[已解决]cmd运行python代码报错

lym20191688

在运行python代码是报错__init__( ) missing 1 required positional argument :'units'
代码如下，请各位鱼友们帮我看看哪里出问题#coding=utf-8
import numpy as np
from pandas import DataFrame, read_csv
import matplotlib.pyplot as plt
#from keras.utils.visualize_util import plot
import DNN

def select_input_datasets(data, y, x,step):
    inputs = data[x].values.tolist()
    new_input_sets = []   
    for i in range(len(inputs)):
        x = [] #  2D
        for j in range(step-1,-1,-1):
            if (i-j) < 0:
                x.append([0]*len(inputs[0]))
            else:
                x.append(inputs[i-j])
        new_input_sets.append(x)
    #print np.array(new_input_sets).shape
    return {'y':np.array(data[y].values),'x':np.array(new_input_sets)}

def read_data(train_file_name):   
    print ("The train data is loading")
    return read_csv(train_file_name)

def training(train_data_name=None, time_step=None, x_variables=None, target=None,
             dim_hidden_layer=None, lr=None, loss=None, batch_size=None, nb_epoch=None, validation_split=None,
             best_model_weight_name=None, last_model_weight_name=None):
    original_train_data = read_data(train_data_name)
    train_input = select_input_datasets(original_train_data,
                            y=target,
                            x=x_variables,
                            step=time_step)
    DNN_model = DNN.DNN(train_input)   
    # build a DNN model
    DNN_model.build_model(dim_hidden_layer=dim_hidden_layer, lr=lr, loss=loss)
    DNN_model.shows_model()
    # fit the model according to training data
    DNN_model.fit_model(batch_size=batch_size, nb_epoch=nb_epoch, validation_split=validation_split, best_model_weight_name=best_model_weight_name)
    DNN_model.save_model_to_file(last_model_weight_name)

    DNN_model.predict(train_input, show=True)
    DNN_model.evaluate(train_input)
    return DNN_model

def testing(test_data_name=None, time_step=None, x_variables=None, target=None,
            restore_model_weight_name=None):
    original_test_data = read_data(test_data_name)

    testing_input = select_input_datasets(original_test_data,
                        y=target,
                        x=x_variables,
                        step=time_step)
    testing_input['x'] = testing_input['x'][0:]
    testing_input['y'] = testing_input['y'][0:]
    DNN_model = DNN.DNN(testing_input)
    DNN_model.restore_model_from_file(restore_model_weight_name)

    DNN_model.predict(testing_input, show=True)
    DNN_model.evaluate(testing_input)
    return DNN_model


if __name__ == "__main__":
    #**************************************************
    # training or testing
    only_test = False
    a='0'
    # 'Best_model.weights' or 'last_model'
    restore_model_weight_name = 'C:\\Users\\Iron\\Desktop\\0\\best_model.weights'

    # data
    train_data_name = 'C:\\Users\\Iron\\Desktop\\0\\train_data.csv'
    test_data_name = 'C:\\Users\\Iron\\Desktop\\0\\test_data.csv'
    target = 'w'
    x_variables = [

        "a1","a2","a3","a4",
                "a6","a6","a7","a8",
                "a9","a10"

                      ]
    # model param
    time_step = 2
    dim_hidden_layer = 64
    lr = 0.001
    loss = 'mape'

    # fit param
    batch_size = 2
    nb_epoch = 15000
    validation_split = 0.05

    # save
    best_model_weight_name = 'C:\\Users\\Iron\\Desktop\\0\\best_model.weights'
    last_model_weight_name = 'C:\\Users\\Iron\\Desktop\\0\\last_model.weights'
    #**************************************************


    if not only_test:

        model = training(train_data_name=train_data_name,
                     time_step=time_step,
                     x_variables=x_variables, target=target,
                     dim_hidden_layer=dim_hidden_layer, lr=lr, loss=loss,
                     batch_size=batch_size, nb_epoch=nb_epoch, validation_split=validation_split,
                     best_model_weight_name=best_model_weight_name,
                     last_model_weight_name=last_model_weight_name)

    if only_test:
        model = testing(test_data_name=test_data_name,
                    time_step=time_step,
                    x_variables=x_variables, target=target,
                    restore_model_weight_name=restore_model_weight_name)

最佳答案

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Twilight6

2020-8-1 09:14:31

__init__( ) missing 1 required positional argument :'units'

这个报错就是缺少需要传入的参数，你自己检查下代码吧

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Twilight6

__init__( ) missing 1 required positional argument :'units'

这个报错就是缺少需要传入的参数，你自己检查下代码吧