6biSrSSKJr SSKJr SSKJr SSKJr SSKJr SSKJ r SSK J r \ "S S 5"S S \ 55r \ r g )zKeras 1D convolution layer.) activations) constraints) initializers) regularizers)utils)Conv) keras_exportzkeras.layers.Conv1Dzkeras.layers.Convolution1Dcj^\rSrSrSr\R SU4Sjj5rSrU=r $)Conv1Da@1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. If `use_bias` is True, a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. When using this layer as the first layer in a model, provide an `input_shape` argument (tuple of integers or `None`, e.g. `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors, or `(None, 128)` for variable-length sequences of 128-dimensional vectors. Examples: >>> # The inputs are 128-length vectors with 10 timesteps, and the >>> # batch size is 4. >>> input_shape = (4, 10, 128) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv1D( ... 32, 3, activation='relu',input_shape=input_shape[1:])(x) >>> print(y.shape) (4, 8, 32) >>> # With extended batch shape [4, 7] (e.g. weather data where batch >>> # dimensions correspond to spatial location and the third dimension >>> # corresponds to time.) >>> input_shape = (4, 7, 10, 128) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv1D( ... 32, 3, activation='relu', input_shape=input_shape[2:])(x) >>> print(y.shape) (4, 7, 8, 32) Args: filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"`, `"same"` or `"causal"` (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. `"causal"` results in causal (dilated) convolutions, e.g. `output[t]` does not depend on `input[t+1:]`. Useful when modeling temporal data where the model should not violate the temporal order. See [WaveNet: A Generative Model for Raw Audio, section 2.1](https://arxiv.org/abs/1609.03499). 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, width, channels)` while `channels_first` corresponds to inputs with shape `(batch_size, channels, width)`. Note that the `channels_first` format is currently not supported by TensorFlow on CPU. dilation_rate: an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. groups: A positive integer specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with `filters / groups` filters. The output is the concatenation of all the `groups` results along the channel axis. Input channels and `filters` must both be divisible by `groups`. 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. kernel_initializer: Initializer for the `kernel` weights matrix (see `keras.initializers`). Defaults to 'glorot_uniform'. bias_initializer: Initializer for the bias vector (see `keras.initializers`). Defaults to 'zeros'. kernel_regularizer: Regularizer function applied to the `kernel` weights 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`). kernel_constraint: Constraint function applied to the kernel matrix (see `keras.constraints`). bias_constraint: Constraint function applied to the bias vector (see `keras.constraints`). Input shape: 3+D tensor with shape: `batch_shape + (steps, input_dim)` Output shape: 3+D tensor with shape: `batch_shape + (new_steps, filters)` `steps` value might have changed due to padding or strides. Returns: A tensor of rank 3 representing `activation(conv1d(inputs, kernel) + bias)`. 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