Sequential: Initialize sequential model
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Author(s) References See Also Examples

Use this function to construct an empty model to which layers will be added, or pass a list of layers directly to the function. The first layer passed to a Sequential model should have a defined input shape.

1	Sequential(...)

...

keras model layers to construct the model from

Taylor B. Arnold, taylor.arnold@acm.org

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

Other model functions: LoadSave, Predict, keras_compile, keras_fit

Other layers: Activation, ActivityRegularization, AdvancedActivation, BatchNormalization, Conv, Dense, Dropout, Embedding, Flatten, GaussianNoise, LayerWrapper, LocallyConnected, Masking, MaxPooling, Permute, RNN, RepeatVector, Reshape

if(keras_available()) {
  X_train <- matrix(rnorm(100 * 10), nrow = 100)
  Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)

  mod <- Sequential()
  mod$add(Dense(units = 50, input_shape = dim(X_train)[2]))
  mod$add(  Dropout(rate = 0.5))
  mod$add(Activation("relu"))
  mod$add(Dense(units = 3))
  mod$add(ActivityRegularization(l1 = 1))
  mod$add(Activation("softmax"))
  keras_compile(mod,  loss = 'categorical_crossentropy', optimizer = RMSprop())

  keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
            verbose = 0, validation_split = 0.2)
  
  # You can also add layers directly as arguments to Sequential()

  mod <- Sequential(
    Dense(units = 50, input_shape = ncol(X_train)),
    Dropout(rate = 0.5),
    Activation("relu"),
    Dense(units = 3),
    ActivityRegularization(l1 = 1),
    Activation("softmax")
  )
  keras_compile(mod,  loss = 'categorical_crossentropy', optimizer = RMSprop())
  
  keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
            verbose = 0, validation_split = 0.2)
  
}