#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)
以上的代碼獲得了99.14%的訓(xùn)練精度和96.89%的驗(yàn)證精度。在我的i5處理器上跑這段代碼完整訓(xùn)練一次用時(shí)13.5秒，而在TITANx GPU上，驗(yàn)證精度可以達(dá)到98.44%，訓(xùn)練一次平均用時(shí)2秒。
為了更好地比較，我同樣使用Python來實(shí)現(xiàn)解決以上的MINIST歸類問題。結(jié)果不應(yīng)當(dāng)有任何差別，因?yàn)镽會創(chuàng)建一個(gè)進(jìn)程（conda instance）并在其中運(yùn)行keras。但你仍然可以嘗試以下同等的Python代碼。
#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))
以上模型在同樣的GPU上達(dá)到了98.42%的驗(yàn)證精度。所以，就像我們在一開始猜測的那樣，結(jié)果是相同的。
