tf.keras.layers.Conv1D

TensorFlow 2 version View source on GitHub

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

tf.keras.layers.Conv1D(
    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
)

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.

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.