Recurrent neural network layers
Recurrent neural network layers
SimpleRNN(units, activation = "tanh", use_bias = TRUE, kernel_initializer = "glorot_uniform", recurrent_initializer = "orthogonal", bias_initializer = "zeros", kernel_regularizer = NULL, recurrent_regularizer = NULL, bias_regularizer = NULL, activity_regularizer = NULL, kernel_constraint = NULL, recurrent_constraint = NULL, bias_constraint = NULL, dropout = 0, recurrent_dropout = 0, input_shape = NULL) GRU(units, activation = "tanh", recurrent_activation = "hard_sigmoid", use_bias = TRUE, kernel_initializer = "glorot_uniform", recurrent_initializer = "orthogonal", bias_initializer = "zeros", kernel_regularizer = NULL, recurrent_regularizer = NULL, bias_regularizer = NULL, activity_regularizer = NULL, kernel_constraint = NULL, recurrent_constraint = NULL, bias_constraint = NULL, dropout = 0, recurrent_dropout = 0, input_shape = NULL) LSTM(units, 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, dropout = 0, recurrent_dropout = 0, return_sequences = FALSE, input_shape = NULL)
units |
Positive integer, dimensionality of the output space. |
activation |
Activation function to use |
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 recurrentstate. |
bias_initializer |
Initializer for the bias vector |
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 |
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. |
input_shape |
only need when first layer of a model; sets the input shape of the data |
recurrent_activation |
Activation function to use for the recurrent step |
unit_forget_bias |
Boolean. If True, add 1 to the bias of the forget gate at initialization. |
return_sequences |
Boolean. Whether to return the last output in the output sequence, or the full sequence. |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
if(keras_available()) {
X_train <- matrix(sample(0:19, 100 * 100, TRUE), ncol = 100)
Y_train <- rnorm(100)
mod <- Sequential()
mod$add(Embedding(input_dim = 20, output_dim = 10,
input_length = 100))
mod$add(Dropout(0.5))
mod$add(LSTM(16))
mod$add(Dense(1))
mod$add(Activation("sigmoid"))
keras_compile(mod, loss = "mse", optimizer = RMSprop())
keras_fit(mod, X_train, Y_train, epochs = 3, verbose = 0)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.