Я получаю следующую ошибку, и я не могу понять, почему:
Ошибка выполнения: функция построения модели не вернула допустимый экземпляр модели Keras, найденный (объект tensorflow.python.keras.engine.functional.Functional по адресу 0x7f74d8b849d0›, объект ‹tensorflow.python.keras.engine.functional.Functional по адресу 0x7f74d8b80810› )
Я прочитал ответы здесь и здесь, которые, кажется, говорят об импорте keras из tensorflow вместо отдельного keras, что я делаю, но все еще получаю сообщение об ошибке. Я был бы очень признателен за вашу помощь в выяснении этого. Ниже приведен весь мой код:
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]
