def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
**kwargs):
"""Create a ``Conv3D`` Layer.
Parameters
----------
filters : int
The number of output filters.
kernel_size : Union[int, Sequence[int]]
The shape of convolution window.
strides : Union[int, Sequence[int]], optional, default=1
The stride of convolution window.
padding : Union[str, Sequence[int]], optional
The padding algorithm or size.
data_format : str, optional, default='channels_last'
``'channels_first'`` or ``'channels_last'``.
dilation_rate : Union[int, Sequence[int]], optional, default=1
The rate of dilated convolution.
activation : Union[callable, str], optional
The optional activation function.
use_bias : bool, optional, default=True
Add a bias tensor to output or not.
kernel_initializer : Union[callable, str], optional
The initializer for kernel tensor.
bias_initializer : Union[callable, str], optional
The initializer for bias tensor.
kernel_regularizer : Union[callable, str], optional
The regularizer for kernel tensor.
bias_regularizer : Union[callable, str], optional
The regularizer for bias tensor.
"""
super(Conv3D, self).__init__(
rank=3,
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),
**kwargs)
def __init__(self, activation, **kwargs):
"""Create an ``Activation`` layer.
Parameters
----------
activation : Union[callable, str], optional
The optional activation function.
"""
super(Activation, self).__init__(**kwargs)
self.activation = activations.get(activation)
self.inplace = kwargs.get('inplace', False)
def __init__(self,
units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
**kwargs):
"""Create a ``Dense`` layer.
Parameters
----------
units : int
The number of output units.
activation : Union[callable, str], optional
The optional activation function.
use_bias : bool, optional, default=True
``True`` to apply a ``bias``.
kernel_initializer : Union[callable, str], optional
The initializer for kernel tensor.
bias_initializer : Union[callable, str], optional
The initializer for bias tensor.
kernel_regularizer : Union[callable, str], optional
The regularizer for kernel tensor.
bias_regularizer : Union[callable, str], optional
The regularizer for bias tensor.
"""
super(Dense, self).__init__(**kwargs)
self.input_dim = kwargs.get('input_dim', None)
self.units = int(units)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.input_spec = InputSpec(min_ndim=2)
self.kernel = None
self.bias = None
def __init__(
self,
rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
trainable=True,
name=None,
**kwargs,
):
"""Create a ``Conv`` Layer.
Parameters
----------
rank : int
The number of spatial axes.
filters : int, optional
The number of output filters.
kernel_size : Union[int, Sequence[int]]
The shape of convolution window.
strides : Union[int, Sequence[int]], optional, default=1
The stride of convolution window.
padding : Union[int, Sequence[int], str], optional
The padding algorithm or size.
data_format : str, optional, default='channels_last'
``'channels_first'`` or ``'channels_last'``.
dilation_rate : Union[int, Sequence[int]], optional, default=1
The rate of dilated convolution.
activation : Union[callable, str], optional
The optional activation function.
use_bias : bool, optional, default=True
Add a bias tensor to output or not.
kernel_initializer : Union[callable, str], optional
The initializer for kernel tensor.
bias_initializer : Union[callable, str], optional
The initializer for bias tensor.
kernel_regularizer : Union[callable, str], optional
The regularizer for kernel tensor.
bias_regularizer : Union[callable, str], optional
The regularizer for bias tensor.
"""
super(Conv, self).__init__(trainable=trainable, name=name, **kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank)
self.strides = conv_utils.normalize_tuple(strides, rank)
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)
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.input_spec = InputSpec(ndim=self.rank + 2)
self.conv_function = kwargs.get('conv_function', nn_ops.convolution)
self.kernel = None
self.bias = None
