TensorFlow 1 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.
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.