R 中的 Keras API

我们在第 3 章中了解了 Keras API。在 R 中,此 API 使用keras R 包实现。 keras R 软件包实现了 Keras Python 接口的大部分功能,包括顺序 API 和函数式 API。

作为示例,我们提供了 MLP 模型的演练,用于在以下链接中对来自 MNIST 数据集的手写数字进行分类: https://keras.rstudio.com/articles/examples/mnist_mlp.html

您可以按照 Jupyter R 笔记本中的代码ch-17c_Keras_in_R

  1. 首先,加载库:
library(keras)
  1. 定义超参数:
batch_size <- 128
num_classes <- 10
epochs <- 30
  1. 准备数据:
# The data, shuffled and split between train and test sets
c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()

x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))

# Transform RGB values into [0,1] range
x_train <- x_train / 255
x_test <- x_test / 255

cat(nrow(x_train), 'train samples\n')
cat(nrow(x_test), 'test samples\n')

# Convert class vectors to binary class matrices
y_train <- to_categorical(y_train, num_classes)
y_test <- to_categorical(y_test, num_classes)

注释是不言自明的:数据从 Keras 数据集库加载,然后转换为 2D arrray 并归一化为[0,1]范围。

  1. 定义模型:
model <- keras_model_sequential()
model %>%
  layer_dense(units=256,activation='relu',input_shape=c(784)) %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 10, activation = 'softmax')

summary(model)

model %>% compile(
    loss = 'categorical_crossentropy',
    optimizer = optimizer_rmsprop(),
    metrics = c('accuracy')
)
  1. 定义和编译顺序模型。我们得到的模型定义如下:
_____________________________________________________
Layer (type)               Output Shape       Param #     
=====================================================
dense_26 (Dense)           (None, 256)        200960      
_____________________________________________________
dropout_14 (Dropout)       (None, 256)        0           
_____________________________________________________
dense_27 (Dense)           (None, 128)        32896       
_____________________________________________________
dropout_15 (Dropout)       (None, 128)        0           
_____________________________________________________
dense_28 (Dense)           (None, 10)         1290        
=====================================================
Total params: 235,146
Trainable params: 235,146
Non-trainable params: 0
  1. 训练模型:
history <- model %>% fit(
    x_train, y_train,
    batch_size = batch_size,
    epochs = epochs,
    verbose = 1,
    validation_split = 0.2
)

plot(history)

拟合函数的输出存储在历史对象中,其包含来自训练周期的损失和度量值。绘制历史对象中的数据,结果如下:

Training and Validation Accuracy (y-axis) in Epochs (x-axis)

  1. 评估模型:
score <- model %>% evaluate(
    x_test, y_test,
    verbose = 0
)

# Output metrics
cat('Test loss:', score[[1]], '\n')
cat('Test accuracy:', score[[2]], '\n')

输出如下:

Test loss: 0.1128517 
Test accuracy: 0.9816

太酷!!

在以下链接中查找更多关于 Keras in R 的示例:https://keras.rstudio.com/articles/examples/index.html

有关 Keras R 软件包的更多文档可在以下链接中找到:https://keras.rstudio.com/reference/index.html

results matching ""

    No results matching ""