Conv1D
classkeras.layers.Conv1D(
filters,
kernel_size,
strides=1,
padding="valid",
data_format=None,
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
)
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.
Arguments
strides > 1
is incompatible with
dilation_rate > 1
."valid"
, "same"
or "causal"
(case-insensitive).
"valid"
means no padding. "same"
results in padding evenly to
the left/right or up/down of the input. When padding="same"
and
strides=1
, the output has the same size as the input.
"causal"
results in causal(dilated) convolutions, e.g. output[t]
does not depend oninput[t+1:]
. Useful when modeling temporal data
where the model should not violate the temporal order.
See WaveNet: A Generative Model for Raw Audio, section2.1."channels_last"
or "channels_first"
.
The ordering of the dimensions in the inputs. "channels_last"
corresponds to inputs with shape (batch, steps, features)
while "channels_first"
corresponds to inputs with shape
(batch, features, steps)
. It defaults to the image_data_format
value found in your Keras config file at ~/.keras/keras.json
.
If you never set it, then it will be "channels_last"
.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
.None
, no activation is applied.True
, bias will be added to the output.None
,
the default initializer ("glorot_uniform"
) will be used.None
, the
default initializer ("zeros"
) will be used.Optimizer
(e.g. used to implement
norm constraints or value constraints for layer weights). The
function must take as input the unprojected variable and must return
the projected variable (which must have the same shape). Constraints
are not safe to use when doing asynchronous distributed training.Optimizer
.Input shape
data_format="channels_last"
:
A 3D tensor with shape: (batch_shape, steps, channels)
data_format="channels_first"
:
A 3D tensor with shape: (batch_shape, channels, steps)
Output shape
data_format="channels_last"
:
A 3D tensor with shape: (batch_shape, new_steps, filters)
data_format="channels_first"
:
A 3D tensor with shape: (batch_shape, filters, new_steps)
Returns
A 3D tensor representing activation(conv1d(inputs, kernel) + bias)
.
Raises
strides > 1
and dilation_rate > 1
.Example
>>> # The inputs are 128-length vectors with 10 timesteps, and the
>>> # batch size is 4.
>>> x = np.random.rand(4, 10, 128)
>>> y = keras.layers.Conv1D(32, 3, activation='relu')(x)
>>> print(y.shape)
(4, 8, 32)