1D convolution layer (e.g. temporal convolution).
Inherits From: Layer
, Module
tf.keras.layers.Conv1D(
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
groups=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.
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.
|
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 ).
|
|
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) .
|
Raises |
ValueError
|
when both strides > 1 and dilation_rate > 1 .
|
Methods
convolution_op
View source
convolution_op(
inputs, kernel
)