1D Convolutional LSTM

layer_conv_lstm_1d(
object,
filters,
kernel_size,
strides = 1L,
data_format = NULL,
dilation_rate = 1L,
activation = "tanh",
recurrent_activation = "hard_sigmoid",
use_bias = TRUE,
kernel_initializer = "glorot_uniform",
recurrent_initializer = "orthogonal",
bias_initializer = "zeros",
unit_forget_bias = TRUE,
kernel_regularizer = NULL,
recurrent_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
recurrent_constraint = NULL,
bias_constraint = NULL,
return_sequences = FALSE,
return_state = FALSE,
go_backwards = FALSE,
stateful = FALSE,
dropout = 0,
recurrent_dropout = 0,
...
)

## Arguments

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 n integers, specifying the dimensions of the convolution window.

strides

An integer or list of n integers, specifying the strides of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.

One of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.

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, time, ..., channels) while channels_first corresponds to inputs with shape (batch, time, channels, ...). 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".

dilation_rate

An integer or list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1.

activation

Activation function to use. By default hyperbolic tangent activation function is applied (tanh(x)).

recurrent_activation

Activation function to use for the recurrent step.

use_bias

Boolean, whether the layer uses a bias vector.

kernel_initializer

Initializer for the kernel weights matrix, used for the linear transformation of the inputs.

recurrent_initializer

Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.

bias_initializer

Initializer for the bias vector.

unit_forget_bias

Boolean. If TRUE, add 1 to the bias of the forget gate at initialization. Use in combination with bias_initializer="zeros". This is recommended in Jozefowicz et al., 2015

kernel_regularizer

Regularizer function applied to the kernel weights matrix.

recurrent_regularizer

Regularizer function applied to the recurrent_kernel weights matrix.

bias_regularizer

Regularizer function applied to the bias vector.

activity_regularizer

Regularizer function applied to.

kernel_constraint

Constraint function applied to the kernel weights matrix.

recurrent_constraint

Constraint function applied to the recurrent_kernel weights matrix.

bias_constraint

Constraint function applied to the bias vector.

return_sequences

Boolean. Whether to return the last output in the output sequence, or the full sequence. (default FALSE)

return_state

Boolean Whether to return the last state in addition to the output. (default FALSE)

go_backwards

Boolean (default FALSE). If TRUE, process the input sequence backwards.

stateful

Boolean (default FALSE). If TRUE, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.

dropout

Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.

recurrent_dropout

Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.

...

standard layer arguments.

## Details

Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.