View source: R/layersconvolutional.R
layer_conv_1d  R Documentation 
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 128dimensional vectors, or (NULL, 128)
for
variablelength sequences of 128dimensional 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
)
object 
What to compose the new

filters 
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). 
kernel_size 
An integer or list of a single integer, specifying the length of the 1D convolution window. 
strides 
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 
padding 
One of 
data_format 
A string, one of 
dilation_rate 
an integer or list of a single integer, specifying the
dilation rate to use for dilated convolution. Currently, specifying any

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 
activation 
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: 
use_bias 
Boolean, whether the layer uses a bias vector. 
kernel_initializer 
Initializer for the 
bias_initializer 
Initializer for the bias vector. 
kernel_regularizer 
Regularizer function applied to the 
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. 
input_shape 
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. 
batch_input_shape 
Shapes, including the batch size. For instance,

batch_size 
Fixed batch size for layer 
dtype 
The data type expected by the input, as a string ( 
name 
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. 
trainable 
Whether the layer weights will be updated during training. 
weights 
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()
,
layer_conv_2d_transpose()
,
layer_conv_3d()
,
layer_conv_3d_transpose()
,
layer_conv_lstm_2d()
,
layer_cropping_1d()
,
layer_cropping_2d()
,
layer_cropping_3d()
,
layer_depthwise_conv_1d()
,
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()
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