【R笔记】基于R构建tensorflow框架实现神经网络

实现代码

library(tensorflow)# Create 100 phony x, y data points, y = x * 0.1 + 0.3x_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")}

运行结果如下：