我做了什么？我實現了一個keras模型如下：train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.2, random_state=np.random.seed(7), shuffle=True)train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))model = Sequential()model.add(LSTM(100, return_sequences=False, input_shape=(train_X.shape[1], train_X.shape[2])))model.add(Dense(train_Y.shape[1], activation='softmax'))model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])model.fit(train_X, train_Y, validation_split=.20, epochs=1000, batch_size=50)我想要的是？我想給出support vector machine(SVM)倒數第二層 (LSTM) 的輸出，在任何epoch（即 1000）層中svm也要進行訓練。但我不知道如何做到這一點？任何的想法？更新：我使用ModelCheckpoint如下：model = Sequential()model.add(LSTM(100, return_sequences=False, input_shape=(train_X.shape[1], train_X.shape[2])))model.add(Dense(train_Y.shape[1], activation='softmax'))model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])# checkpointfilepath="weights-{epoch:02d}-{val_acc:.2f}.hdf5"checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')callbacks_list = [checkpoint]model.fit(train_X, train_Y, validation_split=.20, epochs=1000, batch_size=50, callbacks=callbacks_list, verbose=0)輸出：Epoch 00991: val_acc did not improveEpoch 00992: val_acc improved from 0.93465 to 0.93900, saving model to weights-992-0.94.hdf5Epoch 00993: val_acc did not improveEpoch 00994: val_acc did not improveEpoch 00995: val_acc did not improveEpoch 00996: val_acc did not improveEpoch 00997: val_acc did not improveEpoch 00998: val_acc improved from 0.93900 to 0.94543, saving model to weights-998-0.94.hdf5Epoch 00999: val_acc did not improve問題：如@IonicSolutions 所說，如何加載所有這些模型以獲得每個時期中 LSTM 層的輸出？
Keras，在每個時期獲得一層的輸出
慕無忌1623718
2021-07-23 18:06:26
