tf.keras.layers.Conv1D

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Class Conv1D

1D convolution layer (e.g. temporal convolution).

Aliases:

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.

Arguments:

  • 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", "causal" or "same" (case-insensitive). "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.
  • data_format: A string, one of channels_last (default) or channels_first.
  • 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.
  • 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.
  • kernel_initializer: Initializer for the kernel weights matrix.
  • bias_initializer: Initializer for the bias vector.
  • kernel_regularizer: Regularizer function applied to the kernel weights matrix.
  • bias_regularizer: Regularizer function applied to the bias vector.
  • activity_regularizer: Regularizer function applied to the output of the layer (its "activation")..
  • kernel_constraint: Constraint function applied to the kernel matrix.
  • bias_constraint: Constraint function applied to the bias vector.

Examples:

# Small convolutional model for 128-length vectors with 6 timesteps
# model.input_shape == (None, 6, 128)

model = Sequential()
model.add(Conv1D(32, 3, 
          activation='relu', 
          input_shape=(6, 128)))

# now: model.output_shape == (None, 4, 32)

Input shape:

3D tensor with shape: (batch_size, steps, input_dim)

Output shape:

3D tensor with shape: (batch_size, new_steps, filters) steps value might have changed due to padding or strides.

__init__

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__init__(
    filters,
    kernel_size,
    strides=1,
    padding='valid',
    data_format='channels_last',
    dilation_rate=1,
    activation=None,
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros',
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs
)