auto cnn layer.py

import tensorflow as tf

X = tf.placeholder("float")

init1 = 100
init = 0
step = 10
K = int(init1/step) - 1
W={}
b={}
for i in range(K):
    init = init1
    init1 -= step
    W[i] = tf.Variable(tf.random_normal([init,init1]))
    b[i]=tf.Variable(tf.random_normal([init1]))
    if i==0:
        L=tf.nn.sigmoid(tf.matmul(X,W[0])+b[0])
    else:
        L=tf.nn.sigmoid(tf.matmul(L,W[i])+b[i])
    

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(K):
        q = tf.shape(W[i])
        print(sess.run(q))