tf.keras.layers.DepthwiseConv2D | TensorFlow v2.0.0 (original) (raw)
tf.keras.layers.DepthwiseConv2D
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Depthwise separable 2D convolution.
Inherits From: Conv2D
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tf.compat.v1.keras.layers.DepthwiseConv2D
tf.keras.layers.DepthwiseConv2D(
kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1,
data_format=None, 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 consists in 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). |
| 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, height, width, channels) while channels_firstcorresponds to inputs with shape(batch, 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'. |
| activation | Activation function to use. If you don't specify anything, no activation is applied (ie. 'linear' activation: a(x) = x). |
| use_bias | Boolean, whether the layer uses a bias vector. |
| depthwise_initializer | Initializer for the depthwise kernel matrix. |
| bias_initializer | Initializer for the bias vector. |
| depthwise_regularizer | Regularizer function applied to the depthwise kernel matrix. |
| bias_regularizer | Regularizer function applied to the bias vector. |
| activity_regularizer | Regularizer function applied to the output of the layer (its 'activation'). |
| depthwise_constraint | Constraint function applied to the depthwise kernel matrix. |
| bias_constraint | Constraint function applied to the bias vector. |
Input shape:
4D tensor with shape:[batch, channels, rows, cols] if data_format='channels_first' or 4D tensor with shape:[batch, rows, cols, channels] if data_format='channels_last'.
Output shape:
4D tensor with shape:[batch, filters, new_rows, new_cols] if data_format='channels_first' or 4D tensor with shape:[batch, new_rows, new_cols, filters] if data_format='channels_last'.rows and cols values might have changed due to padding.