tensorflow/python/keras/layers/convolutional.py
# 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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ==============================================================================
"""Keras convolution layers and image transformation layers."""
import functools
from tensorflow.python.eager import context
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras import activations
from tensorflow.python.keras import backend
from tensorflow.python.keras import constraints
from tensorflow.python.keras import initializers
from tensorflow.python.keras import regularizers
from tensorflow.python.keras.engine.base_layer import Layer
from tensorflow.python.keras.engine.input_spec import InputSpec
# imports for backwards namespace compatibility
# pylint: disable=unused-import
from tensorflow.python.keras.layers.pooling import AveragePooling1D
from tensorflow.python.keras.layers.pooling import AveragePooling2D
from tensorflow.python.keras.layers.pooling import AveragePooling3D
from tensorflow.python.keras.layers.pooling import MaxPooling1D
from tensorflow.python.keras.layers.pooling import MaxPooling2D
from tensorflow.python.keras.layers.pooling import MaxPooling3D
# pylint: enable=unused-import
from tensorflow.python.keras.utils import conv_utils
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import array_ops_stack
from tensorflow.python.ops import nn
from tensorflow.python.ops import nn_ops
# pylint: disable=g-classes-have-attributes
class Conv(Layer):
"""Abstract N-D convolution layer (private, used as implementation base).
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If `use_bias` is True (and a `bias_initializer` is provided),
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
Note: layer attributes cannot be modified after the layer has been called
once (except the `trainable` attribute).
Args:
rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution). Could be "None", eg in the case of
depth wise convolution.
kernel_size: An integer or tuple/list of n integers, specifying the
length of the convolution window.
strides: An integer or tuple/list of n integers,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"`, `"same"`, or `"causal"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros
evenly to the left/right or up/down of the input such that output has the
same height/width dimension as the input. `"causal"` results in causal
(dilated) convolutions, e.g. `output[t]` does not depend on `input[t+1:]`.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, ..., channels)` while `channels_first` corresponds to
inputs with shape `(batch_size, channels, ...)`.
Note: `channels_first` is only available on GPUs.
dilation_rate: An integer or tuple/list of n integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
groups: A positive integer specifying the number of groups in which the
input is split along the channel axis. Each group is convolved
separately with `filters / groups` filters. The output is the
concatenation of all the `groups` results along the channel axis.
Input channels and `filters` must both be divisible by `groups`.
activation: Activation function to use.
If you don't specify anything, no activation is applied.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel. If None, the
default initializer (glorot_uniform) will be used.
bias_initializer: An initializer for the bias vector. If None, the default
initializer (zeros) will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: 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: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
"""
def __init__(self,
rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
groups=1,
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,
trainable=True,
name=None,
conv_op=None,
**kwargs):
super(Conv, self).__init__(
trainable=trainable,
name=name,
activity_regularizer=regularizers.get(activity_regularizer),
**kwargs)
self.rank = rank
if isinstance(filters, float):
filters = int(filters)
if filters is not None and filters < 0:
raise ValueError(f'Received a negative value for `filters`.'
f'Was expecting a positive value, got {filters}.')
self.filters = filters
self.groups = groups or 1
self.kernel_size = conv_utils.normalize_tuple(
kernel_size, rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(
dilation_rate, rank, 'dilation_rate')
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.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=self.rank + 2)
self._validate_init()
self._is_causal = self.padding == 'causal'
self._channels_first = self.data_format == 'channels_first'
self._tf_data_format = conv_utils.convert_data_format(
self.data_format, self.rank + 2)
def _validate_init(self):
if self.filters is not None and self.filters % self.groups != 0:
raise ValueError(
'The number of filters must be evenly divisible by the number of '
'groups. Received: groups={}, filters={}'.format(
self.groups, self.filters))
if not all(self.kernel_size):
raise ValueError('The argument `kernel_size` cannot contain 0(s). '
'Received: %s' % (self.kernel_size,))
if not all(self.strides):
raise ValueError('The argument `strides` cannot contains 0(s). '
'Received: %s' % (self.strides,))
if (self.padding == 'causal' and not isinstance(self,
(Conv1D, SeparableConv1D))):
raise ValueError('Causal padding is only supported for `Conv1D`'
'and `SeparableConv1D`.')
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
input_channel = self._get_input_channel(input_shape)
if input_channel % self.groups != 0:
raise ValueError(
'The number of input channels must be evenly divisible by the number '
'of groups. Received groups={}, but the input has {} channels '
'(full input shape is {}).'.format(self.groups, input_channel,
input_shape))
kernel_shape = self.kernel_size + (input_channel // self.groups,
self.filters)
self.kernel = self.add_weight(
name='kernel',
shape=kernel_shape,
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
trainable=True,
dtype=self.dtype)
if self.use_bias:
self.bias = self.add_weight(
name='bias',
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype)
else:
self.bias = None
channel_axis = self._get_channel_axis()
self.input_spec = InputSpec(min_ndim=self.rank + 2,
axes={channel_axis: input_channel})
# Convert Keras formats to TF native formats.
if self.padding == 'causal':
tf_padding = 'VALID' # Causal padding handled in `call`.
elif isinstance(self.padding, str):
tf_padding = self.padding.upper()
else:
tf_padding = self.padding
tf_dilations = list(self.dilation_rate)
tf_strides = list(self.strides)
tf_op_name = self.__class__.__name__
if tf_op_name == 'Conv1D':
tf_op_name = 'conv1d' # Backwards compat.
self._convolution_op = functools.partial(
nn_ops.convolution_v2,
strides=tf_strides,
padding=tf_padding,
dilations=tf_dilations,
data_format=self._tf_data_format,
name=tf_op_name)
self.built = True
def call(self, inputs):
input_shape = inputs.shape
if self._is_causal: # Apply causal padding to inputs for Conv1D.
inputs = array_ops.pad(inputs, self._compute_causal_padding(inputs))
outputs = self._convolution_op(inputs, self.kernel)
if self.use_bias:
output_rank = outputs.shape.rank
if self.rank == 1 and self._channels_first:
# nn.bias_add does not accept a 1D input tensor.
bias = array_ops.reshape(self.bias, (1, self.filters, 1))
outputs += bias
else:
# Handle multiple batch dimensions.
if output_rank is not None and output_rank > 2 + self.rank:
def _apply_fn(o):
return nn.bias_add(o, self.bias, data_format=self._tf_data_format)
outputs = conv_utils.squeeze_batch_dims(
outputs, _apply_fn, inner_rank=self.rank + 1)
else:
outputs = nn.bias_add(
outputs, self.bias, data_format=self._tf_data_format)
if not context.executing_eagerly():
# Infer the static output shape:
out_shape = self.compute_output_shape(input_shape)
outputs.set_shape(out_shape)
if self.activation is not None:
return self.activation(outputs)
return outputs
def _spatial_output_shape(self, spatial_input_shape):
return [
conv_utils.conv_output_length(
length,
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
for i, length in enumerate(spatial_input_shape)
]
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
batch_rank = len(input_shape) - self.rank - 1
if self.data_format == 'channels_last':
return tensor_shape.TensorShape(
input_shape[:batch_rank]
+ self._spatial_output_shape(input_shape[batch_rank:-1])
+ [self.filters])
else:
return tensor_shape.TensorShape(
input_shape[:batch_rank] + [self.filters] +
self._spatial_output_shape(input_shape[batch_rank + 1:]))
def _recreate_conv_op(self, inputs): # pylint: disable=unused-argument
return False
def get_config(self):
config = {
'filters':
self.filters,
'kernel_size':
self.kernel_size,
'strides':
self.strides,
'padding':
self.padding,
'data_format':
self.data_format,
'dilation_rate':
self.dilation_rate,
'groups':
self.groups,
'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(Conv, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def _compute_causal_padding(self, inputs):
"""Calculates padding for 'causal' option for 1-d conv layers."""
left_pad = self.dilation_rate[0] * (self.kernel_size[0] - 1)
if getattr(inputs.shape, 'ndims', None) is None:
batch_rank = 1
else:
batch_rank = len(inputs.shape) - 2
if self.data_format == 'channels_last':
causal_padding = [[0, 0]] * batch_rank + [[left_pad, 0], [0, 0]]
else:
causal_padding = [[0, 0]] * batch_rank + [[0, 0], [left_pad, 0]]
return causal_padding
def _get_channel_axis(self):
if self.data_format == 'channels_first':
return -1 - self.rank
else:
return -1
def _get_input_channel(self, input_shape):
channel_axis = self._get_channel_axis()
if input_shape.dims[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
return int(input_shape[channel_axis])
def _get_padding_op(self):
if self.padding == 'causal':
op_padding = 'valid'
else:
op_padding = self.padding
if not isinstance(op_padding, (list, tuple)):
op_padding = op_padding.upper()
return op_padding
class Conv1D(Conv):
"""1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
with the layer input over a single spatial (or temporal) dimension
to produce a tensor of outputs.
If `use_bias` is True, a bias vector is created and added to the outputs.
Finally, if `activation` is not `None`,
it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide an `input_shape` argument
(tuple of integers or `None`, e.g.
`(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
Examples:
>>> # The inputs are 128-length vectors with 10 timesteps, and the batch size
>>> # is 4.
>>> input_shape = (4, 10, 128)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv1D(
... 32, 3, activation='relu',input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 8, 32)
>>> # With extended batch shape [4, 7] (e.g. weather data where batch
>>> # dimensions correspond to spatial location and the third dimension
>>> # corresponds to time.)
>>> input_shape = (4, 7, 10, 128)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv1D(
... 32, 3, activation='relu', input_shape=input_shape[2:])(x)
>>> print(y.shape)
(4, 7, 8, 32)
Args:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of a single integer,
specifying the length of the 1D convolution window.
strides: An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"`, `"same"` or `"causal"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
`"causal"` results in causal (dilated) convolutions, e.g. `output[t]`
does not depend on `input[t+1:]`. Useful when modeling temporal data
where the model should not violate the temporal order.
See [WaveNet: A Generative Model for Raw Audio, section
2.1](https://arxiv.org/abs/1609.03499).
data_format: A string,
one of `channels_last` (default) or `channels_first`.
dilation_rate: an integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
groups: A positive integer specifying the number of groups in which the
input is split along the channel axis. Each group is convolved
separately with `filters / groups` filters. The output is the
concatenation of all the `groups` results along the channel axis.
Input channels and `filters` must both be divisible by `groups`.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix (
see `keras.initializers`). Defaults to 'glorot_uniform'.
bias_initializer: Initializer for the bias vector (
see `keras.initializers`). Defaults to 'zeros'.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix (see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (
see `keras.regularizers`).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation") (
see `keras.regularizers`).
kernel_constraint: Constraint function applied to the kernel matrix (
see `keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (
see `keras.constraints`).
Input shape:
3+D tensor with shape: `batch_shape + (steps, input_dim)`
Output shape:
3+D tensor with shape: `batch_shape + (new_steps, filters)`
`steps` value might have changed due to padding or strides.
Returns:
A tensor of rank 3 representing
`activation(conv1d(inputs, kernel) + bias)`.
Raises:
ValueError: when both `strides > 1` and `dilation_rate > 1`.
"""
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
groups=1,
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):
super(Conv1D, self).__init__(
rank=1,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
class Conv2D(Conv):
"""2D convolution layer (e.g. spatial convolution over images).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is True,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers or `None`, does not include the sample axis),
e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
in `data_format="channels_last"`. You can use `None` when
a dimension has variable size.
Examples:
>>> # The inputs are 28x28 RGB images with `channels_last` and the batch
>>> # size is 4.
>>> input_shape = (4, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 26, 26, 2)
>>> # With `dilation_rate` as 2.
>>> input_shape = (4, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', dilation_rate=2, input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 24, 24, 2)
>>> # With `padding` as "same".
>>> input_shape = (4, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', padding="same", input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 28, 28, 2)
>>> # With extended batch shape [4, 7]:
>>> input_shape = (4, 7, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
>>> print(y.shape)
(4, 7, 26, 26, 2)
Args:
filters: Integer, the dimensionality of the output space (i.e. the number of
output filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the height
and width of the 2D convolution window. Can be a single integer to specify
the same value for all spatial dimensions.
strides: An integer or tuple/list of 2 integers, specifying the strides of
the convolution along the height and width. Can be a single integer to
specify the same value for all spatial dimensions. Specifying any stride
value != 1 is incompatible with specifying any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs. `channels_last` corresponds
to inputs with shape `(batch_size, height, width, channels)` while
`channels_first` corresponds to inputs with shape `(batch_size, channels,
height, width)`. 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`.
dilation_rate: an integer or tuple/list of 2 integers, specifying the
dilation rate to use for dilated convolution. Can be a single integer to
specify the same value for all spatial dimensions. Currently, specifying
any `dilation_rate` value != 1 is incompatible with specifying any stride
value != 1.
groups: A positive integer specifying the number of groups in which the
input is split along the channel axis. Each group is convolved separately
with `filters / groups` filters. The output is the concatenation of all
the `groups` results along the channel axis. Input channels and `filters`
must both be divisible by `groups`.
activation: Activation function to use. If you don't specify anything, no
activation is applied (see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix (see
`keras.initializers`). Defaults to 'glorot_uniform'.
bias_initializer: Initializer for the bias vector (see
`keras.initializers`). Defaults to 'zeros'.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix (see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (see
`keras.regularizers`).
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation") (see `keras.regularizers`).
kernel_constraint: Constraint function applied to the kernel matrix (see
`keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (see
`keras.constraints`).
Input shape:
4+D tensor with shape: `batch_shape + (channels, rows, cols)` if
`data_format='channels_first'`
or 4+D tensor with shape: `batch_shape + (rows, cols, channels)` if
`data_format='channels_last'`.
Output shape:
4+D tensor with shape: `batch_shape + (filters, new_rows, new_cols)` if
`data_format='channels_first'` or 4+D tensor with shape: `batch_shape +
(new_rows, new_cols, filters)` if `data_format='channels_last'`. `rows`
and `cols` values might have changed due to padding.
Returns:
A tensor of rank 4+ representing
`activation(conv2d(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is `"causal"`.
ValueError: when both `strides > 1` and `dilation_rate > 1`.
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
groups=1,
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):
super(Conv2D, self).__init__(
rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
class Conv3D(Conv):
"""3D convolution layer (e.g. spatial convolution over volumes).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is True,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers or `None`, does not include the sample axis),
e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
with a single channel,
in `data_format="channels_last"`.
Examples:
>>> # The inputs are 28x28x28 volumes with a single channel, and the
>>> # batch size is 4
>>> input_shape =(4, 28, 28, 28, 1)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv3D(
... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 26, 26, 26, 2)
>>> # With extended batch shape [4, 7], e.g. a batch of 4 videos of 3D frames,
>>> # with 7 frames per video.
>>> input_shape = (4, 7, 28, 28, 28, 1)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv3D(
... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
>>> print(y.shape)
(4, 7, 26, 26, 26, 2)
Args:
filters: Integer, the dimensionality of the output space (i.e. the number of
output filters in the convolution).
kernel_size: An integer or tuple/list of 3 integers, specifying the depth,
height and width of the 3D convolution window. Can be a single integer to
specify the same value for all spatial dimensions.
strides: An integer or tuple/list of 3 integers, specifying the strides of
the convolution along each spatial dimension. Can be a single integer to
specify the same value for all spatial dimensions. Specifying any stride
value != 1 is incompatible with specifying any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs. `channels_last` corresponds
to inputs with shape `batch_shape + (spatial_dim1, spatial_dim2,
spatial_dim3, channels)` while `channels_first` corresponds to inputs with
shape `batch_shape + (channels, spatial_dim1, spatial_dim2,
spatial_dim3)`. 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".
dilation_rate: an integer or tuple/list of 3 integers, specifying the
dilation rate to use for dilated convolution. Can be a single integer to
specify the same value for all spatial dimensions. Currently, specifying
any `dilation_rate` value != 1 is incompatible with specifying any stride
value != 1.
groups: A positive integer specifying the number of groups in which the
input is split along the channel axis. Each group is convolved separately
with `filters / groups` filters. The output is the concatenation of all
the `groups` results along the channel axis. Input channels and `filters`
must both be divisible by `groups`.
activation: Activation function to use. If you don't specify anything, no
activation is applied (see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix (see
`keras.initializers`). Defaults to 'glorot_uniform'.
bias_initializer: Initializer for the bias vector (see
`keras.initializers`). Defaults to 'zeros'.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix (see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (see
`keras.regularizers`).
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation") (see `keras.regularizers`).
kernel_constraint: Constraint function applied to the kernel matrix (see
`keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (see
`keras.constraints`).
Input shape:
5+D tensor with shape: `batch_shape + (channels, conv_dim1, conv_dim2,
conv_dim3)` if data_format='channels_first'
or 5+D tensor with shape: `batch_shape + (conv_dim1, conv_dim2, conv_dim3,
channels)` if data_format='channels_last'.
Output shape:
5+D tensor with shape: `batch_shape + (filters, new_conv_dim1,
new_conv_dim2, new_conv_dim3)` if data_format='channels_first'
or 5+D tensor with shape: `batch_shape + (new_conv_dim1, new_conv_dim2,
new_conv_dim3, filters)` if data_format='channels_last'. `new_conv_dim1`,
`new_conv_dim2` and `new_conv_dim3` values might have changed due to
padding.
Returns:
A tensor of rank 5+ representing
`activation(conv3d(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is "causal".
ValueError: when both `strides > 1` and `dilation_rate > 1`.
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1, 1),
groups=1,
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):
super(Conv3D, self).__init__(
rank=3,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
class Conv1DTranspose(Conv1D):
"""Transposed convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers or `None`, does not include the sample axis),
e.g. `input_shape=(128, 3)` for data with 128 time steps and 3 channels.
Args:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer length of the 1D convolution window.
strides: An integer specifying the stride of the convolution along the
time dimension. Specifying a stride value != 1 is incompatible with
specifying a `dilation_rate` value != 1. Defaults to 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
output_padding: An integer specifying the amount of padding along
the time dimension of the output tensor.
The amount of output padding must be lower than the stride.
If set to `None` (default), the output shape is inferred.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch_size, channels, length)`.
dilation_rate: an integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying a `dilation_rate` value != 1 is
incompatible with specifying a stride value != 1.
Also dilation rate larger than 1 is not currently supported.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix (
see `keras.initializers`). Defaults to 'glorot_uniform'.
bias_initializer: Initializer for the bias vector (
see `keras.initializers`). Defaults to 'zeros'.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix (see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (
see `keras.regularizers`).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation") (see `keras.regularizers`).
kernel_constraint: Constraint function applied to the kernel matrix (
see `keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (
see `keras.constraints`).
Input shape:
3D tensor with shape:
`(batch_size, steps, channels)`
Output shape:
3D tensor with shape:
`(batch_size, new_steps, filters)`
If `output_padding` is specified:
```
new_timesteps = ((timesteps - 1) * strides + kernel_size -
2 * padding + output_padding)
```
Returns:
A tensor of rank 3 representing
`activation(conv1dtranspose(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is "causal".
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
References:
- [A guide to convolution arithmetic for deep learning](
https://arxiv.org/abs/1603.07285v1)
- [Deconvolutional Networks](
https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
"""
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
output_padding=None,
data_format=None,
dilation_rate=1,
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):
super(Conv1DTranspose, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
self.output_padding = output_padding
if self.output_padding is not None:
self.output_padding = conv_utils.normalize_tuple(
self.output_padding, 1, 'output_padding')
for stride, out_pad in zip(self.strides, self.output_padding):
if out_pad >= stride:
raise ValueError('Stride ' + str(self.strides) + ' must be '
'greater than output padding ' +
str(self.output_padding))
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if len(input_shape) != 3:
raise ValueError('Inputs should have rank 3. Received input shape: ' +
str(input_shape))
channel_axis = self._get_channel_axis()
if input_shape.dims[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = int(input_shape[channel_axis])
self.input_spec = InputSpec(ndim=3, axes={channel_axis: input_dim})
kernel_shape = self.kernel_size + (self.filters, input_dim)
self.kernel = self.add_weight(
name='kernel',
shape=kernel_shape,
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
trainable=True,
dtype=self.dtype)
if self.use_bias:
self.bias = self.add_weight(
name='bias',
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype)
else:
self.bias = None
self.built = True
def call(self, inputs):
inputs_shape = array_ops.shape(inputs)
batch_size = inputs_shape[0]
if self.data_format == 'channels_first':
t_axis = 2
else:
t_axis = 1
length = inputs_shape[t_axis]
if self.output_padding is None:
output_padding = None
else:
output_padding = self.output_padding[0]
# Infer the dynamic output shape:
out_length = conv_utils.deconv_output_length(
length, self.kernel_size[0], padding=self.padding,
output_padding=output_padding, stride=self.strides[0],
dilation=self.dilation_rate[0])
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_length)
else:
output_shape = (batch_size, out_length, self.filters)
data_format = conv_utils.convert_data_format(self.data_format, ndim=3)
output_shape_tensor = array_ops_stack.stack(output_shape)
outputs = nn_ops.conv1d_transpose(
inputs,
self.kernel,
output_shape_tensor,
strides=self.strides,
padding=self.padding.upper(),
data_format=data_format,
dilations=self.dilation_rate)
if not context.executing_eagerly():
# Infer the static output shape:
out_shape = self.compute_output_shape(inputs.shape)
outputs.set_shape(out_shape)
if self.use_bias:
outputs = nn.bias_add(
outputs,
self.bias,
data_format=data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, t_axis = 1, 2
else:
c_axis, t_axis = 2, 1
if self.output_padding is None:
output_padding = None
else:
output_padding = self.output_padding[0]
output_shape[c_axis] = self.filters
output_shape[t_axis] = conv_utils.deconv_output_length(
output_shape[t_axis],
self.kernel_size[0],
padding=self.padding,
output_padding=output_padding,
stride=self.strides[0],
dilation=self.dilation_rate[0])
return tensor_shape.TensorShape(output_shape)
def get_config(self):
config = super(Conv1DTranspose, self).get_config()
config['output_padding'] = self.output_padding
return config
class Conv2DTranspose(Conv2D):
"""Transposed convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers or `None`, does not include the sample axis),
e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
in `data_format="channels_last"`.
Args:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
height and width of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
output_padding: An integer or tuple/list of 2 integers,
specifying the amount of padding along the height and width
of the output tensor.
Can be a single integer to specify the same value for all
spatial dimensions.
The amount of output padding along a given dimension must be
lower than the stride along that same dimension.
If set to `None` (default), the output shape is inferred.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, channels, height, width)`.
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".
dilation_rate: an integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix (
see `keras.initializers`). Defaults to 'glorot_uniform'.
bias_initializer: Initializer for the bias vector (
see `keras.initializers`). Defaults to 'zeros'.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix (see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (
see `keras.regularizers`).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation") (see `keras.regularizers`).
kernel_constraint: Constraint function applied to the kernel matrix (
see `keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (
see `keras.constraints`).
Input shape:
4D tensor with shape:
`(batch_size, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch_size, rows, cols, channels)` if data_format='channels_last'.
Output shape:
4D tensor with shape:
`(batch_size, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch_size, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
If `output_padding` is specified:
```
new_rows = ((rows - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
output_padding[0])
new_cols = ((cols - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
output_padding[1])
```
Returns:
A tensor of rank 4 representing
`activation(conv2dtranspose(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is "causal".
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
References:
- [A guide to convolution arithmetic for deep
learning](https://arxiv.org/abs/1603.07285v1)
- [Deconvolutional
Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
output_padding=None,
data_format=None,
dilation_rate=(1, 1),
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):
super(Conv2DTranspose, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
self.output_padding = output_padding
if self.output_padding is not None:
self.output_padding = conv_utils.normalize_tuple(
self.output_padding, 2, 'output_padding')
for stride, out_pad in zip(self.strides, self.output_padding):
if out_pad >= stride:
raise ValueError('Stride ' + str(self.strides) + ' must be '
'greater than output padding ' +
str(self.output_padding))
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if len(input_shape) != 4:
raise ValueError('Inputs should have rank 4. Received input '
'shape: ' + str(input_shape))
channel_axis = self._get_channel_axis()
if input_shape.dims[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = int(input_shape[channel_axis])
self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
kernel_shape = self.kernel_size + (self.filters, input_dim)
self.kernel = self.add_weight(
name='kernel',
shape=kernel_shape,
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
trainable=True,
dtype=self.dtype)
if self.use_bias:
self.bias = self.add_weight(
name='bias',
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype)
else:
self.bias = None
self.built = True
def call(self, inputs):
inputs_shape = array_ops.shape(inputs)
batch_size = inputs_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
# Use the constant height and weight when possible.
# TODO(scottzhu): Extract this into a utility function that can be applied
# to all convolutional layers, which currently lost the static shape
# information due to tf.shape().
height, width = None, None
if inputs.shape.rank is not None:
dims = inputs.shape.as_list()
height = dims[h_axis]
width = dims[w_axis]
height = height if height is not None else inputs_shape[h_axis]
width = width if width is not None else inputs_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_height = conv_utils.deconv_output_length(height,
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h,
dilation=self.dilation_rate[0])
out_width = conv_utils.deconv_output_length(width,
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w,
dilation=self.dilation_rate[1])
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
output_shape_tensor = array_ops_stack.stack(output_shape)
outputs = backend.conv2d_transpose(
inputs,
self.kernel,
output_shape_tensor,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if not context.executing_eagerly():
# Infer the static output shape:
out_shape = self.compute_output_shape(inputs.shape)
outputs.set_shape(out_shape)
if self.use_bias:
outputs = nn.bias_add(
outputs,
self.bias,
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, h_axis, w_axis = 1, 2, 3
else:
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
output_shape[c_axis] = self.filters
output_shape[h_axis] = conv_utils.deconv_output_length(
output_shape[h_axis],
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h,
dilation=self.dilation_rate[0])
output_shape[w_axis] = conv_utils.deconv_output_length(
output_shape[w_axis],
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w,
dilation=self.dilation_rate[1])
return tensor_shape.TensorShape(output_shape)
def get_config(self):
config = super(Conv2DTranspose, self).get_config()
config['output_padding'] = self.output_padding
return config
class Conv3DTranspose(Conv3D):
"""Transposed convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers or `None`, does not include the sample axis),
e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels
if `data_format="channels_last"`.
Args:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of 3 integers, specifying the
depth, height and width of the 3D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the convolution along the depth, height
and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
output_padding: An integer or tuple/list of 3 integers,
specifying the amount of padding along the depth, height, and
width.
Can be a single integer to specify the same value for all
spatial dimensions.
The amount of output padding along a given dimension must be
lower than the stride along that same dimension.
If set to `None` (default), the output shape is inferred.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, channels, depth, height, width)`.
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".
dilation_rate: an integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix (
see `keras.initializers`). Defaults to 'glorot_uniform'.
bias_initializer: Initializer for the bias vector (
see `keras.initializers`). Defaults to 'zeros'.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix (
see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (
see `keras.regularizers`).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation") (
see `keras.regularizers`).
kernel_constraint: Constraint function applied to the kernel matrix (
see `keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (
see `keras.constraints`).
Input shape:
5D tensor with shape:
`(batch_size, channels, depth, rows, cols)` if data_format='channels_first'
or 5D tensor with shape:
`(batch_size, depth, rows, cols, channels)` if data_format='channels_last'.
Output shape:
5D tensor with shape:
`(batch_size, filters, new_depth, new_rows, new_cols)` if
data_format='channels_first'
or 5D tensor with shape:
`(batch_size, new_depth, new_rows, new_cols, filters)` if
data_format='channels_last'.
`depth` and `rows` and `cols` values might have changed due to padding.
If `output_padding` is specified::
```
new_depth = ((depth - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
output_padding[0])
new_rows = ((rows - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
output_padding[1])
new_cols = ((cols - 1) * strides[2] + kernel_size[2] - 2 * padding[2] +
output_padding[2])
```
Returns:
A tensor of rank 5 representing
`activation(conv3dtranspose(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is "causal".
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
References:
- [A guide to convolution arithmetic for deep
learning](https://arxiv.org/abs/1603.07285v1)
- [Deconvolutional
Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
output_padding=None,
data_format=None,
dilation_rate=(1, 1, 1),
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):
super(Conv3DTranspose, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
self.output_padding = output_padding
if self.output_padding is not None:
self.output_padding = conv_utils.normalize_tuple(
self.output_padding, 3, 'output_padding')
for stride, out_pad in zip(self.strides, self.output_padding):
if out_pad >= stride:
raise ValueError('Stride ' + str(self.strides) + ' must be '
'greater than output padding ' +
str(self.output_padding))
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if len(input_shape) != 5:
raise ValueError('Inputs should have rank 5, received input shape:',
str(input_shape))
channel_axis = self._get_channel_axis()
if input_shape.dims[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined, found None: ' + str(input_shape))
input_dim = int(input_shape[channel_axis])
kernel_shape = self.kernel_size + (self.filters, input_dim)
self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})
self.kernel = self.add_weight(
'kernel',
shape=kernel_shape,
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
trainable=True,
dtype=self.dtype)
if self.use_bias:
self.bias = self.add_weight(
'bias',
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype)
else:
self.bias = None
self.built = True
def call(self, inputs):
inputs_shape = array_ops.shape(inputs)
batch_size = inputs_shape[0]
if self.data_format == 'channels_first':
d_axis, h_axis, w_axis = 2, 3, 4
else:
d_axis, h_axis, w_axis = 1, 2, 3
depth = inputs_shape[d_axis]
height = inputs_shape[h_axis]
width = inputs_shape[w_axis]
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_d = out_pad_h = out_pad_w = None
else:
out_pad_d, out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_depth = conv_utils.deconv_output_length(depth,
kernel_d,
padding=self.padding,
output_padding=out_pad_d,
stride=stride_d)
out_height = conv_utils.deconv_output_length(height,
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h)
out_width = conv_utils.deconv_output_length(width,
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_depth, out_height,
out_width)
strides = (1, 1, stride_d, stride_h, stride_w)
else:
output_shape = (batch_size, out_depth, out_height, out_width,
self.filters)
strides = (1, stride_d, stride_h, stride_w, 1)
output_shape_tensor = array_ops_stack.stack(output_shape)
outputs = nn.conv3d_transpose(
inputs,
self.kernel,
output_shape_tensor,
strides,
data_format=conv_utils.convert_data_format(self.data_format, ndim=5),
padding=self.padding.upper())
if not context.executing_eagerly():
# Infer the static output shape:
out_shape = self.compute_output_shape(inputs.shape)
outputs.set_shape(out_shape)
if self.use_bias:
outputs = nn.bias_add(
outputs,
self.bias,
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
else:
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_d = out_pad_h = out_pad_w = None
else:
out_pad_d, out_pad_h, out_pad_w = self.output_padding
output_shape[c_axis] = self.filters
output_shape[d_axis] = conv_utils.deconv_output_length(
output_shape[d_axis],
kernel_d,
padding=self.padding,
output_padding=out_pad_d,
stride=stride_d)
output_shape[h_axis] = conv_utils.deconv_output_length(
output_shape[h_axis],
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h)
output_shape[w_axis] = conv_utils.deconv_output_length(
output_shape[w_axis],
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w)
return tensor_shape.TensorShape(output_shape)
def get_config(self):
config = super(Conv3DTranspose, self).get_config()
config.pop('dilation_rate')
config['output_padding'] = self.output_padding
return config
class SeparableConv(Conv):
"""Abstract base layer for separable nD convolution.
This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If `use_bias` is True and a bias initializer is provided,
it adds a bias vector to the output.
It then optionally applies an activation function to produce the final output.
Args:
rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A tuple or list of integers specifying the spatial
dimensions of the filters. Can be a single integer to specify the same
value for all spatial dimensions.
strides: A tuple or list of integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any `stride` value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, ..., channels)` while `channels_first` corresponds to
inputs with shape `(batch_size, channels, ...)`.
dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
depth_multiplier: The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to `num_filters_in * depth_multiplier`.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias.
depthwise_initializer: An initializer for the depthwise convolution kernel (
see `keras.initializers`). If None, then the default initializer (
'glorot_uniform') will be used.
pointwise_initializer: An initializer for the pointwise convolution kernel (
see `keras.initializers`). If None, then the default initializer
('glorot_uniform') will be used.
bias_initializer: An initializer for the bias vector. If None, the default
initializer ('zeros') will be used (see `keras.initializers`).
depthwise_regularizer: Optional regularizer for the depthwise
convolution kernel.
pointwise_regularizer: Optional regularizer for the pointwise
convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
depthwise_constraint: Optional projection function to be applied to the
depthwise kernel after being updated by an `Optimizer` (e.g. used for
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.
pointwise_constraint: Optional projection function to be applied to the
pointwise kernel after being updated by an `Optimizer`.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` the weights of this layer will be marked as
trainable (and listed in `layer.trainable_weights`).
"""
def __init__(self,
rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(SeparableConv, self).__init__(
rank=rank,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
bias_initializer=initializers.get(bias_initializer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.pointwise_initializer = initializers.get(pointwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.pointwise_regularizer = regularizers.get(pointwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.pointwise_constraint = constraints.get(pointwise_constraint)
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
channel_axis = self._get_channel_axis()
if input_shape.dims[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = int(input_shape[channel_axis])
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: input_dim})
depthwise_kernel_shape = self.kernel_size + (input_dim,
self.depth_multiplier)
pointwise_kernel_shape = (
1,) * self.rank + (self.depth_multiplier * input_dim, self.filters)
self.depthwise_kernel = self.add_weight(
name='depthwise_kernel',
shape=depthwise_kernel_shape,
initializer=self.depthwise_initializer,
regularizer=self.depthwise_regularizer,
constraint=self.depthwise_constraint,
trainable=True,
dtype=self.dtype)
self.pointwise_kernel = self.add_weight(
name='pointwise_kernel',
shape=pointwise_kernel_shape,
initializer=self.pointwise_initializer,
regularizer=self.pointwise_regularizer,
constraint=self.pointwise_constraint,
trainable=True,
dtype=self.dtype)
if self.use_bias:
self.bias = self.add_weight(
name='bias',
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype)
else:
self.bias = None
self.built = True
def call(self, inputs):
raise NotImplementedError
def get_config(self):
config = {
'filters':
self.filters,
'kernel_size':
self.kernel_size,
'strides':
self.strides,
'padding':
self.padding,
'data_format':
self.data_format,
'depth_multiplier':
self.depth_multiplier,
'dilation_rate':
self.dilation_rate,
'activation':
activations.serialize(self.activation),
'use_bias':
self.use_bias,
'depthwise_initializer':
initializers.serialize(self.depthwise_initializer),
'pointwise_initializer':
initializers.serialize(self.pointwise_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'depthwise_regularizer':
regularizers.serialize(self.depthwise_regularizer),
'pointwise_regularizer':
regularizers.serialize(self.pointwise_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'depthwise_constraint':
constraints.serialize(self.depthwise_constraint),
'pointwise_constraint':
constraints.serialize(self.pointwise_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint)
}
base_config = super(SeparableConv, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class SeparableConv1D(SeparableConv):
"""Depthwise separable 1D convolution.
This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If `use_bias` is True and a bias initializer is provided,
it adds a bias vector to the output.
It then optionally applies an activation function to produce the final output.
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A single integer specifying the spatial
dimensions of the filters.
strides: A single integer specifying the strides
of the convolution.
Specifying any `stride` value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"`, `"same"`, or `"causal"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input. `"causal"` results in causal
(dilated) convolutions, e.g. `output[t]` does not depend on `input[t+1:]`.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch_size, channels, length)`.
dilation_rate: A single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
depth_multiplier: The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to `num_filters_in * depth_multiplier`.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias.
depthwise_initializer: An initializer for the depthwise convolution kernel (
see `keras.initializers`). If None, then the default initializer (
'glorot_uniform') will be used.
pointwise_initializer: An initializer for the pointwise convolution kernel (
see `keras.initializers`). If None, then the default initializer
('glorot_uniform') will be used.
bias_initializer: An initializer for the bias vector. If None, the default
initializer ('zeros') will be used (see `keras.initializers`).
depthwise_regularizer: Optional regularizer for the depthwise
convolution kernel (see `keras.regularizers`).
pointwise_regularizer: Optional regularizer for the pointwise
convolution kernel (see `keras.regularizers`).
bias_regularizer: Optional regularizer for the bias vector (
see `keras.regularizers`).
activity_regularizer: Optional regularizer function for the output (
see `keras.regularizers`).
depthwise_constraint: Optional projection function to be applied to the
depthwise kernel after being updated by an `Optimizer` (e.g. used for
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 (
see `keras.constraints`).
pointwise_constraint: Optional projection function to be applied to the
pointwise kernel after being updated by an `Optimizer` (
see `keras.constraints`).
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer` (
see `keras.constraints`).
trainable: Boolean, if `True` the weights of this layer will be marked as
trainable (and listed in `layer.trainable_weights`).
Input shape:
3D tensor with shape:
`(batch_size, channels, steps)` if data_format='channels_first'
or 5D tensor with shape:
`(batch_size, steps, channels)` if data_format='channels_last'.
Output shape:
3D tensor with shape:
`(batch_size, filters, new_steps)` if data_format='channels_first'
or 3D tensor with shape:
`(batch_size, new_steps, filters)` if data_format='channels_last'.
`new_steps` value might have changed due to padding or strides.
Returns:
A tensor of rank 3 representing
`activation(separableconv1d(inputs, kernel) + bias)`.
Raises:
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
"""
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs):
super(SeparableConv1D, self).__init__(
rank=1,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=activations.get(activation),
use_bias=use_bias,
depthwise_initializer=initializers.get(depthwise_initializer),
pointwise_initializer=initializers.get(pointwise_initializer),
bias_initializer=initializers.get(bias_initializer),
depthwise_regularizer=regularizers.get(depthwise_regularizer),
pointwise_regularizer=regularizers.get(pointwise_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
depthwise_constraint=constraints.get(depthwise_constraint),
pointwise_constraint=constraints.get(pointwise_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
def call(self, inputs):
if self.padding == 'causal':
inputs = array_ops.pad(inputs, self._compute_causal_padding(inputs))
if self.data_format == 'channels_last':
strides = (1,) + self.strides * 2 + (1,)
spatial_start_dim = 1
else:
strides = (1, 1) + self.strides * 2
spatial_start_dim = 2
# Explicitly broadcast inputs and kernels to 4D.
# TODO(fchollet): refactor when a native separable_conv1d op is available.
inputs = array_ops.expand_dims(inputs, spatial_start_dim)
depthwise_kernel = array_ops.expand_dims(self.depthwise_kernel, 0)
pointwise_kernel = array_ops.expand_dims(self.pointwise_kernel, 0)
dilation_rate = (1,) + self.dilation_rate
if self.padding == 'causal':
op_padding = 'valid'
else:
op_padding = self.padding
outputs = nn.separable_conv2d(
inputs,
depthwise_kernel,
pointwise_kernel,
strides=strides,
padding=op_padding.upper(),
rate=dilation_rate,
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
if self.use_bias:
outputs = nn.bias_add(
outputs,
self.bias,
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
outputs = array_ops.squeeze(outputs, [spatial_start_dim])
if self.activation is not None:
return self.activation(outputs)
return outputs
class SeparableConv2D(SeparableConv):
"""Depthwise separable 2D convolution.
Separable convolutions consist of first performing
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes the resulting
output channels. The `depth_multiplier` argument controls how many
output channels are generated per input channel in the depthwise step.
Intuitively, separable convolutions can be understood as
a way to factorize a convolution kernel into two smaller kernels,
or as an extreme version of an Inception block.
Args:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
height and width of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions. Current implementation only supports equal
length strides in the row and column dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, channels, height, width)`.
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".
dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to `filters_in * depth_multiplier`.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
depthwise_initializer: An initializer for the depthwise convolution kernel (
see `keras.initializers`). If None, then the default initializer (
'glorot_uniform') will be used.
pointwise_initializer: An initializer for the pointwise convolution kernel (
see `keras.initializers`). If None, then the default initializer
('glorot_uniform') will be used.
bias_initializer: An initializer for the bias vector. If None, the default
initializer ('zeros') will be used (see `keras.initializers`).
depthwise_regularizer: Regularizer function applied to
the depthwise kernel matrix (see `keras.regularizers`).
pointwise_regularizer: Regularizer function applied to
the pointwise kernel matrix (see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (
see `keras.regularizers`).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation") (
see `keras.regularizers`).
depthwise_constraint: Constraint function applied to
the depthwise kernel matrix (
see `keras.constraints`).
pointwise_constraint: Constraint function applied to
the pointwise kernel matrix (
see `keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (
see `keras.constraints`).
Input shape:
4D tensor with shape:
`(batch_size, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch_size, rows, cols, channels)` if data_format='channels_last'.
Output shape:
4D tensor with shape:
`(batch_size, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch_size, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
Returns:
A tensor of rank 4 representing
`activation(separableconv2d(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is "causal".
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs):
super(SeparableConv2D, self).__init__(
rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=activations.get(activation),
use_bias=use_bias,
depthwise_initializer=initializers.get(depthwise_initializer),
pointwise_initializer=initializers.get(pointwise_initializer),
bias_initializer=initializers.get(bias_initializer),
depthwise_regularizer=regularizers.get(depthwise_regularizer),
pointwise_regularizer=regularizers.get(pointwise_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
depthwise_constraint=constraints.get(depthwise_constraint),
pointwise_constraint=constraints.get(pointwise_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
def call(self, inputs):
# Apply the actual ops.
if self.data_format == 'channels_last':
strides = (1,) + self.strides + (1,)
else:
strides = (1, 1) + self.strides
outputs = nn.separable_conv2d(
inputs,
self.depthwise_kernel,
self.pointwise_kernel,
strides=strides,
padding=self.padding.upper(),
rate=self.dilation_rate,
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
if self.use_bias:
outputs = nn.bias_add(
outputs,
self.bias,
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
if self.activation is not None:
return self.activation(outputs)
return outputs
class DepthwiseConv2D(Conv2D):
"""Depthwise 2D convolution.
Depthwise convolution is a type of convolution in which a single convolutional
filter is apply to each input channel (i.e. in a depthwise way).
You can understand depthwise convolution as being
the first step in a depthwise separable convolution.
It is implemented via the following steps:
- Split the input into individual channels.
- Convolve each input with the layer's kernel (called a depthwise kernel).
- Stack the convolved outputs together (along the channels axis).
Unlike a regular 2D convolution, depthwise convolution does not mix
information across different input channels.
The `depth_multiplier` argument controls how many
output channels are generated per input channel in the depthwise step.
Args:
kernel_size: An integer or tuple/list of 2 integers, specifying the
height and width of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `'valid'` or `'same'` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding with zeros evenly
to the left/right or up/down of the input such that output has the same
height/width dimension as the input.
depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to `filters_in * depth_multiplier`.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, channels, height, width)`.
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'.
dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
activation: Activation function to use.
If you don't specify anything, no activation is applied (
see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
depthwise_initializer: Initializer for the depthwise kernel matrix (
see `keras.initializers`). If None, the default initializer (
'glorot_uniform') will be used.
bias_initializer: Initializer for the bias vector (
see `keras.initializers`). If None, the default initializer (
'zeros') will bs used.
depthwise_regularizer: Regularizer function applied to
the depthwise kernel matrix (see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector (
see `keras.regularizers`).
activity_regularizer: Regularizer function applied to
the output of the layer (its 'activation') (
see `keras.regularizers`).
depthwise_constraint: Constraint function applied to
the depthwise kernel matrix (
see `keras.constraints`).
bias_constraint: Constraint function applied to the bias vector (
see `keras.constraints`).
Input shape:
4D tensor with shape:
`[batch_size, channels, rows, cols]` if data_format='channels_first'
or 4D tensor with shape:
`[batch_size, rows, cols, channels]` if data_format='channels_last'.
Output shape:
4D tensor with shape:
`[batch_size, channels * depth_multiplier, new_rows, new_cols]` if
data_format='channels_first' or 4D tensor with shape:
`[batch_size, new_rows, new_cols, channels * depth_multiplier]` if
data_format='channels_last'. `rows` and `cols` values might have
changed due to padding.
Returns:
A tensor of rank 4 representing
`activation(depthwiseconv2d(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is "causal".
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
"""
def __init__(self,
kernel_size,
strides=(1, 1),
padding='valid',
depth_multiplier=1,
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs):
super(DepthwiseConv2D, self).__init__(
filters=None,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.bias_initializer = initializers.get(bias_initializer)
def build(self, input_shape):
if len(input_shape) < 4:
raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. '
'Received input shape:', str(input_shape))
input_shape = tensor_shape.TensorShape(input_shape)
channel_axis = self._get_channel_axis()
if input_shape.dims[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs to '
'`DepthwiseConv2D` '
'should be defined. Found `None`.')
input_dim = int(input_shape[channel_axis])
depthwise_kernel_shape = (self.kernel_size[0],
self.kernel_size[1],
input_dim,
self.depth_multiplier)
self.depthwise_kernel = self.add_weight(
shape=depthwise_kernel_shape,
initializer=self.depthwise_initializer,
name='depthwise_kernel',
regularizer=self.depthwise_regularizer,
constraint=self.depthwise_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(input_dim * self.depth_multiplier,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
self.built = True
def call(self, inputs):
outputs = backend.depthwise_conv2d(
inputs,
self.depthwise_kernel,
strides=self.strides,
padding=self.padding,
dilation_rate=self.dilation_rate,
data_format=self.data_format)
if self.use_bias:
outputs = backend.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0],
self.dilation_rate[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1],
self.dilation_rate[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
def get_config(self):
config = super(DepthwiseConv2D, self).get_config()
config.pop('filters')
config.pop('kernel_initializer')
config.pop('kernel_regularizer')
config.pop('kernel_constraint')
config['depth_multiplier'] = self.depth_multiplier
config['depthwise_initializer'] = initializers.serialize(
self.depthwise_initializer)
config['depthwise_regularizer'] = regularizers.serialize(
self.depthwise_regularizer)
config['depthwise_constraint'] = constraints.serialize(
self.depthwise_constraint)
return config
class UpSampling1D(Layer):
"""Upsampling layer for 1D inputs.
Repeats each temporal step `size` times along the time axis.
Examples:
>>> input_shape = (2, 2, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> print(x)
[[[ 0 1 2]
[ 3 4 5]]
[[ 6 7 8]
[ 9 10 11]]]
>>> y = tf.keras.layers.UpSampling1D(size=2)(x)
>>> print(y)
tf.Tensor(
[[[ 0 1 2]
[ 0 1 2]
[ 3 4 5]
[ 3 4 5]]
[[ 6 7 8]
[ 6 7 8]
[ 9 10 11]
[ 9 10 11]]], shape=(2, 4, 3), dtype=int64)
Args:
size: Integer. Upsampling factor.
Input shape:
3D tensor with shape: `(batch_size, steps, features)`.
Output shape:
3D tensor with shape: `(batch_size, upsampled_steps, features)`.
"""
def __init__(self, size=2, **kwargs):
super(UpSampling1D, self).__init__(**kwargs)
self.size = int(size)
self.input_spec = InputSpec(ndim=3)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
size = self.size * input_shape[1] if input_shape[1] is not None else None
return tensor_shape.TensorShape([input_shape[0], size, input_shape[2]])
def call(self, inputs):
output = backend.repeat_elements(inputs, self.size, axis=1)
return output
def get_config(self):
config = {'size': self.size}
base_config = super(UpSampling1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class UpSampling2D(Layer):
"""Upsampling layer for 2D inputs.
Repeats the rows and columns of the data
by `size[0]` and `size[1]` respectively.
Examples:
>>> input_shape = (2, 2, 1, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> print(x)
[[[[ 0 1 2]]
[[ 3 4 5]]]
[[[ 6 7 8]]
[[ 9 10 11]]]]
>>> y = tf.keras.layers.UpSampling2D(size=(1, 2))(x)
>>> print(y)
tf.Tensor(
[[[[ 0 1 2]
[ 0 1 2]]
[[ 3 4 5]
[ 3 4 5]]]
[[[ 6 7 8]
[ 6 7 8]]
[[ 9 10 11]
[ 9 10 11]]]], shape=(2, 2, 2, 3), dtype=int64)
Args:
size: Int, or tuple of 2 integers.
The upsampling factors for rows and columns.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, channels, height, width)`.
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".
interpolation: A string, one of `nearest` or `bilinear`.
Input shape:
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch_size, rows, cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, rows, cols)`
Output shape:
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch_size, upsampled_rows, upsampled_cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, upsampled_rows, upsampled_cols)`
"""
def __init__(self,
size=(2, 2),
data_format=None,
interpolation='nearest',
**kwargs):
super(UpSampling2D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
self.size = conv_utils.normalize_tuple(size, 2, 'size')
if interpolation not in {'nearest', 'bilinear'}:
raise ValueError('`interpolation` argument should be one of `"nearest"` '
'or `"bilinear"`.')
self.interpolation = interpolation
self.input_spec = InputSpec(ndim=4)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_first':
height = self.size[0] * input_shape[
2] if input_shape[2] is not None else None
width = self.size[1] * input_shape[
3] if input_shape[3] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], height, width])
else:
height = self.size[0] * input_shape[
1] if input_shape[1] is not None else None
width = self.size[1] * input_shape[
2] if input_shape[2] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], height, width, input_shape[3]])
def call(self, inputs):
return backend.resize_images(
inputs, self.size[0], self.size[1], self.data_format,
interpolation=self.interpolation)
def get_config(self):
config = {
'size': self.size,
'data_format': self.data_format,
'interpolation': self.interpolation
}
base_config = super(UpSampling2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class UpSampling3D(Layer):
"""Upsampling layer for 3D inputs.
Repeats the 1st, 2nd and 3rd dimensions
of the data by `size[0]`, `size[1]` and `size[2]` respectively.
Examples:
>>> input_shape = (2, 1, 2, 1, 3)
>>> x = tf.constant(1, shape=input_shape)
>>> y = tf.keras.layers.UpSampling3D(size=2)(x)
>>> print(y.shape)
(2, 2, 4, 2, 3)
Args:
size: Int, or tuple of 3 integers.
The upsampling factors for dim1, dim2 and dim3.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
while `channels_first` corresponds to inputs with shape
`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
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:
- If `data_format` is `"channels_last"`:
`(batch_size, dim1, dim2, dim3, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, dim1, dim2, dim3)`
Output shape:
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`
"""
def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
self.data_format = conv_utils.normalize_data_format(data_format)
self.size = conv_utils.normalize_tuple(size, 3, 'size')
self.input_spec = InputSpec(ndim=5)
super(UpSampling3D, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_first':
dim1 = self.size[0] * input_shape[
2] if input_shape[2] is not None else None
dim2 = self.size[1] * input_shape[
3] if input_shape[3] is not None else None
dim3 = self.size[2] * input_shape[
4] if input_shape[4] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], dim1, dim2, dim3])
else:
dim1 = self.size[0] * input_shape[
1] if input_shape[1] is not None else None
dim2 = self.size[1] * input_shape[
2] if input_shape[2] is not None else None
dim3 = self.size[2] * input_shape[
3] if input_shape[3] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], dim1, dim2, dim3, input_shape[4]])
def call(self, inputs):
return backend.resize_volumes(
inputs, self.size[0], self.size[1], self.size[2], self.data_format)
def get_config(self):
config = {'size': self.size, 'data_format': self.data_format}
base_config = super(UpSampling3D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ZeroPadding1D(Layer):
"""Zero-padding layer for 1D input (e.g. temporal sequence).
Examples:
>>> input_shape = (2, 2, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> print(x)
[[[ 0 1 2]
[ 3 4 5]]
[[ 6 7 8]
[ 9 10 11]]]
>>> y = tf.keras.layers.ZeroPadding1D(padding=2)(x)
>>> print(y)
tf.Tensor(
[[[ 0 0 0]
[ 0 0 0]
[ 0 1 2]
[ 3 4 5]
[ 0 0 0]
[ 0 0 0]]
[[ 0 0 0]
[ 0 0 0]
[ 6 7 8]
[ 9 10 11]
[ 0 0 0]
[ 0 0 0]]], shape=(2, 6, 3), dtype=int64)
Args:
padding: Int, or tuple of int (length 2), or dictionary.
- If int:
How many zeros to add at the beginning and end of
the padding dimension (axis 1).
- If tuple of int (length 2):
How many zeros to add at the beginning and the end of
the padding dimension (`(left_pad, right_pad)`).
Input shape:
3D tensor with shape `(batch_size, axis_to_pad, features)`
Output shape:
3D tensor with shape `(batch_size, padded_axis, features)`
"""
def __init__(self, padding=1, **kwargs):
super(ZeroPadding1D, self).__init__(**kwargs)
self.padding = conv_utils.normalize_tuple(padding, 2, 'padding')
self.input_spec = InputSpec(ndim=3)
def compute_output_shape(self, input_shape):
if input_shape[1] is not None:
length = input_shape[1] + self.padding[0] + self.padding[1]
else:
length = None
return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
def call(self, inputs):
return backend.temporal_padding(inputs, padding=self.padding)
def get_config(self):
config = {'padding': self.padding}
base_config = super(ZeroPadding1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ZeroPadding2D(Layer):
"""Zero-padding layer for 2D input (e.g. picture).
This layer can add rows and columns of zeros
at the top, bottom, left and right side of an image tensor.
Examples:
>>> input_shape = (1, 1, 2, 2)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> print(x)
[[[[0 1]
[2 3]]]]
>>> y = tf.keras.layers.ZeroPadding2D(padding=1)(x)
>>> print(y)
tf.Tensor(
[[[[0 0]
[0 0]
[0 0]
[0 0]]
[[0 0]
[0 1]
[2 3]
[0 0]]
[[0 0]
[0 0]
[0 0]
[0 0]]]], shape=(1, 3, 4, 2), dtype=int64)
Args:
padding: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric padding
is applied to height and width.
- If tuple of 2 ints:
interpreted as two different
symmetric padding values for height and width:
`(symmetric_height_pad, symmetric_width_pad)`.
- If tuple of 2 tuples of 2 ints:
interpreted as
`((top_pad, bottom_pad), (left_pad, right_pad))`
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, channels, height, width)`.
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:
- If `data_format` is `"channels_last"`:
`(batch_size, rows, cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, rows, cols)`
Output shape:
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch_size, padded_rows, padded_cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, padded_rows, padded_cols)`
"""
def __init__(self, padding=(1, 1), data_format=None, **kwargs):
super(ZeroPadding2D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(padding, int):
self.padding = ((padding, padding), (padding, padding))
elif hasattr(padding, '__len__'):
if len(padding) != 2:
raise ValueError('`padding` should have two elements. '
'Found: ' + str(padding))
height_padding = conv_utils.normalize_tuple(padding[0], 2,
'1st entry of padding')
width_padding = conv_utils.normalize_tuple(padding[1], 2,
'2nd entry of padding')
self.padding = (height_padding, width_padding)
else:
raise ValueError('`padding` should be either an int, '
'a tuple of 2 ints '
'(symmetric_height_pad, symmetric_width_pad), '
'or a tuple of 2 tuples of 2 ints '
'((top_pad, bottom_pad), (left_pad, right_pad)). '
'Found: ' + str(padding))
self.input_spec = InputSpec(ndim=4)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_first':
if input_shape[2] is not None:
rows = input_shape[2] + self.padding[0][0] + self.padding[0][1]
else:
rows = None
if input_shape[3] is not None:
cols = input_shape[3] + self.padding[1][0] + self.padding[1][1]
else:
cols = None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], rows, cols])
elif self.data_format == 'channels_last':
if input_shape[1] is not None:
rows = input_shape[1] + self.padding[0][0] + self.padding[0][1]
else:
rows = None
if input_shape[2] is not None:
cols = input_shape[2] + self.padding[1][0] + self.padding[1][1]
else:
cols = None
return tensor_shape.TensorShape(
[input_shape[0], rows, cols, input_shape[3]])
def call(self, inputs):
return backend.spatial_2d_padding(
inputs, padding=self.padding, data_format=self.data_format)
def get_config(self):
config = {'padding': self.padding, 'data_format': self.data_format}
base_config = super(ZeroPadding2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ZeroPadding3D(Layer):
"""Zero-padding layer for 3D data (spatial or spatio-temporal).
Examples:
>>> input_shape = (1, 1, 2, 2, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> y = tf.keras.layers.ZeroPadding3D(padding=2)(x)
>>> print(y.shape)
(1, 5, 6, 6, 3)
Args:
padding: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
- If int: the same symmetric padding
is applied to height and width.
- If tuple of 3 ints:
interpreted as two different
symmetric padding values for height and width:
`(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
- If tuple of 3 tuples of 2 ints:
interpreted as
`((left_dim1_pad, right_dim1_pad), (left_dim2_pad,
right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
while `channels_first` corresponds to inputs with shape
`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
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:
- If `data_format` is `"channels_last"`:
`(batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad,
depth)`
- If `data_format` is `"channels_first"`:
`(batch_size, depth, first_axis_to_pad, second_axis_to_pad,
third_axis_to_pad)`
Output shape:
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad,
depth)`
- If `data_format` is `"channels_first"`:
`(batch_size, depth, first_padded_axis, second_padded_axis,
third_axis_to_pad)`
"""
def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
super(ZeroPadding3D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(padding, int):
self.padding = ((padding, padding), (padding, padding), (padding,
padding))
elif hasattr(padding, '__len__'):
if len(padding) != 3:
raise ValueError('`padding` should have 3 elements. '
'Found: ' + str(padding))
dim1_padding = conv_utils.normalize_tuple(padding[0], 2,
'1st entry of padding')
dim2_padding = conv_utils.normalize_tuple(padding[1], 2,
'2nd entry of padding')
dim3_padding = conv_utils.normalize_tuple(padding[2], 2,
'3rd entry of padding')
self.padding = (dim1_padding, dim2_padding, dim3_padding)
else:
raise ValueError(
'`padding` should be either an int, '
'a tuple of 3 ints '
'(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), '
'or a tuple of 3 tuples of 2 ints '
'((left_dim1_pad, right_dim1_pad),'
' (left_dim2_pad, right_dim2_pad),'
' (left_dim3_pad, right_dim2_pad)). '
'Found: ' + str(padding))
self.input_spec = InputSpec(ndim=5)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_first':
if input_shape[2] is not None:
dim1 = input_shape[2] + self.padding[0][0] + self.padding[0][1]
else:
dim1 = None
if input_shape[3] is not None:
dim2 = input_shape[3] + self.padding[1][0] + self.padding[1][1]
else:
dim2 = None
if input_shape[4] is not None:
dim3 = input_shape[4] + self.padding[2][0] + self.padding[2][1]
else:
dim3 = None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], dim1, dim2, dim3])
elif self.data_format == 'channels_last':
if input_shape[1] is not None:
dim1 = input_shape[1] + self.padding[0][0] + self.padding[0][1]
else:
dim1 = None
if input_shape[2] is not None:
dim2 = input_shape[2] + self.padding[1][0] + self.padding[1][1]
else:
dim2 = None
if input_shape[3] is not None:
dim3 = input_shape[3] + self.padding[2][0] + self.padding[2][1]
else:
dim3 = None
return tensor_shape.TensorShape(
[input_shape[0], dim1, dim2, dim3, input_shape[4]])
def call(self, inputs):
return backend.spatial_3d_padding(
inputs, padding=self.padding, data_format=self.data_format)
def get_config(self):
config = {'padding': self.padding, 'data_format': self.data_format}
base_config = super(ZeroPadding3D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Cropping1D(Layer):
"""Cropping layer for 1D input (e.g. temporal sequence).
It crops along the time dimension (axis 1).
Examples:
>>> input_shape = (2, 3, 2)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> print(x)
[[[ 0 1]
[ 2 3]
[ 4 5]]
[[ 6 7]
[ 8 9]
[10 11]]]
>>> y = tf.keras.layers.Cropping1D(cropping=1)(x)
>>> print(y)
tf.Tensor(
[[[2 3]]
[[8 9]]], shape=(2, 1, 2), dtype=int64)
Args:
cropping: Int or tuple of int (length 2)
How many units should be trimmed off at the beginning and end of
the cropping dimension (axis 1).
If a single int is provided, the same value will be used for both.
Input shape:
3D tensor with shape `(batch_size, axis_to_crop, features)`
Output shape:
3D tensor with shape `(batch_size, cropped_axis, features)`
"""
def __init__(self, cropping=(1, 1), **kwargs):
super(Cropping1D, self).__init__(**kwargs)
self.cropping = conv_utils.normalize_tuple(cropping, 2, 'cropping')
self.input_spec = InputSpec(ndim=3)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if input_shape[1] is not None:
length = input_shape[1] - self.cropping[0] - self.cropping[1]
else:
length = None
return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
def call(self, inputs):
if self.cropping[1] == 0:
return inputs[:, self.cropping[0]:, :]
else:
return inputs[:, self.cropping[0]:-self.cropping[1], :]
def get_config(self):
config = {'cropping': self.cropping}
base_config = super(Cropping1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Cropping2D(Layer):
"""Cropping layer for 2D input (e.g. picture).
It crops along spatial dimensions, i.e. height and width.
Examples:
>>> input_shape = (2, 28, 28, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> y = tf.keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
>>> print(y.shape)
(2, 24, 20, 3)
Args:
cropping: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric cropping
is applied to height and width.
- If tuple of 2 ints:
interpreted as two different
symmetric cropping values for height and width:
`(symmetric_height_crop, symmetric_width_crop)`.
- If tuple of 2 tuples of 2 ints:
interpreted as
`((top_crop, bottom_crop), (left_crop, right_crop))`
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, channels, height, width)`.
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:
- If `data_format` is `"channels_last"`:
`(batch_size, rows, cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, rows, cols)`
Output shape:
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch_size, cropped_rows, cropped_cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch_size, channels, cropped_rows, cropped_cols)`
"""
def __init__(self, cropping=((0, 0), (0, 0)), data_format=None, **kwargs):
super(Cropping2D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(cropping, int):
self.cropping = ((cropping, cropping), (cropping, cropping))
elif hasattr(cropping, '__len__'):
if len(cropping) != 2:
raise ValueError('`cropping` should have two elements. '
'Found: ' + str(cropping))
height_cropping = conv_utils.normalize_tuple(cropping[0], 2,
'1st entry of cropping')
width_cropping = conv_utils.normalize_tuple(cropping[1], 2,
'2nd entry of cropping')
self.cropping = (height_cropping, width_cropping)
else:
raise ValueError('`cropping` should be either an int, '
'a tuple of 2 ints '
'(symmetric_height_crop, symmetric_width_crop), '
'or a tuple of 2 tuples of 2 ints '
'((top_crop, bottom_crop), (left_crop, right_crop)). '
'Found: ' + str(cropping))
self.input_spec = InputSpec(ndim=4)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
# pylint: disable=invalid-unary-operand-type
if self.data_format == 'channels_first':
return tensor_shape.TensorShape([
input_shape[0], input_shape[1],
input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
if input_shape[2] else None,
input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
if input_shape[3] else None
])
else:
return tensor_shape.TensorShape([
input_shape[0],
input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
if input_shape[1] else None,
input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
if input_shape[2] else None, input_shape[3]
])
# pylint: enable=invalid-unary-operand-type
def call(self, inputs):
# pylint: disable=invalid-unary-operand-type
if self.data_format == 'channels_first':
if self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:]
elif self.cropping[0][1] == 0:
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
-self.cropping[1][1]]
elif self.cropping[1][1] == 0:
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
self.cropping[1][0]:]
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
self.cropping[1][0]:-self.cropping[1][1]]
else:
if self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:, :]
elif self.cropping[0][1] == 0:
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
-self.cropping[1][1], :]
elif self.cropping[1][1] == 0:
return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
self.cropping[1][0]:, :]
return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
1][0]:-self.cropping[1][1], :] # pylint: disable=invalid-unary-operand-type
# pylint: enable=invalid-unary-operand-type
def get_config(self):
config = {'cropping': self.cropping, 'data_format': self.data_format}
base_config = super(Cropping2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Cropping3D(Layer):
"""Cropping layer for 3D data (e.g. spatial or spatio-temporal).
Examples:
>>> input_shape = (2, 28, 28, 10, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> y = tf.keras.layers.Cropping3D(cropping=(2, 4, 2))(x)
>>> print(y.shape)
(2, 24, 20, 6, 3)
Args:
cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
- If int: the same symmetric cropping
is applied to depth, height, and width.
- If tuple of 3 ints: interpreted as two different
symmetric cropping values for depth, height, and width:
`(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
- If tuple of 3 tuples of 2 ints: interpreted as
`((left_dim1_crop, right_dim1_crop), (left_dim2_crop,
right_dim2_crop), (left_dim3_crop, right_dim3_crop))`
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
while `channels_first` corresponds to inputs with shape
`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
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:
- If `data_format` is `"channels_last"`:
`(batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop,
depth)`
- If `data_format` is `"channels_first"`:
`(batch_size, depth, first_axis_to_crop, second_axis_to_crop,
third_axis_to_crop)`
Output shape:
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis,
depth)`
- If `data_format` is `"channels_first"`:
`(batch_size, depth, first_cropped_axis, second_cropped_axis,
third_cropped_axis)`
"""
def __init__(self,
cropping=((1, 1), (1, 1), (1, 1)),
data_format=None,
**kwargs):
super(Cropping3D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(cropping, int):
self.cropping = ((cropping, cropping), (cropping, cropping), (cropping,
cropping))
elif hasattr(cropping, '__len__'):
if len(cropping) != 3:
raise ValueError('`cropping` should have 3 elements. '
'Found: ' + str(cropping))
dim1_cropping = conv_utils.normalize_tuple(cropping[0], 2,
'1st entry of cropping')
dim2_cropping = conv_utils.normalize_tuple(cropping[1], 2,
'2nd entry of cropping')
dim3_cropping = conv_utils.normalize_tuple(cropping[2], 2,
'3rd entry of cropping')
self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping)
else:
raise ValueError(
'`cropping` should be either an int, '
'a tuple of 3 ints '
'(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop), '
'or a tuple of 3 tuples of 2 ints '
'((left_dim1_crop, right_dim1_crop),'
' (left_dim2_crop, right_dim2_crop),'
' (left_dim3_crop, right_dim2_crop)). '
'Found: ' + str(cropping))
self.input_spec = InputSpec(ndim=5)
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
# pylint: disable=invalid-unary-operand-type
if self.data_format == 'channels_first':
if input_shape[2] is not None:
dim1 = input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
else:
dim1 = None
if input_shape[3] is not None:
dim2 = input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
else:
dim2 = None
if input_shape[4] is not None:
dim3 = input_shape[4] - self.cropping[2][0] - self.cropping[2][1]
else:
dim3 = None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], dim1, dim2, dim3])
elif self.data_format == 'channels_last':
if input_shape[1] is not None:
dim1 = input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
else:
dim1 = None
if input_shape[2] is not None:
dim2 = input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
else:
dim2 = None
if input_shape[3] is not None:
dim3 = input_shape[3] - self.cropping[2][0] - self.cropping[2][1]
else:
dim3 = None
return tensor_shape.TensorShape(
[input_shape[0], dim1, dim2, dim3, input_shape[4]])
# pylint: enable=invalid-unary-operand-type
def call(self, inputs):
# pylint: disable=invalid-unary-operand-type
if self.data_format == 'channels_first':
if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
self.cropping[2][0]:]
elif self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
self.cropping[2][0]:-self.cropping[2][1]]
elif self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
self.cropping[1][0]:, self.cropping[2][0]:]
elif self.cropping[0][1] == self.cropping[2][1] == 0:
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
-self.cropping[1][1], self.cropping[2][0]:]
elif self.cropping[0][1] == 0:
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][
0]:-self.cropping[1][1], self.cropping[2][0]:-self.cropping[2][1]]
elif self.cropping[1][1] == 0:
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
cropping[1][0]:, self.cropping[2][0]:-self.cropping[2][1]]
elif self.cropping[2][1] == 0:
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
cropping[1][0]:-self.cropping[1][1], self.cropping[2][0]:]
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
self.cropping[1][0]:-self.cropping[1][1], self.cropping[2][
0]:-self.cropping[2][1]]
else:
if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
self.cropping[2][0]:, :]
elif self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
self.cropping[2][0]:-self.cropping[2][1], :]
elif self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
self.cropping[1][0]:, self.cropping[2][0]:, :]
elif self.cropping[0][1] == self.cropping[2][1] == 0:
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
-self.cropping[1][1], self.cropping[2][0]:, :]
elif self.cropping[0][1] == 0:
return inputs[:, self.cropping[0][0]:, self.cropping[1][
0]:-self.cropping[1][1], self.cropping[2][0]:
-self.cropping[2][1], :]
elif self.cropping[1][1] == 0:
return inputs[:, self.cropping[0][
0]:-self.cropping[0][1], self.cropping[1][0]:, self.cropping[2][0]:
-self.cropping[2][1], :]
elif self.cropping[2][1] == 0:
return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
self.cropping[1][0]:-self.cropping[1][1], self.cropping[
2][0]:, :]
return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
1][0]:-self.cropping[1][1], self.cropping[2][0]: # pylint: disable=invalid-unary-operand-type
-self.cropping[2][1], :] # pylint: disable=invalid-unary-operand-type
# pylint: enable=invalid-unary-operand-type
def get_config(self):
config = {'cropping': self.cropping, 'data_format': self.data_format}
base_config = super(Cropping3D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# Aliases
Convolution1D = Conv1D
Convolution2D = Conv2D
Convolution3D = Conv3D
SeparableConvolution1D = SeparableConv1D
SeparableConvolution2D = SeparableConv2D
Convolution2DTranspose = Conv2DTranspose
Convolution3DTranspose = Conv3DTranspose
Deconvolution2D = Deconv2D = Conv2DTranspose
Deconvolution3D = Deconv3D = Conv3DTranspose