mlp.py 文件源码

def mlp(X, Y, para):

    if para['w_regularizer'] is 'auto':
        para['w_regularizer'] = [para['layers']]

    l1, l2 = check_w_reg(0, para['w_regularizer'], para['w_reg_values'])

    model = Sequential()
    model.add(Dense(para['neuron_count'][0],
                    input_dim=para['dims'],
                    activation=para['activation'],
                    W_regularizer=l1_l2(l1=l1, l2=l2)))
    model.add(Dropout(para['dropout']))

    j = 1

    for i in range(para['layers'] - 1):

        l1, l2 = check_w_reg(j, para['w_regularizer'], para['w_reg_values'])

        model.add(Dense(para['neuron_count'][i+1], 
                        activation=para['activation'],
                        W_regularizer=l1_l2(l1=l1, l2=l2)))
        model.add(Dropout(para['dropout']))

        j += 1

    l1, l2 = check_w_reg(para['layers'], para['w_regularizer'], para['w_reg_values'])

    model.add(Dense(para['neuron_last'], 
                    activation=para['activation_out'],
                    W_regularizer=l1_l2(l1=l1, l2=l2)))
    model.compile(loss=para['loss'],
                  optimizer=para['optimizer'],
                  metrics=['accuracy'])

    if para['verbose'] >= 1:
        time.sleep(0.1)

    out = model.fit(X, Y, validation_split=para['validation_split'],
                    epochs=para['epoch'],
                    verbose=para['verbose'],
                    batch_size=para['batch_size'])

    return model, out