In [7]:

    

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


    

In [8]:

    

# x and y are placeholders for our training data
x = tf.placeholder("float")
y = tf.placeholder("float")


    

In [9]:

    

# w is the variable storing our values. It is initialised with starting "guesses"
# w[0] is the "a" in our equation, w[1] is the "b"
w = tf.Variable([1.0, 2.0], name="w")
# Our model of y = a*x + b
y_model = tf.mul(x, w[0]) + w[1]


    

In [16]:

    

# Our error is defined as the square of the differences
error = tf.square(y - y_model)
# The Gradient Descent Optimizer does the heavy lifting
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(error)


    

In [11]:

    

# Normal TensorFlow - initialize values, create a session and run the model
model = tf.initialize_all_variables()


    

In [14]:

    

errors = []
with tf.Session() as session:
    session.run(model)
    for i in range(1000):
        x_value = np.random.rand()
        y_value = x_value * 2 + 6
        _, error_value, y_model_value , w_value = session.run([train_op, error, y_model, w], feed_dict={x: x_value, y: y_value})
        errors.append(error_value)
        
    w_value = session.run(w)
    print("Predicted model: {a:.3f}x + {b:.3f}".format(a=w_value[0], b=w_value[1]))


    

    




Predicted model: 2.267x + 5.853

In [15]:

    

plt.plot([np.mean(errors[i-50:i]) for i in range(len(errors))])
plt.show()


    

    




/Users/paulrad/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
  warnings.warn("Mean of empty slice.", RuntimeWarning)

In [ ]: