Applies Dropout to the input.
Applies Dropout to the input.
Dropout(rate, noise_shape = NULL, seed = NULL, input_shape = NULL)
rate |
float between 0 and 1. Fraction of the input units to drop. |
noise_shape |
1D integer tensor representing the shape of the the input. |
seed |
A Python integer to use as random seed. |
input_shape |
only need when first layer of a model; sets the input shape of the data |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other layers: Activation,
ActivityRegularization,
AdvancedActivation,
BatchNormalization, Conv,
Dense, Embedding,
Flatten, GaussianNoise,
LayerWrapper,
LocallyConnected, Masking,
MaxPooling, Permute,
RNN, RepeatVector,
Reshape, Sequential
if (keras_available()) {
X_train <- array(rnorm(100 * 28 * 28), dim = c(100, 28, 28, 1))
Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)
mod <- Sequential()
mod$add(Conv2D(filters = 2, kernel_size = c(2, 2),
input_shape = c(28, 28, 1)))
mod$add(Activation("relu"))
mod$add(MaxPooling2D(pool_size=c(2, 2)))
mod$add(LocallyConnected2D(filters = 2, kernel_size = c(2, 2)))
mod$add(Activation("relu"))
mod$add(MaxPooling2D(pool_size=c(2, 2)))
mod$add(Dropout(0.25))
mod$add(Flatten())
mod$add(Dropout(0.5))
mod$add(Dense(3, activation='softmax'))
keras_compile(mod, loss='categorical_crossentropy', optimizer=RMSprop())
keras_fit(mod, X_train, Y_train, verbose = 0)
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