DeepLearning_Lab03_2 - 8BitsCoding/RobotMentor GitHub Wiki

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# Lab 3 Minimizing Cost
import tensorflow as tf

# tf Graph Input
X = [1, 2, 3]
Y = [1, 2, 3]

# Set wrong model weights
W = tf.Variable(5.0)

# Linear model
hypothesis = X * W

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize: Gradient Descent Optimizer
train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# Launch the graph in a session.
with tf.Session() as sess:
    # Initializes global variables in the graph.
    sess.run(tf.global_variables_initializer())

    for step in range(101):
        _, W_val = sess.run([train, W])
        print(step, W_val)

"""
0 5.0
1 1.2666664
2 1.0177778
3 1.0011852
4 1.000079
...
97 1.0
98 1.0
99 1.0
100 1.0
"""