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author | Norbert Preining <norbert@preining.info> | 2021-12-27 03:02:58 +0000 |
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committer | Norbert Preining <norbert@preining.info> | 2021-12-27 03:02:58 +0000 |
commit | 790995b7e79697514364450bf9c04f1b8d500838 (patch) | |
tree | a59b89b3cfb2e5def88455fa463f95e9a2aaea5f /macros/latex/contrib/codebox/hellopy.py | |
parent | 4a2abb95db9b87c04422a05174b2606b2c8e1d2b (diff) |
CTAN sync 202112270302
Diffstat (limited to 'macros/latex/contrib/codebox/hellopy.py')
-rwxr-xr-x | macros/latex/contrib/codebox/hellopy.py | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/macros/latex/contrib/codebox/hellopy.py b/macros/latex/contrib/codebox/hellopy.py new file mode 100755 index 0000000000..af687ef384 --- /dev/null +++ b/macros/latex/contrib/codebox/hellopy.py @@ -0,0 +1,32 @@ +import tensorflow as tf +import numpy as np +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +# Create 100 phony x, y data points in Numpy, y = x * 0.1 + 0.3 +x_data = np.random.random(100).astype("float32") +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([1], -1.0, 1.0)) +b = tf.Variable(tf.zeros([1])) +y = W * x_data + b + +# Minimize the mean squared errors. +loss = tf.reduce_mean(tf.square(y -y_data)) +optimizer = tf.train.GradientDescentOptimizer(0.5) +train = optimizer.minimize(loss) + +# Before starting, initialize the variables. We will 'run' this first +init = tf.global_variables_initializer() + +# Launch the graph. +sess = tf.Session() +sess.run(init) + +# Fit the line. +for step in range(201): + sess.run(train) + if step % 20 == 0: + print(step, sess.run(W), sess.run(b)) + |