# linear_regression_simple

Simple model that learns W and b by minimizing mean squared errors via gradient descent.

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")
}