Question

我正在Keras中实现双向LSTM。在训练过程中，训练准确性和验证准确性均为0.83，损失为0.45。

Epoch 1/50
32000/32000 [==============================] - 597s 19ms/step - loss: 0.4611 - accuracy: 0.8285 - val_loss: 0.4515 - val_accuracy: 0.8316
Epoch 2/50
32000/32000 [==============================] - 589s 18ms/step - loss: 0.4563 - accuracy: 0.8299 - val_loss: 0.4514 - val_accuracy: 0.8320
Epoch 3/50
32000/32000 [==============================] - 584s 18ms/step - loss: 0.4561 - accuracy: 0.8299 - val_loss: 0.4513 - val_accuracy: 0.8318
Epoch 4/50
32000/32000 [==============================] - 612s 19ms/step - loss: 0.4560 - accuracy: 0.8300 - val_loss: 0.4513 - val_accuracy: 0.8319
Epoch 5/50
32000/32000 [==============================] - 572s 18ms/step - loss: 0.4559 - accuracy: 0.8299 - val_loss: 0.4512 - val_accuracy: 0.8318

这是我的模特

model = tf.keras.Sequential()
model.add(Masking(mask_value=0., input_shape=(timesteps, features)))
model.add(Bidirectional(LSTM(units=100, return_sequences=True), input_shape=(timesteps, features)))
model.add(Dropout(0.7))
model.add(Dense(1, activation='sigmoid'))

我通过scikit-learn StandardScaler对数据集进行了归一化。

我有一个自定义损失：

def get_top_one_probability(vector):
  return (K.exp(vector) / K.sum(K.exp(vector)))

def listnet_loss(real_labels, predicted_labels):
  return -K.sum(get_top_one_probability(real_labels) * tf.math.log(get_top_one_probability(predicted_labels)))

这些是model.compile和model.fit设置：

model.compile(loss=listnet_loss, optimizer=keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics=["accuracy"])

model.fit(training_dataset, training_dataset_labels, validation_split=0.2, batch_size=1, 
            epochs=number_of_epochs, workers=10, verbose=1, 
            callbacks=[SaveModelCallback(), keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)])

这是我的测试阶段：

scaler = StandardScaler()
scaler.fit(test_dataset)
test_dataset = scaler.transform(test_dataset)

test_dataset = test_dataset.reshape((int(test_dataset.shape[0]/20), 20, test_dataset.shape[1]))

# Read model
json_model_file = open('/content/drive/My Drive/Tesi_magistrale/LSTM/models_padded_2/model_11.json', 'r')
loaded_model_json = json_model_file.read()
json_model_file.close()
model = model_from_json(loaded_model_json)
model.load_weights("/content/drive/My Drive/Tesi_magistrale/LSTM/models_weights_padded_2/model_11_weights.h5")

with open("/content/drive/My Drive/Tesi_magistrale/LSTM/predictions/padded/en_ewt-padded.H.pred", "w+") as predictions_file:
  predictions = model.predict(test_dataset)

我也重新缩放了测试仪。在predictions = model.predict(test_dataset)行之后，我放置了一些业务逻辑来处理我的预测（在培训阶段也使用了该逻辑）。

如果训练的结果很好，我在测试仪上也会得到非常差的结果。我做错了什么？

Keras：训练和验证准确性高，但预测不佳

0 个答案: