tf.keras.layers.DepthwiseConv2D     View source on GitHub    Depthwise separable 2D convolution. Inherits From: Conv2D, Layer, Module  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.layers.DepthwiseConv2D  
tf.keras.layers.DepthwiseConv2D(
    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
)
 Depthwise Separable convolutions consist of performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step.
 


 Arguments
  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 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).  
  bias_initializer   Initializer for the bias vector ( see keras.initializers).  
  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, 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(depthwiseconv2d(inputs, kernel) + bias).  

 


 Raises
  ValueError   if padding is "causal".  
  ValueError   when both strides > 1 and dilation_rate > 1.