# lab2

https://tensorflow.rstudio.com/

https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-02-1-linear_regression.py

```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
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)
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 1:365000) {
#   sess\$run(train)
#   if (step %% 300 == 0)
#     #cat(step, "|cost:", sess\$run(loss), "|b:", sess\$run(b),"|W:", sess\$run(W), "\n")
#     cat(step,"|",sess\$run(loss),"|",sess\$run(b),"|",sess\$run(W),"\n")
# }

(fit.a <- lm(y_data ~ x_data))

########################################################################################

capture.output(
for (step in 1:365000) {
sess\$run(train)
if (step %% 300 == 0)
#cat(step, "|cost:", sess\$run(loss), "|b:", sess\$run(b),"|W:", sess\$run(W), "\n")
cat(step,"|",sess\$run(loss),"|",sess\$run(b),"|",sess\$run(W),"\n")
},
file = "foo.txt")

library(rgl)

plot3d(
FOO\$V3, FOO\$V4, FOO\$V2,
xlab="b", ylab="W", zlab="LOSS", type = "p",
lwd = 100, col = c("red","blue"), radius = 0.1
)
rglwidget()```

Categories: DL

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