activations = activation_fn(conv, name='activation')
tf.summary.histogram('activations', self.activations)
tf.summary.histogram('weights', self.weights)
tf.summary.histogram('biases', self.biases)
return self.activations
def forward(self, input_tensor):
self.input_tensor = input_tensor
self.batch_size = tf.shape(self.input_tensor)[0]
self.input_dim = tf.shape(self.input_tensor)[1]
self.input_depth = tf.shape(self.input_tensor)[3]
#pdb.set_trace()
self.check_input_shape()
#input images, featuremap height, feturemap width, num_input_channels
#_
self.in_N, self.in_h, self.in_w, self.in_depth = self.input_tensor.get_shape(
).as_list()
self.in_N = tf.shape(self.input_tensor)[0]
# init weights
self.weights_shape = [
self.kernel_size, self.kernel_size, self.in_depth,
self.output_depth
]
self.strides = [1, self.stride_size, self.stride_size, 1]
with tf.name_scope(self.name):
#initialice random weights and biases
with tf.name_scope(self.name + '_params'):
if self.param_dir == None:
if self.init_DH:
self.weights_init = tf.truncated_normal_initializer(
stddev=np.sqrt(2 /
(self.input_dim * self.input_dim *
self.input_depth)))
self.bias_init = tf.constant_initializer(0.0)
#call variables.py and set parameters
self.weights = variables.weights(
self.weights_shape,
initializer=self.weights_init,
name='weights')
self.biases = variables.biases(self.output_depth,
initializer=self.bias_init,
name='biases')
#CHECK!!!!!!!!!!!!!!1 Coorectly!
#CHECK how to correctly pass weights to method
#9/10 tf.Variable() is added
else:
#self.biases = tf.Variable(tf.convert_to_tensor(np.loadtxt(self.param_dir+'-B.txt'), np.float32))
#self.weights = tf.Variable(tf.convert_to_tensor(np.loadtxt(self.param_dir+'-W.txt'), np.float32))
self.biases = tf.Variable(tf.convert_to_tensor(
np.load(self.param_dir + '-B.npy'), np.float32),
name='biases')
self.weights = tf.Variable(tf.convert_to_tensor(
np.load(self.param_dir + '-W.npy'), np.float32),
name='weights')
#with tf.name_scope(self.name):
#print("hola")
conv = tf.nn.conv2d(self.input_tensor,
self.weights,
strides=self.strides,
padding=self.pad)
conv += self.biases
self.conv = conv
#CHECK this reshape (after all working) simple conv +=biases
#conv = tf.reshape(tf.nn.bias_add(conv, self.biases), conv.get_shape().as_list())
# if isinstance(self.act, str):
# self.activations = activations.apply(conv, self.act)
# elif hasattr(self.act, '__call__'):
# self.activations = self.act(conv)
if self.act == 'relu':
self.activations = tf.nn.relu(conv)
elif self.act == 'lrelu':
self.activations = tf.maximum(self.conv, 0.01 * self.conv)
else:
self.activations = conv
# if self.keep_prob<1.0:
# self.activations = tf.nn.dropout(self.activations, keep_prob=self.keep_prob)
#dont know if i want them
#tf.summary.histogram('activations', self.activations)
tf.summary.histogram('weights', self.weights)
tf.summary.histogram('biases', self.biases)
return self.activations