Bug in ch3 example of linear regression

Nobody has claimed this yet.

Hi.
Thanks for this great deep learning examples!

There appears a bug in ch3 example linear_regression_tf.py
At line 42, y becomes shape(100,) but I think it should be (100,1) because y-y_pred becomes (100,100) given y_pred is (100,1). So the loss function is very largely overestimated.
Simply changing the 42 line as following makes the training converge to global minimum. (W=5, b=2)

y = tf.placeholder(tf.float32, (N,)) --> tf.placeholder(tf.float32, (N,1))

Of course, np.reshape should be removed from line 28 and some additional code change is necessary to make the script runnable.

So I don't think this example is a proper proof of gradient descent not converging to global minimum.

But I still deeply appreciate the great examples of tensorflow and it helps me with studying deep learning so much.
Thanks,

Inspect ch3 example linear_regression_tf.py, especially line 28's reshape and line 42's placeholder shape. Run the example and verify the target and prediction shapes, then confirm training converges to W=5 and b=2 without the loss being inflated by broadcasting.

Written by the indexing model from the issue text.