深度學習，神經網絡

Question

我有一個關於在分類數據中應用神經網絡的問題。

1-我有一個數字輸出（ Connection.Duration ）

2-我有5個輸入，它們中的4（ EVSE.ID ， User.ID ， Fee ， Day ）是分類和1（ Time ）是數字。

我想應用神經網絡來預測Connection.Duration 。 我不知道用於分類數據的正確命令。 我使用了model.matrix但沒有繼續使用包含分類數據的新數據框（ m ）。

我想尋求幫助。

data$Fee <- as.factor(data$Fee)
data$EVSE.ID <- as.factor(data$EVSE.ID)
data$User.ID <- as.factor(data$User.ID)
data$Day <- as.factor(data$Day)
data$Time <- as.factor(data$Time)
data$Connection.Duration <- as.factor(data$Connection.Duration)

m <- model.matrix(Connection.Duration ~ EVSE.ID+Time+Day+Fee+User.ID,
              data= data)

# Neural Networks 
n <- neuralnet(Connection.Duration ~ EVSE.ID+Time+Day+Fee+User.ID,
           data = m,
           hidden=c(100,60))

# Data partition 
set.seed(1234)
ind <- sample(2, nrow(m), replace = TRUE, prob = c(0.7, 0.3))
training <- m[ind==1,1:5]
testing <- m[ind==2,1:5]
trainingtarget <- m[ind==1, 6]
testingtarget <- m[ind==2, 6]

# Normalize
m <- colMeans(training)
s <- apply(training, 2, sd)
training <- scale(training, center = m, scale = s)
testing <- scale(testing, center = m, scale = s)

# Create Model
model <- keras_model_sequential()
model %>%
layer_dense(units = 5, activation = 'relu', input_shape = c(5)) %>%
layer_dense(units = 1)

# Compile
model %>% compile(loss= 'mse',
              optimizer= 'rmsprop',
              metrics='mae')
# Fit model 
mymodel <- model %>%

  fit(training,
      trainingtarget,
      epochs= 100,
      batch_size = 32,
      validation_split = 0.2)

# Evaluate 
model %>% evaluate(testing, testingtarget)
pred <- model %>% predict(testing)
mean(testingtarget- pred^2)
plot(testingtarget, pred)

# Fine-tune Model
model <- keras_model_sequential()
model %>%
layer_dense(units = 100, activation = 'relu', input_shape = c(5)) %>%
 layer_dropout(rate = 0.4) %>%
layer_dense(units = 60, activation = 'relu', input_shape = c(5)) %>%
 layer_dropout(rate = 0.2) %>%
 layer_dense(units = 1)

# Compile
model %>% compile(loss= 'mse',
              optimizer= optimizer_rmsprop(lr=0.0001),
              metrics='mae')

# Fit model
mymodel <- model %>%
  fit(training,
      trainingtarget,
      epochs= 100,
      batch_size = 32,
      validation_split = 0.2)

# Evaluate 
model %>% evaluate(testing, testingtarget)
  pred <- model %>% predict(testing)
  mean(testingtarget- pred^2)
  plot(testingtarget, pred)

Answer 1

您正在尋找的被稱為“一種熱編碼”。 tensorflow / keras中有一些功能可以幫助編碼。

但是否則，我會嘗試先做。 我不會依賴model.matrix因為它不能完全滿足您的需求。

您可以輕松編寫自己的函數，但這是使用mltools軟件包的示例：

library(data.table)
library(mltools)
one_hot(data.table(x = factor(letters), n = 1:26))

注意：它需要data.table而不是data.frame但是您可以來回轉換數據。