Method build

tensorflow/python/keras/layers/convolutional.py:1301–1345

(self, input_shape)

    self.pointwise_constraint = constraints.get(pointwise_constraint)

  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.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