VisualNetwork.py

# coding: utf-8

# In[127]:

import numpy as np 
from scipy.signal import convolve2d as conv2d
p_label = np.array([[[1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0]],
                    [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]],
                    [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]],
                    [[1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0]]])


# In[128]:

p_label.shape


# In[133]:

kernel = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) / 16.0    


# In[134]:

kernel, kernel.shape


# In[135]:

print(p_label[:,:,0])
print(conv2d(p_label[:,:,0], kernel, boundary='fill', mode='same'))


# In[136]:

print(p_label[:,:,1])
print(conv2d(p_label[:,:,1], kernel, boundary='fill', mode='same'))


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