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tensorflow - 在 tensorflow 的推理过程中如何获得神经元的激活?

转载 作者:行者123 更新时间:2023-12-05 02:44:06 24 4
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我想具体知道如何让神经网络中的神经元被激活(激活函数后每个神经元的输出)

当我在 Tensorflow 2 中的模型推理过程中提供输入时,如何获得顺序模型的所有神经元的激活值?

最佳答案

尝试这样的事情:

intermediate_output = tf.keras.Model(model.input, 
model.get_layer('conv2_block1_3_conv').output)

您可以使用 model.layers 获取图层列表,然后选择您想要的图层。

list(map(lambda x: x.name, model.layers))
['input_2',
'conv1_pad',
'conv1_conv',
'conv1_bn',
'conv1_relu',
'pool1_pad',
'pool1_pool',
'conv2_block1_1_conv',
'conv2_block1_1_bn',
'conv2_block1_1_relu',
'conv2_block1_2_conv',
'conv2_block1_2_bn',
'conv2_block1_2_relu',
'conv2_block1_0_conv',
'conv2_block1_3_conv', ...

完整示例:

import tensorflow as tf
from skimage import data
import matplotlib.pyplot as plt

model = tf.keras.applications.resnet.ResNet50(
weights='imagenet', include_top=True)

model.build(input_shape=(None, 224, 224, 3))

image = tf.image.resize(data.chelsea(), (224, 224))/255

enter image description here

intermediate_output = tf.keras.Model(model.input, 
model.get_layer('conv2_block1_3_conv').output)

extracted = intermediate_output(image[None, ...])
<tf.Tensor: shape=(1, 56, 56, 256), dtype=float32, numpy=
array([[[[ 0.23296243, -0.19640587, -0.8846314 , ..., -0.3091477 ,
-0.51000404, -0.00218517],
[ 0.27896926, -0.22646117, -0.91138077, ..., -0.4151363 ,
-0.73324907, 0.05706196],
[ 0.27908558, -0.2267507 , -0.9121696 , ..., -0.41521263,
-0.73362166, 0.05721636],
[ 0.04438811, 0.49058744, -1.5047315 , ..., -0.15204512,
-1.2029954 , -0.29269713],
[ 0.04450325, 0.4905177 , -1.505692 , ..., -0.15128748,
-1.2025517 , -0.29254213],
[ 0.05638191, 0.22808033, -1.5240382 , ..., 0.0052015 ,
-0.8789809 , -0.19639899]]]], dtype=float32)>

关于tensorflow - 在 tensorflow 的推理过程中如何获得神经元的激活?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/66571756/

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