Python tensorflow.Varialbe() Examples
The following are 30
code examples of tensorflow.Varialbe().
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Example #1
| Source File: tf_util.py From ASIS with MIT License | 6 votes |
|
def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope, is_dist=False):
""" Batch normalization on 2D convolutional maps.
Args:
inputs: Tensor, 4D BHWC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
is_dist: true indicating distributed training scheme
Return:
normed: batch-normalized maps
"""
if is_dist:
return batch_norm_dist_template(inputs, is_training, scope, [0,1,2], bn_decay)
else:
return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay)
Example #2
| Source File: tf_util.py From GSPN with MIT License | 6 votes |
|
def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
bn_decay = bn_decay if bn_decay is not None else 0.9
return tf.contrib.layers.batch_norm(inputs,
center=True, scale=True,
is_training=is_training, decay=bn_decay,updates_collections=None,
scope=scope, reuse=False,
data_format=data_format)
Example #3
| Source File: tf_util.py From SpiderCNN with MIT License | 6 votes |
|
def batch_norm_template_multiGPU(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
bn_decay = bn_decay if bn_decay is not None else 0.9
return tf.contrib.layers.batch_norm(inputs,
center=True, scale=True,
is_training=is_training, decay=bn_decay,updates_collections=None,
scope=scope,
data_format=data_format)
Example #4
| Source File: tf_util.py From ASIS with MIT License | 6 votes |
|
def batch_norm_for_fc(inputs, is_training, bn_decay, scope, is_dist=False):
""" Batch normalization on FC data.
Args:
inputs: Tensor, 2D BxC input
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
is_dist: true indicating distributed training scheme
Return:
normed: batch-normalized maps
"""
if is_dist:
return batch_norm_dist_template(inputs, is_training, scope, [0,], bn_decay)
else:
return batch_norm_template(inputs, is_training, scope, [0,], bn_decay)
Example #5
| Source File: tf_util_dgcnn.py From AlignNet-3D with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def batch_norm_for_fc(inputs, is_training, bn_decay, scope, is_dist=False):
""" Batch normalization on FC data.
Args:
inputs: Tensor, 2D BxC input
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
is_dist: true indicating distributed training scheme
Return:
normed: batch-normalized maps
"""
if is_dist:
return batch_norm_dist_template(inputs, is_training, scope, [0,], bn_decay)
else:
return batch_norm_template(inputs, is_training, scope, [0,], bn_decay)
Example #6
| Source File: tf_util.py From reading-frustum-pointnets-code with Apache License 2.0 | 6 votes |
|
def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
bn_decay = bn_decay if bn_decay is not None else 0.9
return tf.contrib.layers.batch_norm(inputs,
center=True, scale=True,
is_training=is_training, decay=bn_decay,updates_collections=None,
scope=scope,
data_format=data_format)
Example #7
| Source File: tf_util_dgcnn.py From AlignNet-3D with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope, is_dist=False):
""" Batch normalization on 1D convolutional maps.
Args:
inputs: Tensor, 3D BLC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
is_dist: true indicating distributed training scheme
Return:
normed: batch-normalized maps
"""
if is_dist:
return batch_norm_dist_template(inputs, is_training, scope, [0,1], bn_decay)
else:
return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay)
Example #8
| Source File: tf_util_dgcnn.py From AlignNet-3D with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope, is_dist=False):
""" Batch normalization on 2D convolutional maps.
Args:
inputs: Tensor, 4D BHWC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
is_dist: true indicating distributed training scheme
Return:
normed: batch-normalized maps
"""
if is_dist:
return batch_norm_dist_template(inputs, is_training, scope, [0,1,2], bn_decay)
else:
return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay)
Example #9
| Source File: tf_util_dgcnn.py From AlignNet-3D with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def batch_norm_for_conv3d(inputs, is_training, bn_decay, scope, is_dist=False):
""" Batch normalization on 3D convolutional maps.
Args:
inputs: Tensor, 5D BDHWC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
is_dist: true indicating distributed training scheme
Return:
normed: batch-normalized maps
"""
if is_dist:
return batch_norm_dist_template(inputs, is_training, scope, [0,1,2,3], bn_decay)
else:
return batch_norm_template(inputs, is_training, scope, [0,1,2,3], bn_decay)
Example #10
| Source File: tf_util.py From ASIS with MIT License | 6 votes |
|
def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope, is_dist=False):
""" Batch normalization on 1D convolutional maps.
Args:
inputs: Tensor, 3D BLC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
is_dist: true indicating distributed training scheme
Return:
normed: batch-normalized maps
"""
if is_dist:
return batch_norm_dist_template(inputs, is_training, scope, [0,1], bn_decay)
else:
return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay)
Example #11
| Source File: tf_util.py From SPFN with MIT License | 6 votes |
|
def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
bn_decay = bn_decay if bn_decay is not None else 0.9
return tf.contrib.layers.batch_norm(inputs,
center=True, scale=True,
is_training=is_training, decay=bn_decay,updates_collections=None,
scope=scope,
data_format=data_format)
Example #12
| Source File: tf_util.py From deep-functional-dictionaries with MIT License | 6 votes |
|
def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
bn_decay = bn_decay if bn_decay is not None else 0.9
return tf.contrib.layers.batch_norm(inputs,
center=True, scale=True,
is_training=is_training, decay=bn_decay,updates_collections=None,
scope=scope,
data_format=data_format)
Example #13
| Source File: tf_util.py From Geo-CNN with Apache License 2.0 | 6 votes |
|
def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
bn_decay = bn_decay if bn_decay is not None else 0.9
return tf.contrib.layers.batch_norm(inputs,
center=True, scale=True,
is_training=is_training, decay=bn_decay,updates_collections=None,
scope=scope,
data_format=data_format)
Example #14
| Source File: nn_functions.py From AmusingPythonCodes with MIT License | 6 votes |
|
def batch_normalization(x, out_shape, phase_train):
"""Batch normalization on convolutional maps.
Args:
x: Tensor, 4D Batch-Height-Width-Depth (BHWD) input maps
out_shape: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
Return:
normed: batch-normalized maps
"""
with tf.variable_scope('batch_norm'):
beta = tf.Variable(tf.constant(0.0, shape=[out_shape]), name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=[out_shape]), name='gamma', trainable=True)
batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.5)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train, mean_var_with_update, lambda: (ema.average(batch_mean),
ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
return normed
Example #15
| Source File: architecture.py From DCNets with MIT License | 5 votes |
|
def batch_norm(self, x, n_out, phase_train):
"""
Batch normalization on convolutional maps.
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope('bn'):
gamma = self.get_bias(n_out, 1.0, 'gamma')
beta = self.get_bias(n_out, 0.0, 'beta')
batch_mean, batch_var = tf.nn.moments(x, [0,1,2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.999)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
return tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
Example #16
| Source File: tf_util.py From GSPN with MIT License | 5 votes |
|
def batch_norm_for_fc(inputs, is_training, bn_decay, scope):
""" Batch normalization on FC data.
Args:
inputs: Tensor, 2D BxC input
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,], bn_decay)
Example #17
| Source File: architecture.py From DCNets with MIT License | 5 votes |
|
def xnorm_batch_norm(self, x, phase_train):
"""
Batch normalization on convolutional maps.
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope('xnorm_bn'):
batch_mean = tf.reduce_mean(x, [0,1,2])
ema = tf.train.ExponentialMovingAverage(decay=0.999)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean)
mean = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean)))
return mean
Example #18
| Source File: architecture.py From DCNets with MIT License | 5 votes |
|
def xnorm_batch_norm(self, x, phase_train):
"""
Batch normalization on convolutional maps.
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope('xnorm_bn'):
batch_mean = tf.reduce_mean(x, [0,1,2])
ema = tf.train.ExponentialMovingAverage(decay=0.999)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean)
mean = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean)))
return mean
Example #19
| Source File: architecture.py From DCNets with MIT License | 5 votes |
|
def batch_norm(self, x, n_out, phase_train):
"""
Batch normalization on convolutional maps.
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope('bn'):
gamma = self.get_bias(n_out, 1.0, 'gamma')
beta = self.get_bias(n_out, 0.0, 'beta')
batch_mean, batch_var = tf.nn.moments(x, [0,1,2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.999)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
return tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
Example #20
| Source File: architecture.py From DCNets with MIT License | 5 votes |
|
def xnorm_batch_norm(self, x, phase_train):
"""
Batch normalization on convolutional maps.
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope('xnorm_bn'):
batch_mean = tf.reduce_mean(x, [0,1,2])
ema = tf.train.ExponentialMovingAverage(decay=0.999)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean)
mean = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean)))
return mean
Example #21
| Source File: tf_util.py From Geo-CNN with Apache License 2.0 | 5 votes |
|
def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope, data_format):
""" Batch normalization on 1D convolutional maps.
Args:
inputs: Tensor, 3D BLC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay, data_format)
Example #22
| Source File: tf_util.py From SpiderCNN with MIT License | 5 votes |
|
def batch_norm_template(inputs, is_training, scope, moments_dims, bn_decay):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
Return:
normed: batch-normalized maps
"""
with tf.variable_scope(scope) as sc:
num_channels = inputs.get_shape()[-1].value
beta = tf.Variable(tf.constant(0.0, shape=[num_channels]),
name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=[num_channels]),
name='gamma', trainable=True)
batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments')
decay = bn_decay if bn_decay is not None else 0.9
ema = tf.train.ExponentialMovingAverage(decay=decay)
# Operator that maintains moving averages of variables.
ema_apply_op = tf.cond(is_training,
lambda: ema.apply([batch_mean, batch_var]),
lambda: tf.no_op())
# Update moving average and return current batch's avg and var.
def mean_var_with_update():
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
# ema.average returns the Variable holding the average of var.
mean, var = tf.cond(is_training,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3)
return normed
Example #23
| Source File: architecture.py From MHE with MIT License | 5 votes |
|
def batch_norm(self, x, n_out, phase_train):
"""
Batch normalization on convolutional maps.
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope('bn'):
gamma = self.get_bias(n_out, 1.0, 'gamma')
beta = self.get_bias(n_out, 0.0, 'beta')
batch_mean, batch_var = tf.nn.moments(x, [0,1,2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.999)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
return tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
Example #24
| Source File: tf_util.py From pointnetvlad with MIT License | 5 votes |
|
def batch_norm_for_conv3d(inputs, is_training, bn_decay, scope):
""" Batch normalization on 3D convolutional maps.
Args:
inputs: Tensor, 5D BDHWC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,1,2,3], bn_decay)
Example #25
| Source File: tf_util.py From pointnetvlad with MIT License | 5 votes |
|
def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope):
""" Batch normalization on 2D convolutional maps.
Args:
inputs: Tensor, 4D BHWC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay)
Example #26
| Source File: tf_util.py From pointnetvlad with MIT License | 5 votes |
|
def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope):
""" Batch normalization on 1D convolutional maps.
Args:
inputs: Tensor, 3D BLC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay)
Example #27
| Source File: tf_util.py From pointnetvlad with MIT License | 5 votes |
|
def batch_norm_for_fc(inputs, is_training, bn_decay, scope):
""" Batch normalization on FC data.
Args:
inputs: Tensor, 2D BxC input
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,], bn_decay)
Example #28
| Source File: tf_util.py From pointnetvlad with MIT License | 5 votes |
|
def batch_norm_template(inputs, is_training, scope, moments_dims, bn_decay):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
Return:
normed: batch-normalized maps
"""
with tf.variable_scope(scope) as sc:
num_channels = inputs.get_shape()[-1].value
beta = tf.Variable(tf.constant(0.0, shape=[num_channels]),
name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=[num_channels]),
name='gamma', trainable=True)
batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments')
decay = bn_decay if bn_decay is not None else 0.9
ema = tf.train.ExponentialMovingAverage(decay=decay)
# Operator that maintains moving averages of variables.
ema_apply_op = tf.cond(is_training,
lambda: ema.apply([batch_mean, batch_var]),
lambda: tf.no_op())
# Update moving average and return current batch's avg and var.
def mean_var_with_update():
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
# ema.average returns the Variable holding the average of var.
mean, var = tf.cond(is_training,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3)
return normed
Example #29
| Source File: tf_util.py From Geo-CNN with Apache License 2.0 | 5 votes |
|
def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope, data_format):
""" Batch normalization on 2D convolutional maps.
Args:
inputs: Tensor, 4D BHWC input maps
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
data_format: 'NHWC' or 'NCHW'
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay, data_format)
Example #30
| Source File: nn_layers.py From DeepLoc with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def batch_norm_fc(x, n_out, phase_train, scope='bn'):
"""
Batch normalization on convolutional maps.
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope(scope):
beta = tf.Variable(tf.constant(0.0, shape=[n_out]),
name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=[n_out]),
name='gamma', trainable=True)
batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.5)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
return normed
