lstmclassification.py 文件源码

def build_model():
    model = Sequential()

    # model.add(Convolution1D(16, 2, border_mode='valid', input_shape=(20, 1)))
    # model.add(Activation('relu'))

    # model.add(Convolution1D(32, 3, border_mode='valid'))
    # model.add(Activation('relu'))

    # model.add(Convolution1D(32, 2, border_mode='valid'))
    # model.add(Activation('relu'))
    # model.add(MaxPooling1D(pool_length=2))

    # model.add(Flatten())
    # model.add(Dense(32))
    # model.add(Activation('relu'))

    # model.add(Reshape((32, 1)))
    model.add(LSTM(input_dim=1, output_dim=16, activation='relu', return_sequences=True))
    model.add(Dropout(0.2))  # Dropout overfitting

    model.add(LSTM(32, activation='relu', return_sequences=False))
    model.add(Dropout(0.2))  # Dropout overfitting

    # model.add(Dense(64))
    # model.add(Activation("relu"))
    # model.add(Dropout(0.2))  # Dropout overfitting

    model.add(Dense(64))
    model.add(Activation("softmax"))

    start = time.time()
    # sgd = SGD(lr=0.5, decay=1e-6, momentum=0.9, nesterov=True)
    # model.compile(loss="mse", optimizer=sgd)
    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])  # Nadam RMSprop()
    print "Compilation Time : ", time.time() - start
    return model