1
x = tf.placeholder(dtype=tf.float32, shape=[batch_size,784])
2
y = tf.placeholder(dtype=tf.float32, shape=[batch_size,10])
3
4
w1 = tf.Variable(np.random.normal(scale=np.sqrt(2./784),size=[784,512]).astype(np.float32))
5
b1 = tf.Variable(np.zeros(512,dtype=np.float32))
6
w2 = tf.Variable(np.random.normal(scale=np.sqrt(2./512),size=[512,512]).astype(np.float32))
7
b2 = tf.Variable(np.zeros(512,dtype=np.float32))
8
w3 = tf.Variable(np.random.normal(scale=np.sqrt(2./512),size=[512,10]).astype(np.float32))
9
b3 = tf.Variable(np.zeros(10,dtype=np.float32))
10
11
params = [w1,b1,w2,b2,w3,b3]
12
nr_params = sum([np.prod(p.get_shape().as_list()) for p in params])
13
scaling = 2**125
14
15
def get_logits(par):
16
    h1 = tf.nn.bias_add(tf.matmul(x , par[0]), par[1]) / scaling
17
    h2 = tf.nn.bias_add(tf.matmul(h1, par[2]) , par[3] / scaling)   
18
    o =   tf.nn.bias_add(tf.matmul(h2, par[4]), par[5]/ scaling)*scaling
19
return o