Tensorflow 如何从Keras模型的张量中获得激活值？

我试图从层中的节点访问激活值

l0_out = model.layers[0].output

print(l0_out)
print(type(l0_out))

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尝试使用

K.function

并将一批

train_x

注入函数

from keras import backend as K

get_relu_output = K.function([model.layers[0].input], [model.layers[0].output])
relu_output = get_relu_output([train_x])

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那么，您的输入是什么？如果您犯了我们不知道的错误，您必须向我们展示您尝试过的所有内容。您可以将（10,4）数据输入此函数。@HashRocketSyntax您必须制作一个示例来说明问题，您所说的毫无意义，批处理维度不应该更改。这很尴尬，我看着

train_x

的形状，同时预测

test_x

，想知道为什么我的值比预期的要多。

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

iris_data = load_iris()

x = iris_data.data
y_ = iris_data.target.reshape(-1, 1)

encoder = OneHotEncoder(sparse=False)
y = encoder.fit_transform(y_)

train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.20)


model = Sequential()
model.add(Dense(10, input_shape=(4,), activation='relu', name='fc1'))
model.add(Dense(10, activation='relu', name='fc2'))
model.add(Dense(3, activation='softmax', name='output'))

model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

print(model.summary())

# Train
model.fit(train_x, train_y, verbose=2, batch_size=5, epochs=200)

from keras import backend as K

get_relu_output = K.function([model.layers[0].input], [model.layers[0].output])
relu_output = get_relu_output([train_x])