Python tensorflow.python.ops.init_ops.glorot_uniform_initializer() Examples
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code examples of tensorflow.python.ops.init_ops.glorot_uniform_initializer().
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tensorflow.python.ops.init_ops
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Example #1
| Source File: variable_scope.py From lambda-packs with MIT License | 5 votes |
|
def _get_default_initializer(self, name, shape=None, dtype=dtypes.float32):
"""Provide a default initializer and a corresponding value.
Args:
name: see get_variable.
shape: see get_variable.
dtype: see get_variable.
Returns:
initializer and initializing_from_value. See get_variable above.
Raises:
ValueError: When giving unsupported dtype.
"""
# If dtype is DT_FLOAT, provide a uniform unit scaling initializer
if dtype.is_floating:
initializer = init_ops.glorot_uniform_initializer()
initializing_from_value = False
# If dtype is DT_INT/DT_UINT, provide a default value `zero`
# If dtype is DT_BOOL, provide a default value `FALSE`
elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
initializer = init_ops.zeros_initializer()(
shape=shape, dtype=dtype.base_dtype)
initializing_from_value = True
# NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
else:
raise ValueError("An initializer for variable %s of %s is required"
% (name, dtype.base_dtype))
return initializer, initializing_from_value
# To stop regularization, use this regularizer
Example #2
| Source File: variable_scope.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _get_default_initializer(self, name, shape=None, dtype=dtypes.float32):
"""Provide a default initializer and a corresponding value.
Args:
name: see get_variable.
shape: see get_variable.
dtype: see get_variable.
Returns:
initializer and initializing_from_value. See get_variable above.
Raises:
ValueError: When giving unsupported dtype.
"""
# If dtype is DT_FLOAT, provide a uniform unit scaling initializer
if dtype.is_floating:
initializer = init_ops.glorot_uniform_initializer()
initializing_from_value = False
# If dtype is DT_INT/DT_UINT, provide a default value `zero`
# If dtype is DT_BOOL, provide a default value `FALSE`
elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
initializer = init_ops.zeros_initializer()(
shape=shape, dtype=dtype.base_dtype)
initializing_from_value = True
# NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
else:
raise ValueError("An initializer for variable %s of %s is required"
% (name, dtype.base_dtype))
return initializer, initializing_from_value
# To stop regularization, use this regularizer
Example #3
| Source File: transformer_layers.py From nematus with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def __init__(self, vocabulary_size, embedding_size, hidden_size, float_dtype, name):
# Set arguments
self.vocabulary_size = vocabulary_size
self.hidden_size = hidden_size
self.float_dtype = float_dtype
self.name = name
# Create embedding matrix and its transposes
with tf.compat.v1.variable_scope(self.name):
self.embedding_table = tf.compat.v1.get_variable(name='embedding_table',
shape=[vocabulary_size, embedding_size],
dtype=float_dtype,
initializer=glorot_uniform_initializer(),
trainable=True)
self.projection_matrix = tf.transpose(a=self.embedding_table, name='vocab_projection_matrix')
Example #4
| Source File: rnn.py From seq2seq with Apache License 2.0 | 5 votes |
|
def __init__(self, cell_size):
self.cell_size = cell_size
self.default_initializer = tf.get_variable_scope().initializer or init_ops.glorot_uniform_initializer()
self.initializer = tf.orthogonal_initializer()
Example #5
| Source File: nvcnn.py From dlcookbook-dlbs with Apache License 2.0 | 5 votes |
|
def _get_variable(self, name, shape, dtype=None,
initializer=None, seed=None):
if dtype is None:
dtype = self.dtype
if initializer is None:
initializer = init_ops.glorot_uniform_initializer(seed=seed)
elif (isinstance(initializer, float) or
isinstance(initializer, int)):
initializer = tf.constant_initializer(float(initializer))
return tf.get_variable(name, shape, dtype, initializer)
Example #6
| Source File: variable_scope.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _get_default_initializer(self, name, shape=None, dtype=dtypes.float32):
"""Provide a default initializer and a corresponding value.
Args:
name: see get_variable.
shape: see get_variable.
dtype: see get_variable.
Returns:
initializer and initializing_from_value. See get_variable above.
Raises:
ValueError: When giving unsupported dtype.
"""
# If dtype is DT_FLOAT, provide a uniform unit scaling initializer
if dtype.is_floating:
initializer = init_ops.glorot_uniform_initializer()
initializing_from_value = False
# If dtype is DT_INT/DT_UINT, provide a default value `zero`
# If dtype is DT_BOOL, provide a default value `FALSE`
elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
initializer = init_ops.zeros_initializer()(
shape=shape, dtype=dtype.base_dtype)
initializing_from_value = True
# NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
else:
raise ValueError("An initializer for variable %s of %s is required"
% (name, dtype.base_dtype))
return initializer, initializing_from_value
# To stop regularization, use this regularizer
Example #7
| Source File: variable_scope.py From keras-lambda with MIT License | 5 votes |
|
def _get_default_initializer(self, name, shape=None, dtype=dtypes.float32):
"""Provide a default initializer and a corresponding value.
Args:
name: see get_variable.
shape: see get_variable.
dtype: see get_variable.
Returns:
initializer and initializing_from_value. See get_variable above.
Raises:
ValueError: When giving unsupported dtype.
"""
# If dtype is DT_FLOAT, provide a uniform unit scaling initializer
if dtype.is_floating:
initializer = init_ops.glorot_uniform_initializer()
initializing_from_value = False
# If dtype is DT_INT/DT_UINT, provide a default value `zero`
# If dtype is DT_BOOL, provide a default value `FALSE`
elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
initializer = init_ops.zeros_initializer()(
shape=shape, dtype=dtype.base_dtype)
initializing_from_value = True
# NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
else:
raise ValueError("An initializer for variable %s of %s is required"
% (name, dtype.base_dtype))
return initializer, initializing_from_value
# To stop regularization, use this regularizer
Example #8
| Source File: transformer_attention_modules.py From nematus with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def __init__(self,
reference_dims,
hypothesis_dims,
hidden_dims,
float_dtype,
dropout_attn,
training,
name,
attn_type='multiplicative'):
# Declare attributes
self.reference_dims = reference_dims
self.hypothesis_dims = hypothesis_dims
self.hidden_dims = hidden_dims
self.float_dtype = float_dtype
self.attn_type = attn_type
self.training = training
self.name = name
assert attn_type in ['additive', 'multiplicative'], 'Attention type {:s} is not supported.'.format(attn_type)
if dropout_attn > 0:
self.dropout_attn = tf.keras.layers.Dropout(rate=dropout_attn)
else:
self.dropout_attn = None
# Instantiate parameters
with tf.compat.v1.variable_scope(self.name):
self.queries_projection = None
self.attn_weight = None
if attn_type == 'additive':
self.queries_projection = FeedForwardLayer(self.hypothesis_dims,
self.hidden_dims,
float_dtype,
dropout_rate=0.,
activation=None,
use_bias=False,
use_layer_norm=False,
training=self.training,
name='queries_projection')
self.attn_weight = tf.compat.v1.get_variable(name='attention_weight',
shape=self.hidden_dims,
dtype=float_dtype,
initializer=glorot_uniform_initializer(),
trainable=True)
self.keys_projection = FeedForwardLayer(self.reference_dims,
self.hidden_dims,
float_dtype,
dropout_rate=0.,
activation=None,
use_bias=False,
use_layer_norm=False,
training=self.training,
name='keys_projection')
Example #9
| Source File: transformer_layers.py From nematus with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def __init__(self,
in_size,
out_size,
float_dtype,
dropout_rate,
activation,
use_bias,
use_layer_norm,
training,
name):
# Set attributes
self.in_size = in_size
self.out_size = out_size
self.dropout_rate = dropout_rate
self.activation = activation
self.use_bias = use_bias
self.training = training
self.name = name
with tf.compat.v1.variable_scope(self.name):
# Set up layer normalization
if use_layer_norm:
self.layer_norm_layer = LayerNormLayer(out_size)
else:
self.layer_norm_layer = None
if dropout_rate > 0:
self.dropout = tf.keras.layers.Dropout(rate=dropout_rate)
else:
self.dropout = None
# Define parameters
weights_shape = [in_size, out_size] if out_size is not None else [in_size]
self.weights = tf.compat.v1.get_variable(name='dense_layer_weights',
shape=weights_shape,
dtype=float_dtype,
initializer=glorot_uniform_initializer(),
trainable=True)
if use_bias:
biases_shape = [out_size] if out_size is not None else [in_size]
self.biases = tf.compat.v1.get_variable(name='dense_layer_biases',
shape=biases_shape,
dtype=float_dtype,
initializer=tf.zeros_initializer(),
trainable=True)
