Flatten(input_shape = NULL)

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)
}

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)
}