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 NULL, it is applied to the outputs
as well. When using this layer as the first layer in a model, provide an
input_shape argument (list of integers or NULL , e.g. (10, 128) for
sequences of 10 vectors of 128-dimensional vectors, or (NULL, 128) for
variable-length sequences of 128-dimensional vectors.
layer_conv_1d(
object,
filters,
kernel_size,
strides = 1L,
padding = "valid",
data_format = "channels_last",
dilation_rate = 1L,
groups = 1L,
activation = NULL,
use_bias = TRUE,
kernel_initializer = "glorot_uniform",
bias_initializer = "zeros",
kernel_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
bias_constraint = NULL,
input_shape = NULL,
batch_input_shape = NULL,
batch_size = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)What to call the new Layer instance with. Typically a keras
Model, another Layer, or a tf.Tensor/KerasTensor. If object is
missing, the Layer instance is returned, otherwise, layer(object) is
returned.
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
An integer or list of a single integer, specifying the length of the 1D convolution window.
An integer or 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.
One of "valid", "causal" or "same" (case-insensitive).
"valid" means "no padding".
"same" results in padding the input such that the output has the same
length as the original 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.
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, length, channels) (default format for
temporal data in Keras) while "channels_first" corresponds to inputs
with shape (batch, channels, length).
an integer or 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.
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 function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: a(x) = x).
Boolean, whether the layer uses a bias vector.
Initializer for the kernel weights matrix.
Initializer for the bias vector.
Regularizer function applied to the kernel
weights matrix.
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the kernel matrix.
Constraint function applied to the bias vector.
Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
Shapes, including the batch size. For instance,
batch_input_shape=c(10, 32) indicates that the expected input will be
batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32)
indicates batches of an arbitrary number of 32-dimensional vectors.
Fixed batch size for layer
The data type expected by the input, as a string (float32,
float64, int32...)
An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
Whether the layer weights will be updated during training.
Initial weights for layer.
3D tensor with shape: (batch_size, steps, input_dim)
3D tensor with shape: (batch_size, new_steps, filters) steps value might have changed due to padding or strides.
Other convolutional layers:
layer_conv_1d_transpose(),
layer_conv_2d_transpose(),
layer_conv_2d(),
layer_conv_3d_transpose(),
layer_conv_3d(),
layer_conv_lstm_2d(),
layer_cropping_1d(),
layer_cropping_2d(),
layer_cropping_3d(),
layer_depthwise_conv_2d(),
layer_separable_conv_1d(),
layer_separable_conv_2d(),
layer_upsampling_1d(),
layer_upsampling_2d(),
layer_upsampling_3d(),
layer_zero_padding_1d(),
layer_zero_padding_2d(),
layer_zero_padding_3d()