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# -*- coding: utf-8 -*-
"""Core Keras layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import copy
import types as python_types
import warnings
from .. import backend as K
from .. import activations
from .. import initializers
from .. import regularizers
from .. import constraints
from ..engine.base_layer import InputSpec
from ..engine.base_layer import Layer
from ..utils.generic_utils import func_dump
from ..utils.generic_utils import func_load
from ..utils.generic_utils import deserialize_keras_object
from ..utils.generic_utils import has_arg
from ..utils import conv_utils
from ..legacy import interfaces
class Masking(Layer):
"""Masks a sequence by using a mask value to skip timesteps.
For each timestep in the input tensor (dimension #1 in the tensor),
if all values in the input tensor at that timestep
are equal to `mask_value`, then the timestep will be masked (skipped)
in all downstream layers (as long as they support masking).
If any downstream layer does not support masking yet receives such
an input mask, an exception will be raised.
# Example
Consider a Numpy data array `x` of shape `(samples, timesteps, features)`,
to be fed to an LSTM layer.
You want to mask timestep #3 and #5 because you lack data for
these timesteps. You can:
- set `x[:, 3, :] = 0.` and `x[:, 5, :] = 0.`
- insert a `Masking` layer with `mask_value=0.` before the LSTM layer:
```python
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(timesteps, features)))
model.add(LSTM(32))
```
"""
def __init__(self, mask_value=0., **kwargs):
super(Masking, self).__init__(**kwargs)
self.supports_masking = True
self.mask_value = mask_value
def compute_mask(self, inputs, mask=None):
output_mask = K.any(K.not_equal(inputs, self.mask_value), axis=-1)
return output_mask
def call(self, inputs):
boolean_mask = K.any(K.not_equal(inputs, self.mask_value),
axis=-1, keepdims=True)
return inputs * K.cast(boolean_mask, K.dtype(inputs))
def get_config(self):
config = {'mask_value': self.mask_value}
base_config = super(Masking, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class Dropout(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.
# Arguments
rate: float between 0 and 1. Fraction of the input units to drop.
noise_shape: 1D integer tensor 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 to use as random seed.
# References
- [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
"""
@interfaces.legacy_dropout_support
def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
super(Dropout, self).__init__(**kwargs)
self.rate = min(1., max(0., rate))
self.noise_shape = noise_shape
self.seed = seed
self.supports_masking = True
def _get_noise_shape(self, inputs):
if self.noise_shape is None:
return self.noise_shape
symbolic_shape = K.shape(inputs)
noise_shape = [symbolic_shape[axis] if shape is None else shape
for axis, shape in enumerate(self.noise_shape)]
return tuple(noise_shape)
def call(self, inputs, training=None):
if 0. < self.rate < 1.:
noise_shape = self._get_noise_shape(inputs)
def dropped_inputs():
return K.dropout(inputs, self.rate, noise_shape,
seed=self.seed)
return K.in_train_phase(dropped_inputs, inputs,
training=training)
return inputs
def get_config(self):
config = {'rate': self.rate,
'noise_shape': self.noise_shape,
'seed': self.seed}
base_config = super(Dropout, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class SpatialDropout1D(Dropout):
"""Spatial 1D version of Dropout.
This version performs the same function as Dropout, however it drops
entire 1D feature maps instead of individual elements. If adjacent frames
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, SpatialDropout1D will help promote independence
between feature maps and should be used instead.
# Arguments
rate: float between 0 and 1. Fraction of the input units to drop.
# Input shape
3D tensor with shape:
`(samples, timesteps, channels)`
# Output shape
Same as input
# References
- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280)
"""
@interfaces.legacy_spatialdropout1d_support
def __init__(self, rate, **kwargs):
super(SpatialDropout1D, self).__init__(rate, **kwargs)
self.input_spec = InputSpec(ndim=3)
def _get_noise_shape(self, inputs):
input_shape = K.shape(inputs)
noise_shape = (input_shape[0], 1, input_shape[2])
return noise_shape
class SpatialDropout2D(Dropout):
"""Spatial 2D version of Dropout.
This version performs the same function as Dropout, however it drops
entire 2D feature maps instead of individual elements. If adjacent pixels
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, SpatialDropout2D will help promote independence
between feature maps and should be used instead.
# Arguments
rate: float between 0 and 1. Fraction of the input units to drop.
data_format: 'channels_first' or 'channels_last'.
In 'channels_first' mode, the channels dimension
(the depth) is at index 1,
in 'channels_last' mode is it at index 3.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
Same as input
# References
- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280)
"""
@interfaces.legacy_spatialdropoutNd_support
def __init__(self, rate, data_format=None, **kwargs):
super(SpatialDropout2D, self).__init__(rate, **kwargs)
self.data_format = K.normalize_data_format(data_format)
self.input_spec = InputSpec(ndim=4)
def _get_noise_shape(self, inputs):
input_shape = K.shape(inputs)
if self.data_format == 'channels_first':
noise_shape = (input_shape[0], input_shape[1], 1, 1)
else:
noise_shape = (input_shape[0], 1, 1, input_shape[3])
return noise_shape
class SpatialDropout3D(Dropout):
"""Spatial 3D version of Dropout.
This version performs the same function as Dropout, however it drops
entire 3D feature maps instead of individual elements. If adjacent voxels
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, SpatialDropout3D will help promote independence
between feature maps and should be used instead.
# Arguments
rate: float between 0 and 1. Fraction of the input units to drop.
data_format: 'channels_first' or 'channels_last'.
In 'channels_first' mode, the channels dimension (the depth)
is at index 1, in 'channels_last' mode is it at index 4.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
# Input shape
5D tensor with shape:
`(samples, channels, dim1, dim2, dim3)` if data_format='channels_first'
or 5D tensor with shape:
`(samples, dim1, dim2, dim3, channels)` if data_format='channels_last'.
# Output shape
Same as input
# References
- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280)
"""
@interfaces.legacy_spatialdropoutNd_support
def __init__(self, rate, data_format=None, **kwargs):
super(SpatialDropout3D, self).__init__(rate, **kwargs)
self.data_format = K.normalize_data_format(data_format)
self.input_spec = InputSpec(ndim=5)
def _get_noise_shape(self, inputs):
input_shape = K.shape(inputs)
if self.data_format == 'channels_first':
noise_shape = (input_shape[0], input_shape[1], 1, 1, 1)
else:
noise_shape = (input_shape[0], 1, 1, 1, input_shape[4])
return noise_shape
class Activation(Layer):
"""Applies an activation function to an output.
# Arguments
activation: name of activation function to use
(see: [activations](../activations.md)),
or alternatively, a Theano or TensorFlow operation.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as input.
"""
def __init__(self, activation, **kwargs):
super(Activation, self).__init__(**kwargs)
self.supports_masking = True
self.activation = activations.get(activation)
def call(self, inputs):
return self.activation(inputs)
def get_config(self):
config = {'activation': activations.serialize(self.activation)}
base_config = super(Activation, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class Reshape(Layer):
"""Reshapes an output to a certain shape.
# Arguments
target_shape: target shape. Tuple of integers.
Does not include the batch axis.
# Input shape
Arbitrary, although all dimensions in the input shaped must be fixed.
Use the keyword argument `input_shape`
(tuple of integers, does not include the batch axis)
when using this layer as the first layer in a model.
# Output shape
`(batch_size,) + target_shape`
# Example
```python
# as first layer in a Sequential model
model = Sequential()
model.add(Reshape((3, 4), input_shape=(12,)))
# now: model.output_shape == (None, 3, 4)
# note: `None` is the batch dimension
# as intermediate layer in a Sequential model
model.add(Reshape((6, 2)))
# now: model.output_shape == (None, 6, 2)
# also supports shape inference using `-1` as dimension
model.add(Reshape((-1, 2, 2)))
# now: model.output_shape == (None, 3, 2, 2)
```
"""
def __init__(self, target_shape, **kwargs):
super(Reshape, self).__init__(**kwargs)
self.target_shape = tuple(target_shape)
def _fix_unknown_dimension(self, input_shape, output_shape):
"""Finds and replaces a missing dimension in an output shape.
This is a near direct port of the internal Numpy function
`_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`
# Arguments
input_shape: original shape of array being reshaped
output_shape: target shape of the array, with at most
a single -1 which indicates a dimension that should be
derived from the input shape.
# Returns
The new output shape with a `-1` replaced with its computed value.
# Raises
ValueError: if `input_shape` and `output_shape` do not match.
"""
output_shape = list(output_shape)
msg = 'total size of new array must be unchanged'
known, unknown = 1, None
for index, dim in enumerate(output_shape):
if dim < 0:
if unknown is None:
unknown = index
else:
raise ValueError('Can only specify one unknown dimension.')
else:
known *= dim
original = np.prod(input_shape, dtype=int)
if unknown is not None:
if known == 0 or original % known != 0:
raise ValueError(msg)
output_shape[unknown] = original // known
elif original != known:
raise ValueError(msg)
return tuple(output_shape)
def compute_output_shape(self, input_shape):
if None in input_shape[1:]:
# input shape (partially) unknown? replace -1's with None's
return ((input_shape[0],) +
tuple(s if s != -1 else None for s in self.target_shape))
else:
# input shape known? then we can compute the output shape
return (input_shape[0],) + self._fix_unknown_dimension(
input_shape[1:], self.target_shape)
def call(self, inputs):
return K.reshape(inputs, (K.shape(inputs)[0],) + self.target_shape)
def get_config(self):
config = {'target_shape': self.target_shape}
base_config = super(Reshape, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Permute(Layer):
"""Permutes the dimensions of the input according to a given pattern.
Useful for e.g. connecting RNNs and convnets together.
# Example
```python
model = Sequential()
model.add(Permute((2, 1), input_shape=(10, 64)))
# now: model.output_shape == (None, 64, 10)
# note: `None` is the batch dimension
```
# Arguments
dims: Tuple of integers. Permutation pattern, does not include the
samples dimension. Indexing starts at 1.
For instance, `(2, 1)` permutes the first and second dimension
of the input.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same as the input shape, but with the dimensions re-ordered according
to the specified pattern.
"""
def __init__(self, dims, **kwargs):
super(Permute, self).__init__(**kwargs)
self.dims = tuple(dims)
self.input_spec = InputSpec(ndim=len(self.dims) + 1)
def compute_output_shape(self, input_shape):
input_shape = list(input_shape)
output_shape = copy.copy(input_shape)
for i, dim in enumerate(self.dims):
target_dim = input_shape[dim]
output_shape[i + 1] = target_dim
return tuple(output_shape)
def call(self, inputs):
return K.permute_dimensions(inputs, (0,) + self.dims)
def get_config(self):
config = {'dims': self.dims}
base_config = super(Permute, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Flatten(Layer):
"""Flattens the input. Does not affect the batch size.
# Arguments
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
The purpose of this argument is to preserve weight
ordering when switching a model from one data format
to another.
`channels_last` corresponds to inputs with shape
`(batch, ..., channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, ...)`.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
# Example
```python
model = Sequential()
model.add(Conv2D(64, (3, 3),
input_shape=(3, 32, 32), padding='same',))
# now: model.output_shape == (None, 64, 32, 32)
model.add(Flatten())
# now: model.output_shape == (None, 65536)
```
"""
def __init__(self, data_format=None, **kwargs):
super(Flatten, self).__init__(**kwargs)
self.input_spec = InputSpec(min_ndim=3)
self.data_format = K.normalize_data_format(data_format)
def compute_output_shape(self, input_shape):
if not all(input_shape[1:]):
raise ValueError('The shape of the input to "Flatten" '
'is not fully defined '
'(got ' + str(input_shape[1:]) + '. '
'Make sure to pass a complete "input_shape" '
'or "batch_input_shape" argument to the first '
'layer in your model.')
return (input_shape[0], np.prod(input_shape[1:]))
def call(self, inputs):
if self.data_format == 'channels_first':
# Ensure works for any dim
permutation = [0]
permutation.extend([i for i in
range(2, K.ndim(inputs))])
permutation.append(1)
inputs = K.permute_dimensions(inputs, permutation)
return K.batch_flatten(inputs)
def get_config(self):
config = {'data_format': self.data_format}
base_config = super(Flatten, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class RepeatVector(Layer):
"""Repeats the input n times.
# Example
```python
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
```
# Arguments
n: integer, repetition factor.
# Input shape
2D tensor of shape `(num_samples, features)`.
# Output shape
3D tensor of shape `(num_samples, n, features)`.
"""
def __init__(self, n, **kwargs):
super(RepeatVector, self).__init__(**kwargs)
self.n = n
self.input_spec = InputSpec(ndim=2)
def compute_output_shape(self, input_shape):
return (input_shape[0], self.n, input_shape[1])
def call(self, inputs):
return K.repeat(inputs, self.n)
def get_config(self):
config = {'n': self.n}
base_config = super(RepeatVector, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Lambda(Layer):
"""Wraps arbitrary expression as a `Layer` object.
# Examples
```python
# add a x -> x^2 layer
model.add(Lambda(lambda x: x ** 2))
```
```python
# add a layer that returns the concatenation
# of the positive part of the input and
# the opposite of the negative part
def antirectifier(x):
x -= K.mean(x, axis=1, keepdims=True)
x = K.l2_normalize(x, axis=1)
pos = K.relu(x)
neg = K.relu(-x)
return K.concatenate([pos, neg], axis=1)
def antirectifier_output_shape(input_shape):
shape = list(input_shape)
assert len(shape) == 2 # only valid for 2D tensors
shape[-1] *= 2
return tuple(shape)
model.add(Lambda(antirectifier,
output_shape=antirectifier_output_shape))
```
# Arguments
function: The function to be evaluated.
Takes input tensor as first argument.
output_shape: Expected output shape from function.
Only relevant when using Theano.
Can be a tuple or function.
If a tuple, it only specifies the first dimension onward;
sample dimension is assumed either the same as the input:
`output_shape = (input_shape[0], ) + output_shape`
or, the input is `None` and
the sample dimension is also `None`:
`output_shape = (None, ) + output_shape`
If a function, it specifies the entire shape as a function of the
input shape: `output_shape = f(input_shape)`
arguments: optional dictionary of keyword arguments to be passed
to the function.
# Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Specified by `output_shape` argument
(or auto-inferred when using TensorFlow or CNTK).
"""
@interfaces.legacy_lambda_support
def __init__(self, function, output_shape=None,
mask=None, arguments=None, **kwargs):
super(Lambda, self).__init__(**kwargs)
self.function = function
self.arguments = arguments if arguments else {}
if mask is not None:
self.supports_masking = True
self.mask = mask
if output_shape is None:
self._output_shape = None
elif isinstance(output_shape, (tuple, list)):
self._output_shape = tuple(output_shape)
else:
if not callable(output_shape):
raise TypeError('In Lambda, `output_shape` '
'must be a list, a tuple, or a function.')
self._output_shape = output_shape
def compute_output_shape(self, input_shape):
if self._output_shape is None:
# With TensorFlow or CNTK, we can infer the output shape directly:
if K.backend() in ('tensorflow', 'cntk'):
if isinstance(input_shape, list):
xs = [K.placeholder(shape=shape) for shape in input_shape]
x = self.call(xs)
else:
x = K.placeholder(shape=input_shape)
x = self.call(x)
if isinstance(x, list):
return [K.int_shape(x_elem) for x_elem in x]
else:
return K.int_shape(x)
# Otherwise, we default to the input shape.
warnings.warn('`output_shape` argument not specified for layer {} '
'and cannot be automatically inferred '
'with the Theano backend. '
'Defaulting to output shape `{}` '
'(same as input shape). '
'If the expected output shape is different, '
'specify it via the `output_shape` argument.'
.format(self.name, input_shape))
return input_shape
elif isinstance(self._output_shape, (tuple, list)):
if isinstance(input_shape, list):
num_samples = input_shape[0][0]
else:
num_samples = input_shape[0] if input_shape else None
return (num_samples,) + tuple(self._output_shape)
else:
shape = self._output_shape(input_shape)
if not isinstance(shape, (list, tuple)):
raise ValueError('`output_shape` function must return a tuple or a list of tuples.')
if isinstance(shape, list):
if isinstance(shape[0], int) or shape[0] is None:
shape = tuple(shape)
return shape
def call(self, inputs, mask=None):
arguments = self.arguments
if has_arg(self.function, 'mask'):
arguments['mask'] = mask
return self.function(inputs, **arguments)
def compute_mask(self, inputs, mask=None):
if callable(self.mask):
return self.mask(inputs, mask)
return self.mask
def get_config(self):
if isinstance(self.function, python_types.LambdaType):
function = func_dump(self.function)
function_type = 'lambda'
else:
function = self.function.__name__
function_type = 'function'
if isinstance(self._output_shape, python_types.LambdaType):
output_shape = func_dump(self._output_shape)
output_shape_type = 'lambda'
elif callable(self._output_shape):
output_shape = self._output_shape.__name__
output_shape_type = 'function'
else:
output_shape = self._output_shape
output_shape_type = 'raw'
config = {'function': function,
'function_type': function_type,
'output_shape': output_shape,
'output_shape_type': output_shape_type,
'arguments': self.arguments}
base_config = super(Lambda, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
config = config.copy()
globs = globals()
if custom_objects:
globs = dict(list(globs.items()) + list(custom_objects.items()))
function_type = config.pop('function_type')
if function_type == 'function':
# Simple lookup in custom objects
function = deserialize_keras_object(
config['function'],
custom_objects=custom_objects,
printable_module_name='function in Lambda layer')
elif function_type == 'lambda':
# Unsafe deserialization from bytecode
function = func_load(config['function'], globs=globs)
else:
raise TypeError('Unknown function type:', function_type)
output_shape_type = config.pop('output_shape_type')
if output_shape_type == 'function':
# Simple lookup in custom objects
output_shape = deserialize_keras_object(
config['output_shape'],
custom_objects=custom_objects,
printable_module_name='output_shape function in Lambda layer')
elif output_shape_type == 'lambda':
# Unsafe deserialization from bytecode
output_shape = func_load(config['output_shape'], globs=globs)
else:
output_shape = config['output_shape']
# If arguments were numpy array, they have been saved as
# list. We need to recover the ndarray
if 'arguments' in config:
for key in config['arguments']:
if isinstance(config['arguments'][key], dict):
arg_dict = config['arguments'][key]
if 'type' in arg_dict and arg_dict['type'] == 'ndarray':
# Overwrite the argument with its numpy translation
config['arguments'][key] = np.array(arg_dict['value'])
config['function'] = function
config['output_shape'] = output_shape
return cls(**config)
class Dense(Layer):
"""Just your regular densely-connected NN layer.
`Dense` implements the operation:
`output = activation(dot(input, kernel) + bias)`
where `activation` is the element-wise activation function
passed as the `activation` argument, `kernel` is a weights matrix
created by the layer, and `bias` is a bias vector created by the layer
(only applicable if `use_bias` is `True`).
Note: if the input to the layer has a rank greater than 2, then
it is flattened prior to the initial dot product with `kernel`.
# Example
```python
# as first layer in a sequential model:
model = Sequential()
model.add(Dense(32, input_shape=(16,)))
# now the model will take as input arrays of shape (*, 16)
# and output arrays of shape (*, 32)
# after the first layer, you don't need to specify
# the size of the input anymore:
model.add(Dense(32))
```
# Arguments
units: Positive integer, dimensionality of the output space.
activation: Activation function to use
(see [activations](../activations.md)).
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to
the `kernel` weights matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
nD tensor with shape: `(batch_size, ..., input_dim)`.
The most common situation would be
a 2D input with shape `(batch_size, input_dim)`.
# Output shape
nD tensor with shape: `(batch_size, ..., units)`.
For instance, for a 2D input with shape `(batch_size, input_dim)`,
the output would have shape `(batch_size, units)`.
"""
@interfaces.legacy_dense_support
def __init__(self, units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(Dense, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
self.kernel = self.add_weight(shape=(input_dim, self.units),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.units,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True
def call(self, inputs):
output = K.dot(inputs, self.kernel)
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) >= 2
assert input_shape[-1]
output_shape = list(input_shape)
output_shape[-1] = self.units
return tuple(output_shape)
def get_config(self):
config = {
'units': self.units,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(Dense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ActivityRegularization(Layer):
"""Layer that applies an update to the cost function based input activity.
# Arguments
l1: L1 regularization factor (positive float).
l2: L2 regularization factor (positive float).
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as input.
"""
def __init__(self, l1=0., l2=0., **kwargs):
super(ActivityRegularization, self).__init__(**kwargs)
self.supports_masking = True
self.l1 = l1
self.l2 = l2
self.activity_regularizer = regularizers.L1L2(l1=l1, l2=l2)
def get_config(self):
config = {'l1': self.l1,
'l2': self.l2}
base_config = super(ActivityRegularization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape