tensorflow_regression_model.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# define function that add layer in NN
def add_layer(inputs, in_size, out_size, activation_function=None):

    with tf.name_scope("layer"):
        with tf.name_scope("Weight"):
            Weights = tf.Variable(tf.random_normal([in_size,out_size]), name="W")
        with tf.name_scope("Bias"):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1, name="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


# print(x_data)
# plt.scatter(x_data,y_data)
# plt.show()

# define placeholders for 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.relu)
# add output layer
prediction = add_layer(l1,10,1, activation_function= None)

# error between real and prediction
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 = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.initialize_all_variables()


# run session
sess = tf.Session()
# log the graph data
writer = tf.summary.FileWriter('logs/', sess.graph)
sess.run(init)
# plotting the figure
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()

for i in range(2000):
    #training
    sess.run(train, feed_dict={xs:x_data, ys:y_data})
    
    if i % 100 == 0:
    
        # print steps
        # print(sess.run(loss, feed_dict={xs:x_data, ys:y_data}))
        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=2)
        plt.pause(0.4)

sess.close()

# tensorboard --logdir='logs'  and see for localhost:6006