在本教程之后，我将尝试可视化特征图。
我的模型如下：
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
我现在想可视化索引为10:10 branch_0_output (None, 35)的层
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)
我遵循了教程中所述的代码，对图像进行了预处理，现在我想绘制这一层的35(？(个特征图：我使用了教程中的代码，并修改了平方数，这里是1，但我尝试了几个：
# 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()
独立于我尝试的号码，我得到了这个错误消息：
---------------------------------------------------------------------------
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
有人能帮我修改一下吗？
非常感谢！
错误在第11行显示too many indices for array。之所以会发生这种情况，是因为在要素图中传递索引不正确。在这里，你试图在一个1*1的网格中绘制35张地图，因为你已经给出了平方=1。
假设您需要绘制64映射，那么我们将使用square = 8，那么输出将是8*8网格。