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我收到以下错误，我无法弄清楚原因：

RuntimeError：模型构建函数未返回有效的 Keras 模型实例，发现（<tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b849d0>, <tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b80810> )

我已经阅读了here和here的答案，这些答案似乎告诉我要从中导入keras而tensorflow不是独立keras我正在做的事情，但仍然出现错误。我非常感谢您帮助解决这个问题。以下是我的完整代码：

from tensorflow.keras.layers import Input, Dense, BatchNormalization, Dropout, Concatenate, Lambda, GaussianNoise, Activation
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.losses import BinaryCrossentropy
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from numba import njit
import tensorflow as tf
import numpy as np
from sklearn.model_selection import KFold
from sklearn.model_selection._split import _BaseKFold, indexable, _num_samples
from sklearn.utils.validation import _deprecate_positional_args
import pandas as pd
import kerastuner as kt
import gc
from tqdm import tqdm
from random import choices
import warnings
warnings.filterwarnings('ignore')

class MyTuner(kt.Tuner):
    def run_trial(self, trial, x, y):
        cv = PurgedGroupTimeSeriesSplit(n_splits=5, group_gap = 20)
        val_losses = []
        
        for train_indices, test_indices in cv.split(x, groups=x[0]):
            x_train, y_train = x[train_indices, 1:], y[train_indices]
            x_test, y_test = x[test_indices, 1:], y[test_indices]
            
            x_train = apply_transformation(x_train)
            x_test = apply_transformation(x_test)
            
            model = self.hypermodel.build(trial.hyperparameters)
            model.fit(x_train, y_train, batch_size = hp.Int('batch_size', 500, 5000, step=500, default=4000),
                      epochs = hp.Int('epochs', 100, 1000, step=200, default=500))
            
            val_losses.append(model.evaluate(x_test, y_test))
            
        self.oracle.update_trial(trial.trial_id, {'val_loss': np.mean(val_losses)})
        self.save_model(trial.trial_id, model)

def create_autoencoder(hp, input_dim, output_dim):
    i = Input(input_dim)
    encoded = BatchNormalization()(i)
    encoded = GaussianNoise(hp.Float('gaussian_noise', 1e-2, 1, sampling='log', default=5e-2))(encoded)
    encoded = Dense(hp.Int('encoder_dense', 100, 300, step=50, default=64), activation='relu')(encoded)
    decoded = Dropout(hp.Float('decoder_dropout_1', 1e-1, 1, sampling='log', default=0.2))(encoded)
    decoded = Dense(input_dim,name='decoded')(decoded)
    x = Dense(hp.Int('output_x', 32, 100, step=10, default=32),activation='relu')(decoded)
    x = BatchNormalization()(x)
    x = Dropout(hp.Float('x_dropout_1', 1e-1, 1, sampling='log', default=0.2))(x)
    x = Dense(hp.Int('output_x', 32, 100, step=10, default=32),activation='relu')(x)
    x = BatchNormalization()(x)
    x = Dropout(hp.Float('x_dropout_2', 1e-1, 1, sampling='log', default=0.2))(x)    
    x = Dense(output_dim,activation='sigmoid',name='label_output')(x)
    
    encoder = Model(inputs=i,outputs=encoded)
    autoencoder = Model(inputs=i,outputs=[decoded, x])
    
#     optimizer = hp.Choice('optimizer', ['adam', 'sgd'])
    
    autoencoder.compile(optimizer=Adam(hp.Float('lr', 0.00001, 0.1, default=0.001)), 
                        loss='sparse_binary_crossentropy',
                        metrics=['accuracy'])
    
    return autoencoder, encoder


build_model = lambda hp: create_autoencoder(hp, X[:, 1:].shape[1], y.shape[1])

tuner = MyTuner(
            oracle=kt.oracles.BayesianOptimization(
                    objective=kt.Objective('val_loss', 'min'),
                    max_trials=20),
            hypermodel=build_model,
            directory='./',
            project_name='autoencoders')
    
tuner.search(X, (X,y), callbacks=[EarlyStopping('val_loss',patience=5),
                                  ReduceLROnPlateau('val_loss',patience=3)])

encoder_hp  = tuner.get_best_hyperparameters(1)[0]
print("Best Encoder Hyper-parameter:", encoder_hp)

best_autoencoder = tuner.get_best_models(1)[0]
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1 回答 1

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RuntimeError：模型构建函数未返回有效的 Keras 模型实例，发现（<tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b849d0>, <tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b80810> )

(<tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b849d0>, <tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b80810>)

如您所见，这是一个包含两个 Keras 模型实例的元组。这是 的输出create_autoencoder(hp, input_dim, output_dim)。

def create_autoencoder(hp, input_dim, output_dim): 
    # some lines of codes
    return autoencoder, encoder

据我了解，您没有使用encoder. 因此，您可以在函数中将其删除。

该功能将如下所示

def create_autoencoder(hp, input_dim, output_dim): 
    # some lines of codes
    return autoencoder

它只会返回一个Keras 模型实例。

于 2021-02-21T09:13:45.213 回答