Kerastuner Randomsearch:TypeError:（“关键字参数未理解：”，“激活”）

Question

使用GoogleColab，我试图使用Kerastuner的随机搜索来为我的用例找到最好的CNN

在我看来，一切都应该安排妥当，但出于某种原因，我总是得到一些帮助

TypeError: ('Keyword argument not understood:', 'activation')

每当我宣布我的搜索

用于声明我的模型的函数：

from tensorflow.keras import datasets, layers, models

def model_declaration(hp):
  cnn = models.Sequential([
    # Filtering & Pooling Layers
    layers.Conv2D(
        filters=hp.Int('filter1', min_value = 16, max_value = 128, step = 16), # Optimizing with filters from 16 to 128 in steps of 16 
        kernel_size = hp.Choice('kernel1', values=[3,6]), # Optimizing kernel size from 3 to 6
        activation ='relu',
        input_shape = (48,48,1) # always the same
        ),
    layers.MaxPooling2D(pool_size=hp.Int('max_pooling_1', min_value = 2, max_value = 4, step = 16), activation = 'relu'),
    layers.Conv2D( 
        filters=hp.Int('filter2', min_value = 16, max_value = 128, step = 16 ), # Optimizing with filters from 16 to 128 in steps of 16 
        kernel_size = hp.Choice('kernel2', values=[3,6]), # Optimizing kernel size from 3 to 6
        activation = 'relu'),
    layers.Conv2D( 
        filters=hp.Int('filter3', min_value = 8, max_value = 256, step = 16 ), # Optimizing with filters from 16 to 128 in steps of 16 
        kernel_size = hp.Choice('kernel3', values=[3,6]), # Optimizing kernel size from 3 to 6
        activation = 'relu'
        ),
    layers.Flatten(), # Flattening
  ])

  for i in range(hp.Int('dense_layers', 2, 10)): 
      cnn.add(layers.Dense(units=hp.Int('dense_parameters'), min_value = 16, max_value = 128, step = 16), activation=hp.Choice(['relu', 'tanh', 'sigmoid']))

  model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-1, 1e-2, 1e-3, 1e-4])),
                loss = 'sparse_categorical_crossentropy',
                metrics = ['accuracy'])
  return model

这是我对随机搜索的声明：

import kerastuner
from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameter
random_search = RandomSearch(model_declaration, objective='val_accuracy', max_trials=5, directory='output', project_name='CNN best output')

Tensorflow版本为2.2.0-rc3 Kerastuner版本为1.0.1 Keras版本为2.3.0-tf

提前感谢你的帮助，我真的很难做到这一点，因为我对这个主题还比较陌生

Answer 1

MaxPooling2D层没有activation参数。检查Keras Documentation也请参见图层规范

Answer 2

MaxPoolig2D没有激活。因为MaxPooling是一个最小化数据的层。它没有激活，因为它遵循特定的算法，该算法接受池大小，并为您选择的每个区域获取最大值