6bi"vSrSSKJs Jr SSKJr SSKJr SSKJ r SSK J r \ "S5"SS \55r g) zKeras depthwise 1D convolution.N) DepthwiseConv) conv_utils)tf_utils) keras_exportzkeras.layers.DepthwiseConv1Dcv^\rSrSrSrSU4SjjrSr\RS5r Sr U=r $)DepthwiseConv1DaDepthwise 1D convolution. Depthwise convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution. It is implemented via the following steps: - Split the input into individual channels. - Convolve each channel with an individual depthwise kernel with `depth_multiplier` output channels. - Concatenate the convolved outputs along the channels axis. Unlike a regular 1D convolution, depthwise convolution does not mix information across different input channels. The `depth_multiplier` argument determines how many filter are applied to one input channel. As such, it controls the amount of output channels that are generated per input channel in the depthwise step. Args: kernel_size: An integer, specifying the height and width of the 1D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer, 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 with zeros 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)`. When unspecified, uses `image_data_format` value found in your TF-Keras config file at `~/.keras/keras.json` (if exists) else 'channels_last'. Defaults to 'channels_last'. dilation_rate: A single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride 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`). If None, the default initializer ('glorot_uniform') will be used. bias_initializer: Initializer for the bias vector (see `keras.initializers`). If None, the default initializer ('zeros') will be used. 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: 3D tensor with shape: `[batch_size, channels, input_dim]` if data_format='channels_first' or 3D tensor with shape: `[batch_size, input_dim, channels]` if data_format='channels_last'. Output shape: 3D tensor with shape: `[batch_size, channels * depth_multiplier, new_dims]` if `data_format='channels_first'` or 3D tensor with shape: `[batch_size, new_dims, channels * depth_multiplier]` if `data_format='channels_last'`. `new_dims` values might have changed due to padding. Returns: A tensor of rank 3 representing `activation(depthwiseconv1d(inputs, kernel) + bias)`. Raises: ValueError: if `padding` is "causal". 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