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| import tensorflow as tf
import numpy as np
BATCH_SIZE = 8
seed = 23455
rng = np.random.RandomState(seed)
X = rng.rand(32, 2)
Y = [[int(x0 + x1)] for (x0, x1) in X]
print("X: \n", X)
print("Y: \n", Y)
x = tf.placeholder(tf.float32, shape=[None, 2])
y_ = tf.placeholder(tf.float32, shape=[None, 1])
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)
# loss func
loss = tf.reduce_mean(tf.square(y - y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
with tf.Session() as sess:
init_top = tf.global_variables_initializer()
sess.run(init_top)
print("w1:\n", sess.run(w1))
print("w2:\n", sess.run(w2))
print("\n")
SETPS = 3000
for i in range(SETPS):
start = (i * BATCH_SIZE) % 32
end = start + BATCH_SIZE
sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
if i % 500 == 0:
total_loss = sess.run(loss, feed_dict={x: X, y_: Y})
print("After %d training step(s) ,loss on all data is %g " % (i, total_loss))
print("\n")
print("w1:\n", sess.run(w1))
print("w2:\n", sess.run(w2))
|
