我在 Keras 中成功训练并保存了双向 LSTM 模型:
model = Sequential()
model.add(Bidirectional(LSTM(N_HIDDEN_NEURONS,
return_sequences=True,
activation="tanh",
input_shape=(SEGMENT_TIME_SIZE, N_FEATURES))))
model.add(Bidirectional(LSTM(N_HIDDEN_NEURONS)))
model.add(Dropout(0.5))
model.add(Dense(N_CLASSES, activation='sigmoid'))
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train,
batch_size=BATCH_SIZE,
epochs=N_EPOCHS,
validation_data=[X_test, y_test])
model.save('model_keras/model.h5')
但是,当我想加载它时:
model = load_model('model_keras/model.h5')
我收到错误:
ValueError: You are trying to load a weight file containing 3 layers into a model with 0 layers.
我还尝试了不同的方法,例如分别保存和加载模型架构和权重,但它们都不适合我。另外,之前,当我使用普通(单向)LSTM 时,加载模型效果很好。
最佳答案
正如@mpariente所述和 @today ,input_shape
是双向的参数,而不是 LSTM,请参阅 Keras documentation 。我的解决方案:
# Model
model = Sequential()
model.add(Bidirectional(LSTM(N_HIDDEN_NEURONS,
return_sequences=True,
activation="tanh"),
input_shape=(SEGMENT_TIME_SIZE, N_FEATURES)))
model.add(Bidirectional(LSTM(N_HIDDEN_NEURONS)))
model.add(Dropout(0.5))
model.add(Dense(N_CLASSES, activation='sigmoid'))
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train,
batch_size=BATCH_SIZE,
epochs=N_EPOCHS,
validation_data=[X_test, y_test])
model.save('model_keras/model.h5')
然后,要加载,只需执行以下操作:
model = load_model('model_keras/model.h5')
关于python - 在 Keras 中加载保存的模型(双向 LSTM),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/51236338/