在学习完本教程之后，我将尝试可视化特征图。
我的模型如下所示:
model.summary()
Model: "model_3"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
efficientnet-b0 (Functional) (None, 7, 7, 1280) 4049564 input_5[0][0]
__________________________________________________________________________________________________
flatten_4 (Flatten) (None, 62720) 0 efficientnet-b0[0][0]
__________________________________________________________________________________________________
branch_0_Dense_16000 (Dense) (None, 256) 16056576 flatten_4[0][0]
__________________________________________________________________________________________________
branch_1_Dense_16000 (Dense) (None, 256) 16056576 flatten_4[0][0]
__________________________________________________________________________________________________
branch_2_Dense_16000 (Dense) (None, 256) 16056576 flatten_4[0][0]
__________________________________________________________________________________________________
branch_3_Dense_16000 (Dense) (None, 256) 16056576 flatten_4[0][0]
__________________________________________________________________________________________________
branch_4_Dense_16000 (Dense) (None, 256) 16056576 flatten_4[0][0]
__________________________________________________________________________________________________
branch_5_Dense_16000 (Dense) (None, 256) 16056576 flatten_4[0][0]
__________________________________________________________________________________________________
branch_6_Dense_16000 (Dense) (None, 256) 16056576 flatten_4[0][0]
__________________________________________________________________________________________________
branch_0_output (Dense) (None, 35) 8995 branch_0_Dense_16000[0][0]
__________________________________________________________________________________________________
branch_1_output (Dense) (None, 35) 8995 branch_1_Dense_16000[0][0]
__________________________________________________________________________________________________
branch_2_output (Dense) (None, 35) 8995 branch_2_Dense_16000[0][0]
__________________________________________________________________________________________________
branch_3_output (Dense) (None, 35) 8995 branch_3_Dense_16000[0][0]
__________________________________________________________________________________________________
branch_4_output (Dense) (None, 35) 8995 branch_4_Dense_16000[0][0]
__________________________________________________________________________________________________
branch_5_output (Dense) (None, 35) 8995 branch_5_Dense_16000[0][0]
__________________________________________________________________________________________________
branch_6_output (Dense) (None, 35) 8995 branch_6_Dense_16000[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 245) 0 branch_0_output[0][0]
branch_1_output[0][0]
branch_2_output[0][0]
branch_3_output[0][0]
branch_4_output[0][0]
branch_5_output[0][0]
branch_6_output[0][0]
__________________________________________________________________________________________________
reshape_4 (Reshape) (None, 7, 35) 0 concatenate_4[0][0]
==================================================================================================
Total params: 116,508,561
Trainable params: 116,466,545
Non-trainable params: 42,016
10 branch_0_output (None, 35)3 branch_0_Dense_16000 (None, 256)
4 branch_1_Dense_16000 (None, 256)
5 branch_2_Dense_16000 (None, 256)
6 branch_3_Dense_16000 (None, 256)
7 branch_4_Dense_16000 (None, 256)
8 branch_5_Dense_16000 (None, 256)
9 branch_6_Dense_16000 (None, 256)
10 branch_0_output (None, 35)
11 branch_1_output (None, 35)
12 branch_2_output (None, 35)
13 branch_3_output (None, 35)
14 branch_4_output (None, 35)
15 branch_5_output (None, 35)
16 branch_6_output (None, 35)
我在教程中使用了代码，并修改了平方数，这里是1，但是我尝试了几次:
# plot all 35 maps
square = 1
ix = 1
for _ in range(square):
for _ in range(square):
# specify subplot and turn of axis
ax = pyplot.subplot(square, square, ix)
ax.set_xticks([])
ax.set_yticks([])
# plot filter channel in grayscale
pyplot.imshow(feature_maps[0, :, :, ix-1], cmap='gray')
ix += 1
# show the figure
pyplot.show()
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-28-4c1f464f6978> in <module>()
9 ax.set_yticks([])
10 # plot filter channel in grayscale
---> 11 pyplot.imshow(feature_maps[0, :, ix-1], cmap='gray')
12 ix += 1
13 # show the figure
IndexError: too many indices for array
非常感谢!
最佳答案
该错误在第11行显示too many indices for array。之所以发生这种情况，是因为您在要素图中错误地传递了索引。在这里，您要在正方形为1的情况下尝试在1 * 1网格中绘制35张 map 。
假设您需要绘制64映射，那么我们将使用square = 8，然后输出将是一个8 * 8的网格。
关于python - 可视化特征图: IndexError: too many indices for array，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/63012225/