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$global_variables_initializer()) # Fit the line (Learns best fit is W: 0.1, b: 0.3) for (step in 1:201) { sess$run(train) if (step %% 20 == 0) cat(step, "-", sess$run(W), sess$run(b), "\n") }
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