首先生成3×3×3的黑色图片
"""
生成3×3×3黑色图像
"""
def produce_image():size = 3x, y = ogrid[:size, :size] # 第一部分产生多行一列 第二部分产生一行多列z = x + yz = z[:, :, newaxis] # 增加第三维# print(z)img = repeat(z, 3, 2)/12 # 在第三维上复制两遍# print(img.shape)# print(img)io.imshow(img, interpolation='none')io.show()return img
打印结果：
双线性插值反卷积代码如下：
"""
生成3×3×3黑色图像
"""
def produce_image():size = 3x, y = ogrid[:size, :size] # 第一部分产生多行一列 第二部分产生一行多列z = x + yz = z[:, :, newaxis] # 增加第三维# print(z)img = repeat(z, 3, 2)/12 # 在第三维上复制两遍# print(img.shape)# print(img)io.imshow(img, interpolation='none')io.show()return img"""
上采样 双线性插值生成卷积核
"""
def upsampling_bilinear():#确定卷积核大小def get_kernel_size(factor):return 2*factor-factor%2# 创建相关矩阵def upsample_filt(size):factor=(size+1)//2if size%2==1:center=factor-1else:center=factor-0.5og=np.ogrid[:size,:size]# print(og)# print(og[0])# print(og[1])return (1-abs(og[0]-center)/factor)*(1-abs(og[1]-center)/factor)#进行上采样卷积核def bilinear_upsample_weights(factor,number_of_classes):filter_size=get_kernel_size(factor)weights=np.zeros((filter_size,filter_size,number_of_classes,number_of_classes),dtype=np.float32)upsample_kernel=upsample_filt(filter_size)# print(upsample_kernel)for i in range(number_of_classes):weights[:,:,i,i]=upsample_kernel# print(weights[:,:,i,i])# print(weights)# print(weights.shape)return weightsweights=bilinear_upsample_weights(3,3)return weights
if __name__ == '__main__':import tensorflow as tf# upsampling()# upsampling_bilinear()image=produce_image()img = tf.cast(image, dtype=tf.float32)img = tf.expand_dims(img, 0) # 增加维度#产生卷积核kerenel=upsampling_bilinear()#反卷积处理res=tf.nn.conv2d_transpose(img,kerenel,output_shape=[1,9,9,3],strides=[1,3,3,1],padding='SAME')with tf.Session() as sess:img = sess.run(res)io.imshow(img[0, :, :, :] , interpolation='none')io.show()打印结果：能较好恢复原图像
