我正在尝试仅使用 numpy 来实现 CNN。我在最大池化层中收到索引超出范围的错误。
该函数将特征图数组作为参数。特征图数组是一个ndarray。这是我的功能:
feature_map = np.array([[[4, 3, 4],[2, 4, 3],[2, 3, 4]],
[[3, 4, 2],[2, 4, 4],[2, 4, 2]],
[[5, 7, 6],[2, 1, 3],[3, 3, 8]],
[[3, 3, 2],[1, 3, 5],[7, 4, 9,]]])
def pool_forward(feature_map, mode = "max", size=2, stride=2):
f_num, f_row, f_col = feature_map.shape
#Preparing the output of the pooling operation.
pool_out = np.zeros((np.uint16((f_row-size+1)/stride+1),
np.uint16((f_col-size+1)/stride+1), f_num))
for map_num in range(f_num):
r2 = 0
for r in np.arange(0,f_row-size+1, stride):
c2 = 0
for c in np.arange(0, f_col-size+1, stride):
pool_out[r2, c2, map_num] = np.max([feature_map[r:r+size,
c:c+size, map_num]])
c2 = c2 + 1
r2 = r2 +1
return np.array(pool_res)
这是我得到的错误:
--------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-104-ccb65cb3a606> in <module>()
----> 1 feature_pool = pool_forward(features)
2 feature_pool.shape
<ipython-input-102-5d4c4e76f99a> in pool_forward(feature_map, mode,
filter_size, stride)
13 c2 = 0
14 for c in np.arange(0, f_col-filter_size+1, stride):
---> 15 pool_out[r2, c2, map_num] =
np.max([feature_map[r:r+filter_size, c:c+filter_size, map_num]])
16 c2 = c2 + 1
17 r2 = r2 +1
IndexError: index 3 is out of bounds for axis 2 with size 3
帮帮我。
请您参考如下方法:
请检查答案的更新部分:
错误在于行:
pool_out[r2, c2, map_num] = np.max([feature_map[r:r+size, c:c+size, map_num]])
应该是:
pool_out[r2, c2, map_num] = np.max([feature_map[map_num, r:r+size, c:c+size]])
现在:
def pool_forward(feature_map, mode = "max", size=2, stride=2):
f_num, f_row, f_col = feature_map.shape
#Preparing the output of the pooling operation.
pool_out = np.zeros((np.uint16((f_row-size+1)/stride+1),
np.uint16((f_col-size+1)/stride+1), f_num))
for map_num in range(f_num):
r2 = 0
for r in np.arange(0,f_row-size+1, stride):
c2 = 0
for c in np.arange(0, f_col-size+1, stride):
pool_out[r2, c2, map_num] = np.max([feature_map[map_num, r:r+size, c:c+size]])
c2 = c2 + 1
r2 = r2 +1
return np.array(pool_res)
feature_map = np.array([[[4, 3, 4],[2, 4, 3],[2, 3, 4]],
[[3, 4, 2],[2, 4, 4],[2, 4, 2]],
[[5, 7, 6],[2, 1, 3],[3, 3, 8]],
[[3, 3, 2],[1, 3, 5],[7, 4, 9,]]])
pool_forward(feature_map)
返回:
array([[[4., 4., 7., 3.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.]]])
更新:问题前提不正确。对于形状为 3*3 的输入，您的池化窗口大小为 2*2，步幅为 2，那么您可能需要查看 fractional_max_pooling。对于常规 max_pooling，您应该选择步幅为 1。(即值 (f_row-size)/stride 应该是一个整数)。在这种情况下，请查看以下代码:
feature_map = np.array([[[4, 3, 4],[2, 4, 3],[2, 3, 4]],
[[3, 4, 2],[2, 4, 4],[2, 4, 2]],
[[5, 7, 6],[2, 1, 3],[3, 3, 8]],
[[3, 3, 2],[1, 3, 5],[7, 4, 9,]]])
def pool_forward(feature_map, mode = "max", size=2, stride=1):
f_num, f_row, f_col = feature_map.shape
pool_out = np.zeros((f_num,np.uint16((f_row-size)/stride+1),\
np.uint16((f_col-size)/stride+1)))
for z in range(f_num):
for r in np.arange(0,f_row-size+1, stride):
for c in np.arange(0, f_col-size+1, stride):
pool_out[z, r, c] = np.max(feature_map[z, r:r+size, c:c+size])
return pool_out
pool_forward(feature_map) 返回:
array([[[4., 4.],
[4., 4.]],
[[4., 4.],
[4., 4.]],
[[7., 7.],
[3., 8.]],
[[3., 5.],
[7., 9.]]])
这似乎是正确的。我还扔了变量 c2 和 r2，因为它们似乎不是必需的。
