tensorflow1.0 构建神经网络做非线性归回

"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
#tensorboard --logdir="./"
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
    with tf.name_scope("layer"):
        with tf.name_scope("weights"):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]),name="W")
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
        with tf.name_scope("Wx_plus_b"):
            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
with tf.name_scope("inputs"):
    xs = tf.placeholder(tf.float32, [None, 1],name="x_input")
    ys = tf.placeholder(tf.float32, [None, 1],name="y_input")
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.tanh)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)

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

# important step
init = tf.initialize_all_variables()
sess = tf.Session()
writer = tf.summary.FileWriter("./",sess.graph)
sess.run(init)

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 see the step improvement
        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
        try:
            ax.lines.remove(lines[0])
        except Exception:
            prediction_value = sess.run(prediction,feed_dict={xs: x_data, ys: y_data})
            lines = ax.plot(x_data,prediction_value,"r-",lw = 5)
            plt.pause(0.1)