Keras-Tuner 运行时错误

Question

我收到以下错误，我无法弄清楚原因：

RuntimeError: Model-building function did not return a valid Keras Model instance, found (<tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b849d0>, <tensorflow.python.keras.engine.functional.Functional object at 0x7f74d8b80810>)

我已经阅读了答案 and here 似乎告诉我从 tensorflow 导入 keras 而不是我正在做的独立 keras 但仍然得到错误。非常感谢您帮助解决这个问题。以下是我的全部代码：

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]

Answer 1

RuntimeError: Model-building function did not return a valid Keras Model instance, found (<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，这是两个 Keras 模型实例的元组。这是 create_autoencoder(hp, input_dim, output_dim).

的输出

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。

该函数将如下所示

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

它只会return一个个Keras模型实例。

Keras-Tuner 运行时错误

Keras-Tuner RuntimeError

python-3.x

tensorflow

keras

tf.keras

keras-tuner