如果你有一些数据d，你通过做（类似于）

min_d = np.min(d)
max_d = np.max(d)
normalized_d = (d - min_d) / (max_d - min_d)

你可以通过反转归一化来反归一化。在这种情况下

denormalized_d = normalized_d * (max_d - min_d) + min_d

此外，由于问题被标记为keras，如果您要使用其内置的规范化层规范化数据，则还可以使用规范化层对其进行反规范化。
您需要将反转参数设置为True，并使用原始图层的均值和方差，或使其适应相同的数据。

# Create a variable for demonstration purposes
test_var = pd.Series([2.5, 4.5, 17.5, 10.5], name='test_var')

#Create a normalization layer and adapt it to the data
normalizer_layer = tf.keras.layers.Normalization(axis=-1)
normalizer_layer.adapt(test_var)

#Create a denormalization layer using the mean and variance from the original layer
denormalizer_layer = tf.keras.layers.Normalization(axis=-1, mean=normalizer_layer.mean, variance=normalizer_layer.variance, invert=True)

#Or create a denormalization layer and adapt it to the same data
#denormalizer_layer = tf.keras.layers.Normalization(invert=True)
#denormalizer_layer.adapt(test_var)

#Normalize and denormalize the example variable
normalized_data = normalizer_layer(test_var)
denormalized_data = denormalizer_layer(normalized_data)

#Show the results
print("test_var")
print(test_var)

print("normalized test_var")
print(normalized_data)

print("denormalized test_var")
print(denormalized_data)

查看更多：https://www.tensorflow.org/api_docs/python/tf/keras/layers/Normalization