I am new to Tensorflow and machine learning. I tried to modify the basic Tensorflow example in order to mimic batched input but could not get it to converge.
If I change x_data to be within [0,1) it is able to correctly compute W though.
x_data = np.random.rand(numelements,1).astype(np.float32)
Is there something wrong with my code? Here is a copy:
import tensorflow as tf
import numpy as np
# number of training samples
numelements = 100
# define input, and labled values
# note the inptu and output are actually scalar value
#x_data = np.random.rand(numelements,1).astype(np.float32)
x_data = np.random.randint(0, 10, size=(numelements,1)).astype(np.float32)
y_data = x_data * 10
# Try to find values for W and b that compute y_data = W * x + b
x = tf.placeholder(tf.float32, [None, 1])
W = tf.Variable(tf.zeros([1]))
b = tf.Variable(tf.zeros([1]))
y = tf.mul(x, W) + b
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# Before starting, initialize the variables. We will 'run' this first.
init = tf.global_variables_initializer()
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for step in range(81):
sess.run(train, feed_dict={x: x_data})
if step % 20 == 0:
print(step, sess.run(W), sess.run(b))
