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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# pylint: disable=unused-import,g-bad-import-order
"""Contains the core layers: Dense, Dropout.
Also contains their functional aliases.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from six.moves import xrange # pylint: disable=redefined-builtin
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.layers import base
from tensorflow.python.layers import utils
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import standard_ops
from tensorflow.python.util.tf_export import tf_export
@tf_export('layers.Dense')
class Dense(base.Layer):
"""Densely-connected layer class.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
Where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Arguments:
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer. Layers with the same name will
share weights, but to avoid mistakes we require reuse=True in such cases.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Properties:
units: Python integer, dimensionality of the output space.
activation: Activation function (callable).
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer instance (or name) for the kernel matrix.
bias_initializer: Initializer instance (or name) for the bias.
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
bias_regularizer: Regularizer instance for the bias (callable).
activity_regularizer: Regularizer instance for the output (callable)
kernel_constraint: Constraint function for the kernel matrix.
bias_constraint: Constraint function for the bias.
kernel: Weight matrix (TensorFlow variable or tensor).
bias: Bias vector, if applicable (TensorFlow variable or tensor).
"""
def __init__(self, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Dense, self).__init__(trainable=trainable, name=name,
activity_regularizer=activity_regularizer,
**kwargs)
self.units = units
self.activation = activation
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
self.kernel_constraint = kernel_constraint
self.bias_constraint = bias_constraint
self.input_spec = base.InputSpec(min_ndim=2)
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if input_shape[-1].value is None:
raise ValueError('The last dimension of the inputs to `Dense` '
'should be defined. Found `None`.')
self.input_spec = base.InputSpec(min_ndim=2,
axes={-1: input_shape[-1].value})
self.kernel = self.add_variable('kernel',
shape=[input_shape[-1].value, self.units],
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
dtype=self.dtype,
trainable=True)
if self.use_bias:
self.bias = self.add_variable('bias',
shape=[self.units,],
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
dtype=self.dtype,
trainable=True)
else:
self.bias = None
self.built = True
def call(self, inputs):
inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
shape = inputs.get_shape().as_list()
if len(shape) > 2:
# Broadcasting is required for the inputs.
outputs = standard_ops.tensordot(inputs, self.kernel, [[len(shape) - 1],
[0]])
# Reshape the output back to the original ndim of the input.
if not context.executing_eagerly():
output_shape = shape[:-1] + [self.units]
outputs.set_shape(output_shape)
else:
outputs = gen_math_ops.mat_mul(inputs, self.kernel)
if self.use_bias:
outputs = nn.bias_add(outputs, self.bias)
if self.activation is not None:
return self.activation(outputs) # pylint: disable=not-callable
return outputs
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
input_shape = input_shape.with_rank_at_least(2)
if input_shape[-1].value is None:
raise ValueError(
'The innermost dimension of input_shape must be defined, but saw: %s'
% input_shape)
return input_shape[:-1].concatenate(self.units)
@tf_export('layers.dense')
def dense(
inputs, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the densely-connected layer.
This layer implements the operation:
`outputs = activation(inputs.kernel + bias)`
Where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Arguments:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor the same shape as `inputs` except the last dimension is of
size `units`.
Raises:
ValueError: if eager execution is enabled.
"""
layer = Dense(units,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
dtype=inputs.dtype.base_dtype,
_scope=name,
_reuse=reuse)
return layer.apply(inputs)
@tf_export('layers.Dropout')
class Dropout(base.Layer):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}.
for behavior.
name: The name of the layer (string).
"""
def __init__(self, rate=0.5,
noise_shape=None,
seed=None,
name=None,
**kwargs):
super(Dropout, self).__init__(name=name, **kwargs)
self.rate = rate
self.noise_shape = noise_shape
self.seed = seed
def _get_noise_shape(self, inputs):
# Subclasses of `Dropout` may implement `_get_noise_shape(self, inputs)`,
# which will override `self.noise_shape`, and allows for custom noise
# shapes with dynamically sized inputs.
if self.noise_shape is None:
return self.noise_shape
return nn_ops._get_noise_shape(inputs, self.noise_shape)
def call(self, inputs, training=False):
def dropped_inputs():
return nn.dropout(inputs, 1 - self.rate,
noise_shape=self._get_noise_shape(inputs),
seed=self.seed)
return utils.smart_cond(training,
dropped_inputs,
lambda: array_ops.identity(inputs))
def compute_output_shape(self, input_shape):
return input_shape
@tf_export('layers.dropout')
def dropout(inputs,
rate=0.5,
noise_shape=None,
seed=None,
training=False,
name=None):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
inputs: Tensor input.
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(apply dropout) or in inference mode (return the input untouched).
name: The name of the layer (string).
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
return layer.apply(inputs, training=training)
@tf_export('layers.Flatten')
class Flatten(base.Layer):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Examples:
```
x = tf.placeholder(shape=(None, 4, 4), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, 16)`
x = tf.placeholder(shape=(None, 3, None), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, None)`
```
"""
def __init__(self, **kwargs):
super(Flatten, self).__init__(**kwargs)
self.input_spec = base.InputSpec(min_ndim=2)
def call(self, inputs):
outputs = array_ops.reshape(inputs, (array_ops.shape(inputs)[0], -1))
if not context.executing_eagerly():
outputs.set_shape(self.compute_output_shape(inputs.get_shape()))
return outputs
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
output_shape = [input_shape[0]]
if all(input_shape[1:]):
output_shape += [np.prod(input_shape[1:])]
else:
output_shape += [None]
return tensor_shape.TensorShape(output_shape)
@tf_export('layers.flatten')
def flatten(inputs, name=None):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Arguments:
inputs: Tensor input.
name: The name of the layer (string).
Returns:
Reshaped tensor.
Examples:
```
x = tf.placeholder(shape=(None, 4, 4), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, 16)`
x = tf.placeholder(shape=(None, 3, None), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, None)`
```
"""
layer = Flatten(name=name)
return layer.apply(inputs)
# Aliases
FullyConnected = Dense
fully_connected = dense