models.py 文件源码

def cnn3adam_slim(input_shape, n_classes):
    """
    Input size should be [batch, 1d, 2d, ch] = (None, 3000, 3)
    """
    model = Sequential(name='cnn3adam')
    model.add(Conv1D (kernel_size = (50), filters = 32, strides=5, input_shape=input_shape, kernel_initializer='he_normal', activation='elu')) 
    model.add(BatchNormalization())
    model.add(Dropout(0.2))

    model.add(Conv1D (kernel_size = (5), filters = 64, strides=1, kernel_initializer='he_normal', activation='elu')) 
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(MaxPooling1D())
    model.add(Conv1D (kernel_size = (5), filters = 64, strides=2, kernel_initializer='he_normal', activation='elu')) 
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(MaxPooling1D())
    model.add(Flatten())
    model.add(Dense (250, activation='elu', kernel_initializer='he_normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    model.add(Dense (250, activation='elu', kernel_initializer='he_normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    model.add(Dense(n_classes, activation = 'softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=Adam())
    return model