Created
June 7, 2017 08:33
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import numpy as np | |
import tensorflow as tf | |
W = tf.Variable([.3], tf.float32) | |
b = tf.Variable([-.3], tf.float32) | |
x = tf.placeholder(tf.float32) | |
model = W * x + b | |
y = tf.placeholder(tf.float32) | |
loss = tf.reduce_sum(tf.square(model - y)) | |
optimizer = tf.train.GradientDescentOptimizer(0.01) | |
train = optimizer.minimize(loss) | |
x_train = [1,2,3,4] | |
y_train = [0,-1,-2,-3] | |
init = tf.global_variables_initializer() | |
sess = tf.Session() | |
sess.run(init) | |
for i in range(1000): | |
sess.run(train, {x:x_train, y:y_train}) | |
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train}) | |
print("W: %s b; %s loss: %s"%(curr_W, curr_b, curr_loss)) |
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