Tensorflow network for curve fitting

curve_fit.py

'''
	References used for learning
		https://github.com/aymericdamien/TensorFlow-Examples
		https://datascience.stackexchange.com/questions/20058/tensorflow-regression-using-deep-neural-network
'''

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

size = 1000
def generate_data(size):
    x = np.array(np.random.rand(size,))
    y = 8 * (x**2) + 8*x + 5
    return np.transpose([x]),np.transpose([y])
    
vector_X,vector_Y = generate_data(size)

# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 1
num_classes = 1 

# Parameters
learning_rate = 0.0001
num_steps = 400
batch_size = 128
display_step = 10

# tf Graph input
X = tf.placeholder("float", [None, num_input])
Y = tf.placeholder("float", [None, num_classes])

# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([num_classes]))
}

# Create model
def neural_net(x):
    # Hidden fully connected layer with 256 neurons
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    # Hidden fully connected layer with 256 neurons
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    # Output fully connected layer with a neuron for each class
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer

# Construct model
prediction = neural_net(X)

# Define cost and optimizer
loss = tf.reduce_mean(tf.square((prediction - Y)))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)

plotValues = []
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for step in range(1, num_steps+1):
        loss_val,_ = sess.run([loss, optimizer], feed_dict={X: vector_X,Y: vector_Y})
        plotValues.append(loss_val)
        if step % display_step == 0 or step == 1:
            print("Step " + str(step) + ", Loss= " + "{:.4f}".format(loss_val))
    print("Optimization Finished!")

plt.plot(plotValues)
plt.xlabel("Iterations")
plt.ylabel("cost")
plt.show()