我以前没有使用Tensorflow或Keras的经验。我正在尝试按照教程https://tensorflow.rstudio.com/tutorials/beginners/

library(keras)

mnist <- dataset_mnist()
mnist$train$x <- mnist$train$x/255
mnist$test$x <- mnist$test$x/255

model <- keras_model_sequential() %>% 
  layer_flatten(input_shape = c(28, 28)) %>% 
  layer_dense(units = 128, activation = "relu") %>% 
  layer_dropout(0.2) %>% 
  layer_dense(10, activation = "softmax")

summary(model)

model %>% 
  compile(
    loss = "sparse_categorical_crossentropy",
    optimizer = "adam",
    metrics = "accuracy"
  )

#Note that compile and fit (which we are going to see next) modify the model object in place, unlike most R functions.

model %>% 
  fit(
    x = mnist$train$x, y = mnist$train$y,
    epochs = 5,
    validation_split = 0.3,
    verbose = 2
  )

predictions <- predict(model, mnist$test$x)
head(predictions, 2)

class_predictions <- predict(model, mnist$test$x) %>% k_argmax()
class_predictions

predict_classes已被弃用。在错误中，k_armax（）被作为替代方法发布。但是，我不知道如何将predicted_classes（在本例中为数字0-9）作为向量在confusionMatrix中使用，就像其他R模型一样。如有任何帮助，将不胜感激。