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37 lines
1 KiB
R
37 lines
1 KiB
R
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## Tensorflow:
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install.packages('keras', repos='http://cran.us.r-project.org')
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library(keras)
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mnist <- dataset_mnist()
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x_train <- mnist$train$x
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y_train <- mnist$train$y
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x_test <- mnist$test$x
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y_test <- mnist$test$y
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# reshape
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x_train <- array_reshape(x_train, c(nrow(x_train), 784))
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x_test <- array_reshape(x_test, c(nrow(x_test), 784))
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# rescale
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x_train <- x_train / 255
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x_test <- x_test / 255
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y_train <- to_categorical(y_train, 10)
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y_test <- to_categorical(y_test, 10)
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model <- keras_model_sequential()
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model %>%
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layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>%
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layer_dropout(rate = 0.4) %>%
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layer_dense(units = 128, activation = 'relu') %>%
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layer_dropout(rate = 0.3) %>%
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layer_dense(units = 10, activation = 'softmax')
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model %>% compile(
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loss = 'categorical_crossentropy',
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optimizer = optimizer_rmsprop(),
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metrics = c('accuracy')
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)
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history <- model %>% fit(
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x_train, y_train,
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epochs = 30, batch_size = 128,
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validation_split = 0.2
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)
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model %>% evaluate(x_test, y_test)
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