张量流中的线性模型

Question

I was trying to generate a simple linear model in Tensorflow. Here is the code ...

N        = 400
features = 100
nSteps   = 1000

data = (np.random.randn(N, features), np.random.randint(0, 2, N))

W = tf.placeholder(tf.float32, shape=(features,1), name='W')
b = tf.placeholder(tf.float32, shape=(features,1), name='b')
d = tf.constant(data[0], dtype=tf.float32)

result = tf.add( tf.matmul(d, W), b)

It turns out that there might be some problem with the dimensions of b , but for some reason as far as I can say, they are all ok ...

Not sure why this is throwing an error. Can someone please help?

Note:

result = tf.matmul(d, W)

This is ok.

I have checked the shape of the result, and is the same as that of b . Not really sure what might be the problem.

Answer 1

In a linear model (ie one unit in the output layer), b should be a scalar.

Mathematically, for a single observation, you have: result = WX + b , where dimensions W [1 x features], X [features x 1]. Then, WX is scalar. Thus b should be a scalar.

So you should change b to the following, to get the correct linear model and make the dimensions work out:

b = tf.placeholder(tf.float32, shape=(1,1), name='b')