Import the relevant libraries and initialize the hyper-parameters
In [1]:
%matplotlib inline
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
learning_rate = 0.01
training_epochs = 40
Set up some fake raw input data
In [2]:
trX = np.linspace(-1, 1, 101)
Set up raw output data based on a degree 6 polynomial
In [3]:
num_coeffs = 6
trY_coeffs = [1, 2, 3, 4, 5, 6]
trY = 0
for i in range(num_coeffs):
trY += trY_coeffs[i] * np.power(trX, i)
Add some noise
In [4]:
trY += np.random.randn(*trX.shape) * 1.5
Plot the raw data
In [5]:
plt.scatter(trX, trY)
plt.show()
Define the nodes to hold values for input/output pairs
In [6]:
X = tf.placeholder("float")
Y = tf.placeholder("float")
Define our polynomial model
In [7]:
def model(X, w):
terms = []
for i in range(num_coeffs):
term = tf.multiply(w[i], tf.pow(X, i))
terms.append(term)
return tf.add_n(terms)
Set up the parameter vector to all zero
In [8]:
w = tf.Variable([0.] * num_coeffs, name="parameters")
y_model = model(X, w)
Define the cost function just as before
In [9]:
cost = tf.reduce_sum(tf.square(Y-y_model))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
Set up the session and run the learning algorithm just as before
In [10]:
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for epoch in range(training_epochs):
for (x, y) in zip(trX, trY):
sess.run(train_op, feed_dict={X: x, Y: y})
w_val = sess.run(w)
print(w_val)
Close the session when done
In [11]:
sess.close()
Plot the result
In [12]:
plt.scatter(trX, trY)
trY2 = 0
for i in range(num_coeffs):
trY2 += w_val[i] * np.power(trX, i)
plt.plot(trX, trY2, 'r')
plt.show()