layer_cudnn_lstm
(Deprecated) Fast LSTM implementation backed by CuDNN.
Description
Can only be run on GPU, with the TensorFlow backend.
Usage
layer_cudnn_lstm(
object,
units,
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,
stateful = FALSE,
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. |
| units | Positive integer, dimensionality of the output space. |
| 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. Setting it to true will also force bias_initializer="zeros". This is recommended in Jozefowicz et al. |
| 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 the output of the layer (its “activation”).. |
| 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. |
| return_state | Boolean (default FALSE). Whether to return the last state in addition to the output. |
| 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. |
| 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
References
See Also
Other recurrent layers: layer_cudnn_gru(), layer_gru(), layer_lstm(), layer_rnn(), layer_simple_rnn()