import tensorflow as tf
import numpy as np
sess = tf.Session()

#將size設為[1, 4, 4, 1]是因為tf中圖像函數(shù)是處理四維圖片的。
#這四維依次是： 圖片數(shù)量，高度， 寬度， 顏色通道
x_shape = [1,4,4,1]
x_val = np.random.uniform(size = x_shape)


#tf.nn.conv2d中name表明該layer命名為“Moving_Avg_Window”
#該卷積核為[[0.25,0.25],[0.25,0.25]],所以是一個求平均操作
x_data = tf.placeholder(tf.float32, shape = x_shape)
my_filter = tf.constant(0.25, shape = [2,2,1,1])
my_strides = [1,2,2,1]
mov_avg_layer = tf.nn.conv2d(x_data, my_filter, my_strides, padding = 'SAME', name = 'Moving_Avg_Window')


#自定義layer，對卷積操作之后的輸出做操作
def custom_layer(input_matrix):
  input_matrix_sqeeze = tf.squeeze(input_matrix)
  A = tf.constant([1.,2.],[-1.,3.])
  b = tf.constant(1., shape = [2,2])
  temp1 = tf.matmul(A, input_matrix_sqeeze)
  temp2 = tf.add(temp1, b)
  return(tf.sigmod(temp2))
#把剛剛自定義的layer加入到計算圖中，并給予自定義的命名(利用tf.name_scope())
with tf.name_scope('Custom_Layer') as scope:
  custom_layer1 = custom_layer(mov_avg_layer)


#為占位符傳入4*4圖片，并執(zhí)行計算圖
print(sess.run(custom_layer, feed_dict= {x_data: x_val}))