1D Convolutional LSTM
layer_conv_lstm_1d(
object,
filters,
kernel_size,
strides = 1L,
padding = "valid",
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,
...
)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.
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
An integer or list of n integers, specifying the dimensions of the convolution window.
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.
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".
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 function to use. By default hyperbolic tangent
activation function is applied (tanh(x)).
Activation function to use for the recurrent step.
Boolean, whether the layer uses a bias vector.
Initializer for the kernel weights matrix, used for
the linear transformation of the inputs.
Initializer for the recurrent_kernel weights
matrix, used for the linear transformation of the recurrent state.
Initializer for the bias vector.
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
Regularizer function applied to the kernel weights
matrix.
Regularizer function applied to the
recurrent_kernel weights matrix.
Regularizer function applied to the bias vector.
Regularizer function applied to.
Constraint function applied to the kernel weights
matrix.
Constraint function applied to the recurrent_kernel
weights matrix.
Constraint function applied to the bias vector.
Boolean. Whether to return the last output in the output sequence, or the full sequence. (default FALSE)
Boolean Whether to return the last state in addition to the output. (default FALSE)
Boolean (default FALSE). If TRUE, process the input sequence backwards.
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.
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
standard layer arguments.
Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.