layer_conv_1d
1D convolution layer (e.g. temporal convolution).
Description
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.
Usage
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
)
Arguments
Arguments | Description |
---|---|
object | What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input() ). The return value depends on object . If object is: - missing or NULL , the Layer instance is returned. - a Sequential model, the model with an additional layer is returned. - a Tensor, the output tensor from layer_instance(object) is returned. |
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 dilation_rate value != 1. |
padding | 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. |
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, length, channels) (default format for temporal data in Keras) while "channels_first" corresponds to inputs with shape (batch, channels, length) . |
dilation_rate | 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. |
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 (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. |
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_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. |
batch_size | Fixed batch size for layer |
dtype | The data type expected by the input, as a string (float32 , float64 , int32 …) |
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. |
Section
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.
See Also
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_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()