我做了什么:
我使用 Keras 实现了以下模型:
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]))
inp = Input((train_X.shape[1], train_X.shape[2]))
lstm = LSTM(1, return_sequences=False)(inp)
output = Dense(train_Y.shape[1], activation='softmax')(lstm)
model = Model(inputs=inp, outputs=output)
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.fit(train_X, train_Y, validation_split=.20, epochs=2, batch_size=50)
我想要的:
我想将 SVM 添加到我模型的最后一层,但我不知道该怎么做?有什么想法吗?
最佳答案
这应该适用于添加 svm 作为最后一层。
inp = Input((train_X.shape[1], train_X.shape[2]))
lstm = LSTM(1, return_sequences=False)(inp)
output = Dense(train_Y.shape[1], activation='softmax', W_regularizer=l2(0.01)))(lstm)
model = Model(inputs=inp, outputs=output)
model.compile(loss='hinge', optimizer='adam', metrics=['accuracy'])
model.fit(train_X, train_Y, validation_split=.20, epochs=2, batch_size=50)
这里我使用hinge
作为考虑二元分类目标的损失。但如果不止于此,那么你可以考虑使用categorical_hinge
关于python - 将 SVM 添加到最后一层,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/53019301/