import tensorflow as tf
import numpy as np
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
def add_layer(inputs, in_size, out_size, activation_function=None):
weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
h1 = add_layer(xs, 1, 20, activation_function = tf.nn.relu)
prediction = add_layer(h1, 20, 1, activation_function = None)
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0 :
print(sess.run(loss, feed_dict = {xs: x_data, ys: y_data}))
'''
15.5288
0.00650913
0.00399742
...
0.00237057
0.0023616
0.00235646
'''
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