我有一个基于三个指标成功训练的神经网络。指标是：
- Hamming损失(模式＝"多标签"(
- F1得分(平均(微
- F1成绩(平均=无(
你可以在这个colab链接上查看我的应用程序的完整代码
简而言之，我的神经网络结构：
def create_fit_keras_model(hparams,
version_data_control,
optimizer_name,
validation_method,
callbacks,
optimizer_version = None):
sentenceLength_actors = X_train_seq_actors.shape[1]
vocab_size_frequent_words_actors = len(actors_tokenizer.word_index)
sentenceLength_plot = X_train_seq_plot.shape[1]
vocab_size_frequent_words_plot = len(plot_tokenizer.word_index)
sentenceLength_features = X_train_seq_features.shape[1]
vocab_size_frequent_words_features = len(features_tokenizer.word_index)
sentenceLength_reviews = X_train_seq_reviews.shape[1]
vocab_size_frequent_words_reviews = len(reviews_tokenizer.word_index)
sentenceLength_title = X_train_seq_title.shape[1]
vocab_size_frequent_words_title = len(title_tokenizer.word_index)
model = keras.Sequential(name='{0}_{1}dim_{2}batchsize_{3}lr_{4}decaymultiplier_{5}'.format(sequential_model_name,
str(hparams[HP_EMBEDDING_DIM]),
str(hparams[HP_HIDDEN_UNITS]),
str(hparams[HP_LEARNING_RATE]),
str(hparams[HP_DECAY_STEPS_MULTIPLIER]),
version_data_control))
actors = keras.Input(shape=(sentenceLength_actors,), name='actors_input')
plot = keras.Input(shape=(sentenceLength_plot,), name='plot_input')
features = keras.Input(shape=(sentenceLength_features,), name='features_input')
reviews = keras.Input(shape=(sentenceLength_reviews,), name='reviews_input')
title = keras.Input(shape=(sentenceLength_title,), name='title_input')
emb1 = layers.Embedding(input_dim = vocab_size_frequent_words_actors + 2,
output_dim = hparams[HP_EMBEDDING_DIM],
embeddings_initializer = 'uniform',
mask_zero = True,
input_length = sentenceLength_actors,
name="actors_embedding_layer")(actors)
# encoded_layer1 = layers.GlobalAveragePooling1D(name="globalaveragepooling_actors_layer")(emb1)
encoded_layer1 = layers.GlobalMaxPooling1D(name="globalmaxpooling_actors_layer")(emb1)
emb2 = layers.Embedding(input_dim = vocab_size_frequent_words_plot + 2,
output_dim = hparams[HP_EMBEDDING_DIM],
embeddings_initializer = 'uniform',
mask_zero = True,
input_length = sentenceLength_plot,
name="plot_embedding_layer")(plot)
# encoded_layer2 = layers.GlobalAveragePooling1D(name="globalaveragepooling_plot_summary_Layer")(emb2)
encoded_layer2 = layers.GlobalMaxPooling1D(name="globalmaxpooling_plot_summary_Layer")(emb2)
emb3 = layers.Embedding(input_dim = vocab_size_frequent_words_features + 2,
output_dim = hparams[HP_EMBEDDING_DIM],
embeddings_initializer = 'uniform',
mask_zero = True,
input_length = sentenceLength_features,
name="features_embedding_layer")(features)
# encoded_layer3 = layers.GlobalAveragePooling1D(name="globalaveragepooling_movie_features_layer")(emb3)
encoded_layer3 = layers.GlobalMaxPooling1D(name="globalmaxpooling_movie_features_layer")(emb3)
emb4 = layers.Embedding(input_dim = vocab_size_frequent_words_reviews + 2,
output_dim = hparams[HP_EMBEDDING_DIM],
embeddings_initializer = 'uniform',
mask_zero = True,
input_length = sentenceLength_reviews,
name="reviews_embedding_layer")(reviews)
# encoded_layer4 = layers.GlobalAveragePooling1D(name="globalaveragepooling_user_reviews_layer")(emb4)
encoded_layer4 = layers.GlobalMaxPooling1D(name="globalmaxpooling_user_reviews_layer")(emb4)
emb5 = layers.Embedding(input_dim = vocab_size_frequent_words_title + 2,
output_dim = hparams[HP_EMBEDDING_DIM],
embeddings_initializer = 'uniform',
mask_zero = True,
input_length = sentenceLength_title,
name="title_embedding_layer")(title)
# encoded_layer5 = layers.GlobalAveragePooling1D(name="globalaveragepooling_movie_title_layer")(emb5)
encoded_layer5 = layers.GlobalMaxPooling1D(name="globalmaxpooling_movie_title_layer")(emb5)
merged = layers.concatenate([encoded_layer1, encoded_layer2, encoded_layer3, encoded_layer4, encoded_layer5], axis=-1)
dense_layer_1 = layers.Dense(hparams[HP_HIDDEN_UNITS],
kernel_regularizer=regularizers.l2(neural_network_parameters['l2_regularization']),
activation=neural_network_parameters['dense_activation'],
name="1st_dense_hidden_layer_concatenated_inputs")(merged)
layers.Dropout(neural_network_parameters['dropout_rate'])(dense_layer_1)
output_layer = layers.Dense(neural_network_parameters['number_target_variables'],
activation=neural_network_parameters['output_activation'],
name='output_layer')(dense_layer_1)
model = keras.Model(inputs=[actors, plot, features, reviews, title], outputs=output_layer, name='{0}_{1}dim_{2}batchsize_{3}lr_{4}decaymultiplier_{5}'.format(sequential_model_name,
str(hparams[HP_EMBEDDING_DIM]),
str(hparams[HP_HIDDEN_UNITS]),
str(hparams[HP_LEARNING_RATE]),
str(hparams[HP_DECAY_STEPS_MULTIPLIER]),
version_data_control))
print(model.summary())
# pruning_schedule = tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.0,
# final_sparsity=0.4,
# begin_step=600,
# end_step=1000)
# model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(model, pruning_schedule=pruning_schedule)
if optimizer_name=="adam" and optimizer_version is None:
optimizer = optimizer_adam_v2(hparams[HP_LEARNING_RATE], hparams[HP_DECAY_STEPS_MULTIPLIER], X_train_seq_actors.shape[0], optimizer_parameters['validation_split_ratio'], hparams[HP_HIDDEN_UNITS])
elif optimizer_name=="sgd" and optimizer_version is None:
optimizer = optimizer_sgd_v1(hparams[HP_LEARNING_RATE])
elif optimizer_name=="rmsprop" and optimizer_version is None:
optimizer = optimizer_rmsprop_v1(hparams[HP_LEARNING_RATE])
model.compile(optimizer=optimizer,
loss=neural_network_parameters['model_loss'],
metrics=[tfa.metrics.HammingLoss(mode="multilabel", name="hamming_loss"),
tfa.metrics.F1Score(y_train[0].shape[-1], average="micro", name="f1_score_micro"),
tfa.metrics.F1Score(y_train[0].shape[-1], average=None, name="f1_score_none")], )
#plot model's structure
plot_model(model, to_file=os.path.join(os.getcwd(), '{0}/{1}_{2}batchsize_{3}lr_{4}decaymultiplier_{5}.png'.format(folder_path_model_saved,
network_structure_file_name,
str(hparams[HP_EMBEDDING_DIM]),
str(hparams[HP_HIDDEN_UNITS]),
str(hparams[HP_LEARNING_RATE]),
str(hparams[HP_DECAY_STEPS_MULTIPLIER]),
version_data_control)))
start_time = time.time()
steps_per_epoch=int(np.ceil((X_train_seq_actors.shape[0]*optimizer_parameters['validation_split_ratio'])//hparams[HP_HIDDEN_UNITS]))
print("nSteps per epoch on current run: {0}".format(steps_per_epoch))
if validation_method=="validation_split":
fitted_model=model.fit([X_train_seq_actors, X_train_seq_plot, X_train_seq_features, X_train_seq_reviews, X_train_seq_title],
y_train,
steps_per_epoch=int(np.ceil((X_train_seq_actors.shape[0]*optimizer_parameters['validation_split_ratio'])//hparams[HP_HIDDEN_UNITS])),
epochs=fit_parameters["epoch"],
batch_size=hparams[HP_HIDDEN_UNITS],
validation_split=fit_parameters['validation_data_ratio'],
callbacks=callbacks,
use_multiprocessing=True,
# sample_weight=class_weights_sample
)
elif validation_method=="validation_data":
fitted_model=model.fit([X_train_seq_actors, X_train_seq_plot, X_train_seq_features, X_train_seq_reviews, X_train_seq_title],
y_train,
steps_per_epoch=int(np.ceil((X_train_seq_actors.shape[0]*optimizer_parameters['validation_split_ratio'])//hparams[HP_HIDDEN_UNITS])),
epochs=fit_parameters["epoch"],
verbose=fit_parameters["verbose_fit"],
batch_size=hparams[HP_HIDDEN_UNITS],
validation_data=([X_test_seq_actors, X_test_seq_plot, X_test_seq_features, X_test_seq_reviews, X_test_seq_title],
y_test),
callbacks=callbacks,
)
#save the model
save_model(model,
folder_path_model_saved,
"{0}_{1}dim_{2}batchsize_{3}lr_{4}decaymultiplier_{5}".format(saved_model_name,
str(hparams[HP_EMBEDDING_DIM]),
str(hparams[HP_HIDDEN_UNITS]),
str(hparams[HP_LEARNING_RATE]),
str(hparams[HP_DECAY_STEPS_MULTIPLIER]),
version_data_control))
elapsed_time = time.time() - start_time
print("nTraining time of the multi-input keras model has finished. Duration {} secs".format(format_timespan(elapsed_time)))
# I get an error here!
_, model_metric = model.evaluate([X_test_seq_actors, X_test_seq_plot, X_test_seq_features, X_test_seq_reviews, X_test_seq_title], y_test, batch_size=hparams[HP_HIDDEN_UNITS], verbose=2)
print('Results of model_metrics on test data: {0}'.format(model_metric))
return model_metric, model, fitted_model
在最后一步，我调用model.eevaluate((，得到以下错误：python ValueError: too many values to unpack (expected 2)
我想这是因为我同时使用了3个指标。如何解决它并返回列表或字典中所有度量的值？
model.evaluate分别返回损失和度量的列表
在您的情况下，您有一个输出和3个度量，因此model.evaluate返回此序列中的4个数字的列表[loss，metric1，metric2，metric3]
您只需以这种方式覆盖问题：
evalution = model.evaluate([X_test_seq_actors, X_test_seq_plot, X_test_seq_features, X_test_seq_reviews, X_test_seq_title],
y_test, batch_size=hparams[HP_HIDDEN_UNITS], verbose=2)
loss = evalution[0] # single number
model_metric = evalution[1:] # is a list of 3 elements