tenserflow-1

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

rng = numpy.random

# Parameters
learning_rate = 0.01
training_epochs = 2000
display_step = 50

# Training Data
train_X = numpy.asarray(
    [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1])
train_Y = numpy.asarray(
    [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3])
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Create Model

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# Construct a linear model
activation = tf.add(tf.mul(X, W), b)

# Minimize the squared errors
cost = tf.reduce_sum(tf.pow(activation - Y, 2)) / (2 * n_samples)  # L2 loss
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)  # Gradient descent

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Fit all training data
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict={X: x, Y: y})

        # Display logs per epoch step
        if epoch % display_step == 0:
            print "Epoch:", ‘%04d‘ % (epoch + 1), "cost=",                 "{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y: train_Y})),                 "W=", sess.run(W), "b=", sess.run(b)

    print "Optimization Finished!"
    print "cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}),         "W=", sess.run(W), "b=", sess.run(b)

    # Graphic display
    plt.plot(train_X, train_Y, ‘ro‘, label=‘Original data‘)
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label=‘Fitted line‘)
    plt.legend()
    plt.show()