我有一个基于Keras的简单LSTM模型.
X_train, X_test, Y_train, Y_test = train_test_split(input, labels, test_size=0.2, random_state=i*10)
X_train = X_train.reshape(80,112,12)
X_test = X_test.reshape(20,112,12)
y_train = np.zeros((80,112),dtype='int')
y_test = np.zeros((20,112),dtype='int')
y_train = np.repeat(Y_train,112, axis=1)
y_test = np.repeat(Y_test,112, axis=1)
np.random.seed(1)
# create the model
model = Sequential()
batch_size = 20
model.add(BatchNormalization(input_shape=(112,12), mode = 0, axis = 2))#4
model.add(LSTM(100, return_sequences=False, input_shape=(112,12))) #7
model.add(Dense(112, activation='hard_sigmoid'))#9
model.compile(loss='binary_crossentropy', optimizer='RMSprop', metrics=['binary_accuracy'])#9
model.fit(X_train, y_train, nb_epoch=30)#9
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, batch_size = batch_size, verbose=0)
我知道如何获得重量列表model.get_weights(),但这是模型完全训练后的值.我希望在每个时代得到权重矩阵(例如,我的LSTM中的最后一层),而不仅仅是它的最终值.换句话说,我有30个时代,我需要获得30个权重矩阵值.
真的,谢谢你,我没有在keras的维基上找到解决方案.