import simple_and_naive_tensorflow as tf
import numpy as np
# Prepare train data
# w=2, b=10, err=0.33
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10
# Define the model
X = tf.placeholder("float", "X")
Y = tf.placeholder("float", "Y")
w = tf.Variable(0.0, name="weight")
b = tf.Variable(0.0, name="bias")
loss = tf.square(Y - X * w - b)
train_op = tf.GradientDescentOptimizer(0.01).minimize(loss)
# Create session to run
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
epoch = 1
for i in range(10):
for (x, y) in zip(train_X, train_Y):
_, w_value, b_value = sess.run([train_op, w, b],feed_dict={X: x, Y: y})
print("Epoch: {}, w: {}, b: {}".format(epoch, w_value, b_value))
epoch += 1