def _declare_variables(self):
with tf.variable_scope('vfeedbacknet_model1'):
with tf.variable_scope('convlstm1'):
with tf.variable_scope('rnn'):
with tf.variable_scope('conv_lstm_cell'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
n = 512
m = 4*n
input_size = [7, 7, n]
kernel2d_size = [3, 3]
kernel_size = kernel2d_size + [2*n] + [m]
with tf.variable_scope('convlstm'):
kernel = tf.get_variable('kernel', kernel_size, initializer=initializer, regularizer=regularizer)
W_ci = tf.get_variable('W_ci', input_size, initializer=initializer, regularizer=regularizer)
W_cf = tf.get_variable('W_cf', input_size, initializer=initializer, regularizer=regularizer)
W_co = tf.get_variable('W_co', input_size, initializer=initializer, regularizer=regularizer)
bias = tf.get_variable('bias', [m], initializer=tf.zeros_initializer(), regularizer=regularizer)
self.convLSTMCell1 = ConvLSTMCell([7, 7], 512, [3, 3])
with tf.variable_scope('convlstm2'):
with tf.variable_scope('rnn'):
with tf.variable_scope('conv_lstm_cell'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
n = 512
m = 4*n
input_size = [7, 7, n]
kernel2d_size = [3, 3]
kernel_size = kernel2d_size + [2*n] + [m]
with tf.variable_scope('convlstm'):
kernel = tf.get_variable('kernel', kernel_size, initializer=initializer, regularizer=regularizer)
W_ci = tf.get_variable('W_ci', input_size, initializer=initializer, regularizer=regularizer)
W_cf = tf.get_variable('W_cf', input_size, initializer=initializer, regularizer=regularizer)
W_co = tf.get_variable('W_co', input_size, initializer=initializer, regularizer=regularizer)
bias = tf.get_variable('bias', [m], initializer=tf.zeros_initializer(), regularizer=regularizer)
self.convLSTMCell2 = ConvLSTMCell([7, 7], 512, [3, 3])
def _declare_variables(self):
with tf.variable_scope('vfeedbacknet_{}'.format(Model.model_name)):
# with tf.variable_scope('process_featurizer_output'):
# with tf.variable_scope('conv1'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# kernel = tf.get_variable('kernel', shape=[3, 3, 512, 512], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
# biases = tf.get_variable('biases', shape=[512], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
with tf.variable_scope('convlstm1'):
with tf.variable_scope('rnn'):
with tf.variable_scope('conv_lstm_cell'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
n = 512
m = 4 * n
input_size = [7, 7, n]
kernel2d_size = [3, 3]
kernel_size = kernel2d_size + [2 * n] + [m]
with tf.variable_scope('convlstm'):
kernel = tf.get_variable('kernel',
kernel_size,
initializer=initializer,
regularizer=regularizer)
W_ci = tf.get_variable('W_ci',
input_size,
initializer=initializer,
regularizer=regularizer)
W_cf = tf.get_variable('W_cf',
input_size,
initializer=initializer,
regularizer=regularizer)
W_co = tf.get_variable('W_co',
input_size,
initializer=initializer,
regularizer=regularizer)
bias = tf.get_variable(
'bias', [m],
initializer=tf.zeros_initializer(),
regularizer=regularizer)
self.convLSTMCell1 = ConvLSTMCell(input_size[:2], n, [3, 3])
# with tf.variable_scope('convlstm2'):
# with tf.variable_scope('rnn'):
# with tf.variable_scope('conv_lstm_cell'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# n = 512
# m = 4*n
# input_size = [7, 7, n]
# kernel2d_size = [3, 3]
# kernel_size = kernel2d_size + [2*n] + [m]
# with tf.variable_scope('convlstm'):
# kernel = tf.get_variable('kernel', kernel_size, initializer=initializer, regularizer=regularizer)
# W_ci = tf.get_variable('W_ci', input_size, initializer=initializer, regularizer=regularizer)
# W_cf = tf.get_variable('W_cf', input_size, initializer=initializer, regularizer=regularizer)
# W_co = tf.get_variable('W_co', input_size, initializer=initializer, regularizer=regularizer)
# bias = tf.get_variable('bias', [m], initializer=tf.zeros_initializer(), regularizer=regularizer)
# self.convLSTMCell2 = ConvLSTMCell(input_size[:2], n, [3, 3])
with tf.variable_scope('reshape_convs'):
with tf.variable_scope('conv1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel = tf.get_variable('kernel',
shape=[3, 3, 256, 512],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
biases = tf.get_variable('biases',
shape=[512],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
# with tf.variable_scope('conv2'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# kernel = tf.get_variable('kernel', shape=[3, 3, 512, 1024], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
# biases = tf.get_variable('biases', shape=[1024], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
# with tf.variable_scope('conv3'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# kernel = tf.get_variable('kernel', shape=[3, 3, 512, 1024], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
# biases = tf.get_variable('biases', shape=[1024], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
with tf.variable_scope('feedback_block1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
input_size = [7, 7]
kernel_size = [3, 3, 512, 512]
W_xf = tf.get_variable('W_xf',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_xi = tf.get_variable('W_xi',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_xc = tf.get_variable('W_xc',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_xo = tf.get_variable('W_xo',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_hf = tf.get_variable('W_hf',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_hi = tf.get_variable('W_hi',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_hc = tf.get_variable('W_hc',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_ho = tf.get_variable('W_ho',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_cf = tf.get_variable(
'W_cf', [input_size[0], input_size[1], kernel_size[-1]],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_ci = tf.get_variable(
'W_ci', [input_size[0], input_size[1], kernel_size[-1]],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_co = tf.get_variable(
'W_co', [input_size[0], input_size[1], kernel_size[-1]],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
b_f = tf.get_variable('b_f', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
b_i = tf.get_variable('b_i', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
b_c = tf.get_variable('b_c', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
b_o = tf.get_variable('b_o', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
# with tf.variable_scope('feedback_block2'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# input_size = [14, 14]
# kernel_size = [3, 3, 256, 256]
# W_xf = tf.get_variable('W_xf', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_xi = tf.get_variable('W_xi', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_xc = tf.get_variable('W_xc', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_xo = tf.get_variable('W_xo', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_hf = tf.get_variable('W_hf', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_hi = tf.get_variable('W_hi', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_hc = tf.get_variable('W_hc', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_ho = tf.get_variable('W_ho', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_cf = tf.get_variable('W_cf', [input_size[0],input_size[1],kernel_size[-1]], dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_ci = tf.get_variable('W_ci', [input_size[0],input_size[1],kernel_size[-1]], dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_co = tf.get_variable('W_co', [input_size[0],input_size[1],kernel_size[-1]], dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# b_f = tf.get_variable('b_f', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
# b_i = tf.get_variable('b_i', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
# b_c = tf.get_variable('b_c', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
# b_o = tf.get_variable('b_o', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
# with tf.variable_scope('feedback_block3'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# input_size = [7, 7]
# kernel_size = [3, 3, 512, 512]
# W_xf = tf.get_variable('W_xf', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_xi = tf.get_variable('W_xi', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_xc = tf.get_variable('W_xc', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_xo = tf.get_variable('W_xo', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_hf = tf.get_variable('W_hf', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_hi = tf.get_variable('W_hi', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_hc = tf.get_variable('W_hc', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_ho = tf.get_variable('W_ho', kernel_size, dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_cf = tf.get_variable('W_cf', [input_size[0],input_size[1],kernel_size[-1]], dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_ci = tf.get_variable('W_ci', [input_size[0],input_size[1],kernel_size[-1]], dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# W_co = tf.get_variable('W_co', [input_size[0],input_size[1],kernel_size[-1]], dtype=tf.float32, initializer=initializer, regularizer=regularizer)
# b_f = tf.get_variable('b_f', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
# b_i = tf.get_variable('b_i', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
# b_c = tf.get_variable('b_c', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
# b_o = tf.get_variable('b_o', [kernel_size[-1]], dtype=tf.float32, initializer=tf.zeros_initializer(), regularizer=regularizer)
with tf.variable_scope('fc'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
trainable = False if self.train_fc == 'NO' else True
weight = tf.get_variable('weights',
shape=[512, self.num_classes],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
biases = tf.get_variable('biases',
shape=[self.num_classes],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
def _declare_variables(self):
with tf.variable_scope('vfeedbacknet_{}'.format(Model.model_name)):
with tf.variable_scope('feedbackcell1'):
self.feedbackLSTMCell1 = FeedbackLSTMCell_stack1(
[14, 14, 128], Model.NFEEDBACK)
with tf.variable_scope('convlstm1'):
with tf.variable_scope('rnn'):
with tf.variable_scope('conv_lstm_cell'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
n = 128
m = 4 * n
input_size = [7, 7, n]
kernel2d_size = [3, 3]
kernel_size = kernel2d_size + [2 * n] + [m]
with tf.variable_scope('convlstm'):
kernel = tf.get_variable('kernel',
kernel_size,
initializer=initializer,
regularizer=regularizer)
W_ci = tf.get_variable('W_ci',
input_size,
initializer=initializer,
regularizer=regularizer)
W_cf = tf.get_variable('W_cf',
input_size,
initializer=initializer,
regularizer=regularizer)
W_co = tf.get_variable('W_co',
input_size,
initializer=initializer,
regularizer=regularizer)
bias = tf.get_variable(
'bias', [m],
initializer=tf.zeros_initializer(),
regularizer=regularizer)
self.convLSTMCell1 = ConvLSTMCell(input_size[:2], n, [3, 3])
with tf.variable_scope('reshape_convs'):
with tf.variable_scope('conv1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel = tf.get_variable('kernel',
shape=[7, 7, 3, 32],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
biases = tf.get_variable('biases',
shape=[32],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
with tf.variable_scope('conv2'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel = tf.get_variable('kernel',
shape=[3, 3, 32, 64],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
biases = tf.get_variable('biases',
shape=[64],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
with tf.variable_scope('conv3'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel = tf.get_variable('kernel',
shape=[3, 3, 64, 128],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
biases = tf.get_variable('biases',
shape=[128],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
# with tf.variable_scope('conv4'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# kernel = tf.get_variable('kernel', shape=[3, 3, 512, 1024], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
# biases = tf.get_variable('biases', shape=[1024], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
# with tf.variable_scope('conv5'):
# regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
# initializer = tf.contrib.layers.xavier_initializer()
# kernel = tf.get_variable('kernel', shape=[3, 3, 128, 256], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
# biases = tf.get_variable('biases', shape=[256], dtype=tf.float32, regularizer=regularizer, initializer=initializer)
with tf.variable_scope('fc1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
trainable = False if self.train_fc == 'NO' else True
weight = tf.get_variable('weights',
shape=[128, 128],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
biases = tf.get_variable('biases',
shape=[128],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
with tf.variable_scope('fc2'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
trainable = False if self.train_fc == 'NO' else True
weight = tf.get_variable('weights',
shape=[128, self.num_classes],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
biases = tf.get_variable('biases',
shape=[self.num_classes],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
def _declare_variables(self):
with tf.variable_scope('vfeedbacknet_{}'.format(Model.model_name)):
with tf.variable_scope('convlstm1'):
with tf.variable_scope('rnn'):
with tf.variable_scope('conv_lstm_cell'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
n = 256
m = 4 * n
input_size = [14, 14, n]
kernel2d_size = [3, 3]
kernel_size = kernel2d_size + [2 * n] + [m]
with tf.variable_scope('convlstm'):
kernel = tf.get_variable('kernel',
kernel_size,
initializer=initializer,
regularizer=regularizer)
W_ci = tf.get_variable('W_ci',
input_size,
initializer=initializer,
regularizer=regularizer)
W_cf = tf.get_variable('W_cf',
input_size,
initializer=initializer,
regularizer=regularizer)
W_co = tf.get_variable('W_co',
input_size,
initializer=initializer,
regularizer=regularizer)
bias = tf.get_variable(
'bias', [m],
initializer=tf.zeros_initializer(),
regularizer=regularizer)
self.convLSTMCell1 = ConvLSTMCell([14, 14], 256, [3, 3])
with tf.variable_scope('conv1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel = tf.get_variable('kernel',
shape=[14, 14, 256, 512],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
biases = tf.get_variable('biases',
shape=[512],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
with tf.variable_scope('dconv1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel = tf.get_variable('kernel', [3, 3, 128, 256],
initializer=initializer,
regularizer=regularizer)
biases = tf.get_variable('biases', [128],
initializer=initializer,
regularizer=regularizer)
def _declare_variables(self):
with tf.variable_scope('vfeedbacknet_{}'.format(Model.model_name)):
with tf.variable_scope('process_featurizer_output'):
with tf.variable_scope('conv1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel = tf.get_variable('kernel',
shape=[3, 3, 128, 128],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
biases = tf.get_variable('biases',
shape=[128],
dtype=tf.float32,
regularizer=regularizer,
initializer=initializer)
with tf.variable_scope('convlstm1'):
with tf.variable_scope('rnn'):
with tf.variable_scope('conv_lstm_cell'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
n = 128
m = 4 * n
input_size = [14, 14, n]
kernel2d_size = [3, 3]
kernel_size = kernel2d_size + [2 * n] + [m]
with tf.variable_scope('convlstm'):
kernel = tf.get_variable('kernel',
kernel_size,
initializer=initializer,
regularizer=regularizer)
W_ci = tf.get_variable('W_ci',
input_size,
initializer=initializer,
regularizer=regularizer)
W_cf = tf.get_variable('W_cf',
input_size,
initializer=initializer,
regularizer=regularizer)
W_co = tf.get_variable('W_co',
input_size,
initializer=initializer,
regularizer=regularizer)
bias = tf.get_variable(
'bias', [m],
initializer=tf.zeros_initializer(),
regularizer=regularizer)
self.convLSTMCell1 = ConvLSTMCell([14, 14], 128, [3, 3])
with tf.variable_scope('feedback_block1'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
kernel_size = [3, 3, 128, 128]
W_xf = tf.get_variable('W_xf',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_xi = tf.get_variable('W_xi',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_xc = tf.get_variable('W_xc',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_xo = tf.get_variable('W_xo',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_hf = tf.get_variable('W_hf',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_hi = tf.get_variable('W_hi',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_hc = tf.get_variable('W_hc',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_ho = tf.get_variable('W_ho',
kernel_size,
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_cf = tf.get_variable('W_cf', [14, 14, kernel_size[-1]],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_ci = tf.get_variable('W_ci', [14, 14, kernel_size[-1]],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
W_co = tf.get_variable('W_co', [14, 14, kernel_size[-1]],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer)
b_f = tf.get_variable('b_f', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
b_i = tf.get_variable('b_i', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
b_c = tf.get_variable('b_c', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
b_o = tf.get_variable('b_o', [kernel_size[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
regularizer=regularizer)
with tf.variable_scope('fc'):
regularizer = None # tf.contrib.layers.l2_regularizer(scale=0.25)
initializer = tf.contrib.layers.xavier_initializer()
trainable = False if self.train_fc == 'NO' else True
weight = tf.get_variable('weights',
shape=[128, self.num_classes],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
biases = tf.get_variable('biases',
shape=[self.num_classes],
dtype=tf.float32,
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
