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#loading keras library 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) 以上的代码获得了99.14%的训练精度和96.89%的验证精度。在我的i5处理器上跑这段代码完整训练一次用时13.5秒，而在TITANx GPU上，验证精度可以达到98.44%，训练一次平均用时2秒。 四、使用keras来构建MLP模型——R Vs. Python为了更好地比较，我同样使用Python来实现解决以上的MINIST归类问题。结果不应当有任何差别，因为R会创建一个进程（conda instance）并在其中运行keras。但你仍然可以尝试以下同等的Python代码。 #importing the required libraries for the MLP model import keras from keras.models import Sequential import numpy as np #loading the MNIST dataset from keras from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() #reshaping the x_train, y_train, x_test and y_test to conform to MLP input and output dimensions x_train=np.reshape(x_train,(x_train.shape[0],-1))/255 x_test=np.reshape(x_test,(x_test.shape[0],-1))/255 import pandas as pd y_train=pd.get_dummies(y_train) y_test=pd.get_dummies(y_test) #performing one-hot encoding on target variables for train and test y_train=np.array(y_train) y_test=np.array(y_test) #defining model with one input layer[784 neurons], 1 hidden layer[784 neurons] with dropout rate 0.4 and 1 output layer [10 #neurons] model=Sequential() from keras.layers import Dense model.add(Dense(784, input_dim=784, activation='relu')) keras.layers.core.Dropout(rate=0.4) model.add(Dense(10,input_dim=784,activation='softmax')) # compiling model using adam optimiser and accuracy as metric model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy']) # fitting model and performing validation model.fit(x_train,y_train,epochs=50,batch_size=128,validation_data=(x_test,y_test)) 以上模型在同样的GPU上达到了98.42%的验证精度。所以，就像我们在一开始猜测的那样，结果是相同的。 |
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