代碼

"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs, in_size, out_size, activation_function=None):
??? # add one more layer and return the output of this layer
??? 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

# Make up some real data
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

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)

# the error between prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
???????????????????? reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

# important step
#init = tf.initialize_all_variables()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

# plot the real data
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()

for i in range(1000):
??? # training
??? sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
??? if i % 50 == 0:
??????? # to visualize the result and improvement
??????? try:
??????????? ax.lines.remove(lines[0])
??????? except Exception:
??????????? pass
??????? prediction_value = sess.run(prediction, feed_dict={xs: x_data})
??????? # plot the prediction
??????? lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
??????? plt.pause(0.1)

結果：

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總結

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