R语言神经网络keras,tensorflow,mxnet

library(keras)

#loading the keras inbuilt mnist dataset

data<-dataset_mnist()

#separating train and test file

train_x<-data$train$x

train_y<-data$train$y

test_x<-data$test$x

test_y<-data$test$y

rm(data)

# converting a 2D array into a 1D array for feeding into the MLP and normalising the matrix

train_x <- array(train_x, dim = c(dim(train_x)[1], prod(dim(train_x)[-1]))) / 255

test_x <- array(test_x, dim = c(dim(test_x)[1], prod(dim(test_x)[-1]))) / 255

#converting the target variable to once hot encoded vectors using keras inbuilt function

train_y<-to_categorical(train_y,10)

test_y<-to_categorical(test_y,10)

#defining a keras sequential model

model <- keras_model_sequential()

#defining the model with 1 input layer[784 neurons], 1 hidden layer[784 neurons] with dropout rate 0.4 and 1 output layer[10 neurons]

#i.e number of digits from 0 to 9

model %>%

  layer_dense(units = 784, input_shape = 784) %>%

  layer_dropout(rate=0.4)%>%

  layer_activation(activation = 'relu') %>%

  layer_dense(units = 10) %>%

  layer_activation(activation = 'softmax')

#compiling the defined model with metric = accuracy and optimiser as adam.

model %>% compile(

  loss = 'categorical_crossentropy',

  optimizer = 'adam',

  metrics = c('accuracy')

)

#fitting the model on the training dataset

model %>% fit(train_x, train_y, epochs = 100, batch_size = 128)

#Evaluating model on the cross validation dataset
loss_and_metrics <- model %>% evaluate(test_x, test_y, batch_size = 128)

###################################################################

library(tensorflow)

sess = tf$Session()

hello <- tf$constant('Hello, TensorFlow!')

sess$run(hello)

library(tensorflow)

# Create 100 phony x, y data points, y = x * 0.1 + 0.3

x_data <- runif(100, min=0, max=1)

y_data <- x_data * 0.1 + 0.3

# Try to find values for W and b that compute y_data = W * x_data + b

# (We know that W should be 0.1 and b 0.3, but TensorFlow will

# figure that out for us.)

W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))

b <- tf$Variable(tf$zeros(shape(1L)))

y <- W * x_data + b

# Minimize the mean squared errors.

loss <- tf$reduce_mean((y - y_data) ^ 2)

optimizer <- tf$train$GradientDescentOptimizer(0.5)

train <- optimizer$minimize(loss)

# Launch the graph and initialize the variables.

sess = tf$Session()

sess$run(tf$initialize_all_variables())

# Fit the line (Learns best fit is W: 0.1, b: 0.3)

for (step in 1:201) {

  sess$run(train)

  if (step %% 20 == 0)

    cat(step, "-", sess$run(W), sess$run(b), "\n")

}

################################################################

library(mlbench)

library(mxnet)

data(Sonar, package="mlbench")

str(Sonar)

 Sonar[,61] = as.numeric(Sonar[,61])-1

 train.ind = c(1:50, 100:150)

 train.x = data.matrix(Sonar[train.ind, 1:60])

 train.y = Sonar[train.ind, 61]

 test.x = data.matrix(Sonar[-train.ind, 1:60])

 test.y = Sonar[-train.ind, 61]

 

 mx.set.seed(0)

 # 模型，num.round表示迭代数，array.batch.size表示批规模

 model <- mx.mlp(train.x, train.y, hidden_node=10, out_node=2,out_activation="softmax", 

                 num.round=20, array.batch.size=15, learning.rate=0.07, momentum=0.9, eval.metric=mx.metric.accuracy)

 

 

preds = predict(model, test.x)

pred.label = max.col(t(preds))-1

table(pred.label, test.y)

(24+33) / (24+14+36+33)