Python tensorflow.python.layers.base.InputSpec() Examples

def __init__(self, units,
               activation=None,
               use_bias=True,
               kernel_initializer=None,
               bias_initializer=init_ops.zeros_initializer(),
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               trainable=True,
               name=None,
               **kwargs):
    super(Dense, self).__init__(trainable=trainable, name=name, **kwargs)
    self.units = units
    self.activation = activation
    self.use_bias = use_bias
    self.kernel_initializer = kernel_initializer
    self.bias_initializer = bias_initializer
    self.kernel_regularizer = kernel_regularizer
    self.bias_regularizer = bias_regularizer
    self.activity_regularizer = activity_regularizer
    self.input_spec = base.InputSpec(min_ndim=2) 

def __init__(self,
               inverse=False,
               rectify=False,
               gamma_init=.1,
               data_format="channels_last",
               beta_parameterizer=_default_beta_param,
               gamma_parameterizer=_default_gamma_param,
               activity_regularizer=None,
               trainable=True,
               name=None,
               **kwargs):
    super(GDN, self).__init__(trainable=trainable, name=name,
                              activity_regularizer=activity_regularizer,
                              **kwargs)
    self.inverse = bool(inverse)
    self.rectify = bool(rectify)
    self._gamma_init = float(gamma_init)
    self.data_format = data_format
    self._beta_parameterizer = beta_parameterizer
    self._gamma_parameterizer = gamma_parameterizer
    self._channel_axis()  # trigger ValueError early
    self.input_spec = base.InputSpec(min_ndim=2) 

def build(self, input_shape):
    channel_axis = self._channel_axis()
    input_shape = tensor_shape.TensorShape(input_shape)
    num_channels = input_shape[channel_axis].value
    if num_channels is None:
      raise ValueError("The channel dimension of the inputs to `GDN` "
                       "must be defined.")
    self._input_rank = input_shape.ndims
    self.input_spec = base.InputSpec(ndim=input_shape.ndims,
                                     axes={channel_axis: num_channels})

    self.beta = self._beta_parameterizer(
        name="beta", shape=[num_channels], dtype=self.dtype,
        getter=self.add_variable, initializer=init_ops.Ones())

    self.gamma = self._gamma_parameterizer(
        name="gamma", shape=[num_channels, num_channels], dtype=self.dtype,
        getter=self.add_variable,
        initializer=init_ops.Identity(gain=self._gamma_init))

    self.built = True 

def __init__(self,
                 num_units,
                 recurrent_min_abs=0,
                 recurrent_max_abs=None,
                 recurrent_kernel_initializer=None,
                 input_kernel_initializer=None,
                 activation=None,
                 reuse=None,
                 name=None):
        super(IndRNNCell, self).__init__(_reuse=reuse, name=name)

        self.input_spec = base_layer.InputSpec(ndim=2)

        # initialization
        self._num_units = num_units
        self._recurrent_min_abs = recurrent_min_abs

        self._recurrent_max_abs = recurrent_max_abs
        self._recurrent_recurrent_kernel_initializer = recurrent_kernel_initializer
        self._input_kernel_initializer = input_kernel_initializer
        self._activation = activation or nn_ops.relu 

def build(self, input_shape):
        input_shape = tensor_shape.TensorShape(input_shape)
        if input_shape[-1].value is None:
            raise ValueError('The last dimension of the inputs to `Dense` '
                             'should be defined. Found `None`.')
        self.input_spec = base.InputSpec(min_ndim=2,
                                         axes={-1: input_shape[-1].value})

        self.kernel = self._build_kernel(input_shape)

        if self.use_bias:
            self.bias = self.add_variable('bias',
                                          shape=[self.units],
                                          initializer=self.bias_initializer,
                                          regularizer=self.bias_regularizer,
                                          constraint=self.bias_constraint,
                                          dtype=self.dtype,
                                          trainable=True)
        else:
            self.bias = None
        self.built = True 

def build(self, input_shape):
    input_shape = tensor_shape.TensorShape(input_shape)
    if input_shape[-1].value is None:
      raise ValueError('The last dimension of the inputs to `Dense` '
                       'should be defined. Found `None`.')
    self.input_spec = base.InputSpec(min_ndim=2,
                                     axes={-1: input_shape[-1].value})
    self.kernel = self.add_variable('kernel',
                                    shape=[input_shape[-1].value, self.units],
                                    initializer=self.kernel_initializer,
                                    regularizer=self.kernel_regularizer,
                                    constraint=self.kernel_constraint,
                                    dtype=self.dtype,
                                    trainable=True)
    if self.use_bias:
      self.bias = self.add_variable('bias',
                                    shape=[self.units,],
                                    initializer=self.bias_initializer,
                                    regularizer=self.bias_regularizer,
                                    constraint=self.bias_constraint,
                                    dtype=self.dtype,
                                    trainable=True)
    else:
      self.bias = None
    self.built = True 

def __init__(self,
               num_units,
               recurrent_min_abs=0,
               recurrent_max_abs=None,
               recurrent_kernel_initializer=None,
               input_kernel_initializer=None,
               activation=None,
               reuse=None,
               name=None):
    super(IndRNNCell, self).__init__(_reuse=reuse, name=name)

    # Inputs must be 2-dimensional.
    self.input_spec = base_layer.InputSpec(ndim=2)

    self._num_units = num_units
    self._recurrent_min_abs = recurrent_min_abs
    self._recurrent_max_abs = recurrent_max_abs
    self._recurrent_initializer = recurrent_kernel_initializer
    self._input_initializer = input_kernel_initializer
    self._activation = activation or nn_ops.relu 

def build(self, input_shape):
    input_shape = tensor_shape.TensorShape(input_shape)
    if input_shape[-1].value is None:
      raise ValueError('The last dimension of the inputs to `Dense` '
                       'should be defined. Found `None`.')
    self.input_spec = base.InputSpec(min_ndim=2,
                                     axes={-1: input_shape[-1].value})
    self.kernel = self.add_variable('kernel',
                                    shape=[input_shape[-1].value, self.units],
                                    initializer=self.kernel_initializer,
                                    regularizer=self.kernel_regularizer,
                                    dtype=self.dtype,
                                    trainable=True)
    if self.use_bias:
      self.bias = self.add_variable('bias',
                                    shape=[self.units,],
                                    initializer=self.bias_initializer,
                                    regularizer=self.bias_regularizer,
                                    dtype=self.dtype,
                                    trainable=True)
    else:
      self.bias = None
    self.built = True 

def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling2D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 2, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 2, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=4) 

def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling1D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 1, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 1, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=3) 

def __init__(self, **kwargs):
    super(Flatten, self).__init__(**kwargs)
    self.input_spec = base.InputSpec(min_ndim=2) 

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=None,
               bias_initializer=init_ops.zeros_initializer(),
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               trainable=True,
               name=None,
               **kwargs):
    super(_Conv, self).__init__(trainable=trainable, name=name,
                                activity_regularizer=activity_regularizer,
                                **kwargs)
    self.rank = rank
    self.filters = filters
    self.kernel_size = utils.normalize_tuple(kernel_size, rank, 'kernel_size')
    self.strides = utils.normalize_tuple(strides, rank, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.dilation_rate = utils.normalize_tuple(
        dilation_rate, rank, 'dilation_rate')
    self.activation = activation
    self.use_bias = use_bias
    self.kernel_initializer = kernel_initializer
    self.bias_initializer = bias_initializer
    self.kernel_regularizer = kernel_regularizer
    self.bias_regularizer = bias_regularizer
    self.kernel_constraint = kernel_constraint
    self.bias_constraint = bias_constraint
    self.input_spec = base.InputSpec(ndim=self.rank + 2) 

def build(self, input_shape):
    input_shape = tensor_shape.TensorShape(input_shape)
    if self.data_format == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = -1
    if input_shape[channel_axis].value is None:
      raise ValueError('The channel dimension of the inputs '
                       'should be defined. Found `None`.')
    input_dim = input_shape[channel_axis].value
    kernel_shape = self.kernel_size + (input_dim, self.filters)

    self.kernel = self.add_variable(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_variable(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.input_spec = base.InputSpec(ndim=self.rank + 2,
                                     axes={channel_axis: input_dim})
    self._convolution_op = nn_ops.Convolution(
        input_shape,
        filter_shape=self.kernel.get_shape(),
        dilation_rate=self.dilation_rate,
        strides=self.strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format,
                                              self.rank + 2))
    self.built = True 

def __init__(self, units,
               activation=None,
               use_bias=True,
               kernel_initializer=None,
               bias_initializer=init_ops.zeros_initializer(),
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               trainable=True,
               name=None,
               **kwargs):
    super(Dense, self).__init__(trainable=trainable, name=name,
                                activity_regularizer=activity_regularizer,
                                **kwargs)
    self.units = units
    self.activation = activation
    self.use_bias = use_bias
    self.kernel_initializer = kernel_initializer
    self.bias_initializer = bias_initializer
    self.kernel_regularizer = kernel_regularizer
    self.bias_regularizer = bias_regularizer
    self.kernel_constraint = kernel_constraint
    self.bias_constraint = bias_constraint
    self.input_spec = base.InputSpec(min_ndim=2) 

def __init__(self, filters,
               kernel_size,
               strides=(1, 1),
               padding='valid',
               data_format='channels_last',
               activation=None,
               use_bias=True,
               kernel_initializer=None,
               bias_initializer=init_ops.zeros_initializer(),
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               trainable=True,
               name=None,
               **kwargs):
    super(Conv2DTranspose, self).__init__(
        filters,
        kernel_size,
        strides=strides,
        padding=padding,
        data_format=data_format,
        activation=activation,
        use_bias=use_bias,
        kernel_initializer=kernel_initializer,
        bias_initializer=bias_initializer,
        kernel_regularizer=kernel_regularizer,
        bias_regularizer=bias_regularizer,
        activity_regularizer=activity_regularizer,
        kernel_constraint=kernel_constraint,
        bias_constraint=bias_constraint,
        trainable=trainable,
        name=name,
        **kwargs)
    self.input_spec = base.InputSpec(ndim=4) 

def __init__(self, size=(2, 2), data_format=None, **kwargs):
    super(BilinearUpSampling2D, self).__init__(**kwargs)
    self.data_format = conv_utils.normalize_data_format(data_format)
    self.size = conv_utils.normalize_tuple(size, 2, 'size')
    self.input_spec = InputSpec(ndim=4) 

def build(self, input_shape):
    if len(input_shape) != 4:
      raise ValueError('Inputs should have rank ' +
                       str(4) +
                       'Received input shape:', str(input_shape))
    if self.data_format == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = -1
    if input_shape[channel_axis] is None:
      raise ValueError('The channel dimension of the inputs '
                       'should be defined. Found `None`.')
    input_dim = input_shape[channel_axis]
    self.input_spec = base.InputSpec(ndim=4, axes={channel_axis: input_dim})
    kernel_shape = self.kernel_size + (self.filters, input_dim)

    self.kernel = self.add_variable(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_variable(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 build(self, input_shape):
    input_shape = tensor_shape.TensorShape(input_shape)
    channel_axis = self._channel_axis
    input_channels = input_shape[channel_axis].value
    if input_channels is None:
      raise ValueError("The channel dimension of the inputs must be defined.")
    kernel_shape = self.kernel_support + (input_channels, self.filters)
    if self.channel_separable:
      output_channels = self.filters * input_channels
    else:
      output_channels = self.filters

    if self.kernel_parameterizer is None:
      getter = self.add_variable
    else:
      getter = functools.partial(
          self.kernel_parameterizer, getter=self.add_variable)
    self._kernel = getter(
        name="kernel", shape=kernel_shape, dtype=self.dtype,
        initializer=self.kernel_initializer,
        regularizer=self.kernel_regularizer)

    if self.bias_parameterizer is None:
      getter = self.add_variable
    else:
      getter = functools.partial(
          self.bias_parameterizer, getter=self.add_variable)
    self._bias = None if not self.use_bias else getter(
        name="bias", shape=(output_channels,), dtype=self.dtype,
        initializer=self.bias_initializer, regularizer=self.bias_regularizer)

    self.input_spec = base.InputSpec(
        ndim=self._rank + 2, axes={channel_axis: input_channels})

    super(_SignalConv, self).build(input_shape) 

def build(self, input_shape):
        input_shape = tensor_shape.TensorShape(input_shape)
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis].value is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = input_shape[channel_axis].value
        kernel_shape = self.kernel_size + (input_dim, self.filters)

        # dense kernel
        self.kernel_pre = self.add_variable(name='kernel_pre',
                                            shape=kernel_shape,
                                            initializer=self.kernel_initializer,
                                            regularizer=self.kernel_regularizer,
                                            trainable=True,
                                            dtype=self.dtype)
        conv_th = tf.ones_like(self.kernel_pre) * self.sparse_th
        conv_zero = tf.zeros_like(self.kernel_pre)
        cond = tf.less(tf.abs(self.kernel_pre), conv_th)
        self.kernel = tf.where(cond, conv_zero, self.kernel_pre, name='kernel')

        if self.use_bias:
            self.bias = self.add_variable(name='bias',
                                          shape=(self.filters,),
                                          initializer=self.bias_initializer,
                                          regularizer=self.bias_regularizer,
                                          trainable=True,
                                          dtype=self.dtype)
        else:
            self.bias = None
        self.input_spec = base.InputSpec(ndim=self.rank + 2,
                                         axes={channel_axis: input_dim})
        self.built = True 

def __init__(self,
               num_units,
               forget_bias=1.0,
               cell_clip=None,
               use_peephole=False,
               reuse=None,
               dtype=None,
               name="lstm_cell"):
    """Initialize the LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      cell_clip: clip the cell to this value. Default is no cell clipping.
      use_peephole: Whether to use peephole connections or not.
      reuse: (optional) boolean describing whether to reuse variables in an
        existing scope.  If not `True`, and the existing scope already has the
        given variables, an error is raised.
      dtype: the dtype of variables of this layer.
      name: String, the name of the layer. Layers with the same name will
        share weights, but to avoid mistakes we require reuse=True in such
        cases.  By default this is "lstm_cell", for variable-name compatibility
        with `tf.nn.rnn_cell.LSTMCell`.
    """
    super(LSTMBlockFusedCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype)
    self._num_units = num_units
    self._forget_bias = forget_bias
    self._cell_clip = cell_clip if cell_clip is not None else -1
    self._use_peephole = use_peephole

    # Inputs must be 3-dimensional.
    self.input_spec = base_layer.InputSpec(ndim=3) 

def __init__(self,
               num_units,
               forget_bias=1.0,
               cell_clip=None,
               use_peephole=False,
               reuse=None,
               dtype=None,
               name="lstm_cell"):
    """Initialize the LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      cell_clip: clip the cell to this value. Default is no cell clipping.
      use_peephole: Whether to use peephole connections or not.
      reuse: (optional) boolean describing whether to reuse variables in an
        existing scope.  If not `True`, and the existing scope already has the
        given variables, an error is raised.
      dtype: the dtype of variables of this layer.
      name: String, the name of the layer. Layers with the same name will
        share weights, but to avoid mistakes we require reuse=True in such
        cases.  By default this is "lstm_cell", for variable-name compatibility
        with `tf.nn.rnn_cell.LSTMCell`.
    """
    super(LSTMBlockFusedCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype)
    self._num_units = num_units
    self._forget_bias = forget_bias
    self._cell_clip = cell_clip if cell_clip is not None else -1
    self._use_peephole = use_peephole

    # Inputs must be 3-dimensional.
    self.input_spec = base_layer.InputSpec(ndim=3) 

def __init__(self,
               num_units,
               forget_bias=1.0,
               cell_clip=None,
               use_peephole=False,
               reuse=None,
               dtype=None,
               name="lstm_cell"):
    """Initialize the LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      cell_clip: clip the cell to this value. Default is no cell clipping.
      use_peephole: Whether to use peephole connections or not.
      reuse: (optional) boolean describing whether to reuse variables in an
        existing scope.  If not `True`, and the existing scope already has the
        given variables, an error is raised.
      dtype: the dtype of variables of this layer.
      name: String, the name of the layer. Layers with the same name will
        share weights, but to avoid mistakes we require reuse=True in such
        cases.  By default this is "lstm_cell", for variable-name compatibility
        with `tf.nn.rnn_cell.LSTMCell`.
    """
    super(LSTMBlockFusedCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype)
    self._num_units = num_units
    self._forget_bias = forget_bias
    self._cell_clip = cell_clip if cell_clip is not None else -1
    self._use_peephole = use_peephole

    # Inputs must be 3-dimensional.
    self.input_spec = base_layer.InputSpec(ndim=3) 

def build(self, input_shape):
    if len(input_shape) != 4:
      raise ValueError('Inputs should have rank ' +
                       str(4) +
                       'Received input shape:', str(input_shape))
    if self.data_format == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = -1
    if input_shape[channel_axis] is None:
      raise ValueError('The channel dimension of the inputs '
                       'should be defined. Found `None`.')
    input_dim = input_shape[channel_axis]
    self.input_spec = base.InputSpec(ndim=4, axes={channel_axis: input_dim})
    kernel_shape = self.kernel_size + (self.filters, input_dim)

    self.kernel = self.add_variable(name='kernel',
                                    shape=kernel_shape,
                                    initializer=self.kernel_initializer,
                                    regularizer=self.kernel_regularizer,
                                    trainable=True,
                                    dtype=self.dtype)
    if self.use_bias:
      self.bias = self.add_variable(name='bias',
                                    shape=(self.filters,),
                                    initializer=self.bias_initializer,
                                    regularizer=self.bias_regularizer,
                                    trainable=True,
                                    dtype=self.dtype)
    else:
      self.bias = None
    self.built = True 

def __init__(self, filters,
               kernel_size,
               strides=(1, 1),
               padding='valid',
               data_format='channels_last',
               activation=None,
               use_bias=True,
               kernel_initializer=None,
               bias_initializer=init_ops.zeros_initializer(),
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               trainable=True,
               name=None,
               **kwargs):
    super(Conv2DTranspose, self).__init__(
        filters,
        kernel_size,
        strides=strides,
        padding=padding,
        data_format=data_format,
        activation=activation,
        use_bias=use_bias,
        kernel_initializer=kernel_initializer,
        bias_initializer=bias_initializer,
        kernel_regularizer=kernel_regularizer,
        bias_regularizer=bias_regularizer,
        activity_regularizer=activity_regularizer,
        trainable=trainable,
        name=name,
        **kwargs)
    self.input_spec = base.InputSpec(ndim=4) 

def build(self, input_shape):
    input_shape = tensor_shape.TensorShape(input_shape)
    if self.data_format == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = -1
    if input_shape[channel_axis].value is None:
      raise ValueError('The channel dimension of the inputs '
                       'should be defined. Found `None`.')
    input_dim = input_shape[channel_axis].value
    kernel_shape = self.kernel_size + (input_dim, self.filters)

    self.kernel = self.add_variable(name='kernel',
                                    shape=kernel_shape,
                                    initializer=self.kernel_initializer,
                                    regularizer=self.kernel_regularizer,
                                    trainable=True,
                                    dtype=self.dtype)
    if self.use_bias:
      self.bias = self.add_variable(name='bias',
                                    shape=(self.filters,),
                                    initializer=self.bias_initializer,
                                    regularizer=self.bias_regularizer,
                                    trainable=True,
                                    dtype=self.dtype)
    else:
      self.bias = None
    self.input_spec = base.InputSpec(ndim=self.rank + 2,
                                     axes={channel_axis: input_dim})
    self.built = True 

def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling3D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 3, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 3, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=5) 

def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling2D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 2, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 2, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=4) 

def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling1D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 1, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 1, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=3) 

def __init__(self, rank, filters, kernel_support,
               corr=False, strides_down=1, strides_up=1, padding="valid",
               extra_pad_end=True, channel_separable=False,
               data_format="channels_last",
               activation=None, use_bias=False,
               kernel_initializer=init_ops.VarianceScaling(),
               bias_initializer=init_ops.Zeros(),
               kernel_regularizer=None, bias_regularizer=None,
               kernel_parameterizer=parameterizers.RDFTParameterizer(),
               bias_parameterizer=None,
               **kwargs):
    super(_SignalConv, self).__init__(**kwargs)
    self._rank = int(rank)
    self._filters = int(filters)
    self._kernel_support = utils.normalize_tuple(
        kernel_support, self._rank, "kernel_support")
    self._corr = bool(corr)
    self._strides_down = utils.normalize_tuple(
        strides_down, self._rank, "strides_down")
    self._strides_up = utils.normalize_tuple(
        strides_up, self._rank, "strides_up")
    self._padding = str(padding).lower()
    try:
      self._pad_mode = {
          "valid": None,
          "same_zeros": "CONSTANT",
          "same_reflect": "REFLECT",
      }[self.padding]
    except KeyError:
      raise ValueError("Unsupported padding mode: '{}'".format(padding))
    self._extra_pad_end = bool(extra_pad_end)
    self._channel_separable = bool(channel_separable)
    self._data_format = utils.normalize_data_format(data_format)
    self._activation = activation
    self._use_bias = bool(use_bias)
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    self._kernel_parameterizer = kernel_parameterizer
    self._bias_parameterizer = bias_parameterizer
    self.input_spec = base.InputSpec(ndim=self._rank + 2) 

def build(self, input_shape):
    if len(input_shape) < 4:
      raise ValueError('Inputs to `SeparableConv2D` should have rank 4. '
                       'Received input shape:', str(input_shape))
    if self.data_format == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = 3
    if input_shape[channel_axis] is None:
      raise ValueError('The channel dimension of the inputs to '
                       '`SeparableConv2D` '
                       'should be defined. Found `None`.')
    input_dim = int(input_shape[channel_axis])
    self.input_spec = base.InputSpec(ndim=4, axes={channel_axis: input_dim})
    depthwise_kernel_shape = (self.kernel_size[0],
                              self.kernel_size[1],
                              input_dim,
                              self.depth_multiplier)
    pointwise_kernel_shape = (1, 1,
                              self.depth_multiplier * input_dim,
                              self.filters)

    self.depthwise_kernel = self.add_variable(
        name='depthwise_kernel',
        shape=depthwise_kernel_shape,
        initializer=self.depthwise_initializer,
        regularizer=self.depthwise_regularizer,
        trainable=True,
        dtype=self.dtype)
    self.pointwise_kernel = self.add_variable(
        name='pointwise_kernel',
        shape=pointwise_kernel_shape,
        initializer=self.pointwise_initializer,
        regularizer=self.pointwise_regularizer,
        trainable=True,
        dtype=self.dtype)
    if self.use_bias:
      self.bias = self.add_variable(name='bias',
                                    shape=(self.filters,),
                                    initializer=self.bias_initializer,
                                    regularizer=self.bias_regularizer,
                                    trainable=True,
                                    dtype=self.dtype)
    else:
      self.bias = None
    self.built = True