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library(keras) | |
use_session_with_seed(1,disable_parallel_cpu = FALSE) | |
data = iris[sample(nrow(iris)),] | |
y = data[, "Species"] | |
x = data[,1:4] | |
# scale to [0,1] | |
x = as.matrix(apply(x, 2, function(x) (x-min(x))/(max(x) - min(x)))) | |
# one hot encode classes / create DummyFeatures | |
levels(y) = 1:length(y) | |
y = to_categorical(as.integer(y) - 1 , num_classes = 3) | |
# create sequential model | |
model = keras_model_sequential() | |
# add layers, first layer needs input dimension | |
model %>% | |
layer_dense(input_shape = ncol(x), units = 10, activation = "relu") %>% | |
layer_dense(units = 10, activation = "relu") %>% | |
layer_dense(units = 3, activation = "softmax") | |
# add a loss function and optimizer | |
model %>% | |
compile( | |
loss = "categorical_crossentropy", | |
optimizer = "adagrad", | |
metrics = "accuracy" | |
) | |
# fit model with our training data set, training will be done for 200 times data set | |
fit = model %>% | |
fit( | |
x = x, | |
y = y, | |
shuffle = T, | |
batch_size = 5, | |
validation_split = 0.3, | |
epochs = 200 | |
) | |
plot(fit) |
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