AttributeError: 'str' 对象没有属性 'ndim' [Python |凯拉斯]

如何解决AttributeError: 'str' 对象没有属性 'ndim' [Python |凯拉斯]

以下是我的代码，与此处找到的代码大致相同：https://keras.io/examples/generative/lstm_character_level_text_generation/

它已经在所有时期工作了一天，但是，今天它运行但在随机时期出现错误并出现 AttributeError 错误，表示字符串没有 ndim 属性，因为输入和转换的数据没有意义从第 51-56 行进入一个 numpy 数组与之前工作时相同，那么它如何将此数据更改为字符串？以及在没有篡改输入数据或用于接收数据的代码的情况下，这一天是如何变化的。

def load_file(self,filename):
    file = open(filename,'r')
    content = file.read()
    file.close()
    return content

def sample(self,preds,temperature=1.0):
    preds = np.asarray(preds).astype("float64")
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probas = np.random.multinomial(1,1)
    return np.argmax(probas)
    

def train(self,epochs,batch_size):
    content = self.load_file("data/ABC_cleaned/input.txt")

    chars = sorted(list(set(content)))
    char_indices = dict((c,i) for i,c in enumerate(chars))
    indices_char = dict((i,c) for i,c in enumerate(chars))

    maxlen = 40
    step = 3
    sentences = []
    next_chars = []
    for i in range(0,len(content) - maxlen,step):
        sentences.append(content[i:i+maxlen])
        next_chars.append(content[i+maxlen])

    x = np.zeros((len(sentences),maxlen,len(chars)),dtype=np.bool)
    y = np.zeros((len(sentences),dtype=np.bool)
    for i,sentence in enumerate(sentences):
        for t,char in enumerate(sentence):
            x[i,t,char_indices[char]] = 1
        y[i,char_indices[next_chars[i]]] = 1
    print(type(x))
    model = keras.Sequential()
    model.add(input_layer.InputLayer(input_shape=(maxlen,len(chars))))
    model.add(layers.LSTM(128))
    model.add(layers.Dense(len(chars),activation='softmax'))
    optimizer = optimizers.RMSprop(lr=0.01)
    model.compile(loss="categorical_crossentropy",optimizer=optimizer)
    for epoch in range(epochs):
        model.fit(x,y,batch_size=batch_size,epochs=1)
        print()
        print("Generating text after epoch %d" % epoch)

        start_index = np.random.randint(0,len(content) -maxlen - 1)
        for diversity in [0.2,0.5,1.0,1.2]:
            print("...diversity:",diversity)

            generated = ""
            sentence = content[start_index:start_index+maxlen]
            print('...Generating with seed: "' + sentence + '"')

            for i in range(400):
                x_pred = np.zeros((1,len(chars)))
                for t,char in enumerate(sentence):
                    x_pred[0,char_indices[char]] = 1.0
                preds = model.predict(x_pred,verbose=0)[0]
                next_index = self.sample(preds,diversity)
                next_char = indices_char[next_index]
                sentence = sentence[1:] + next_char
                generated += next_char
            print("...Generated: ",generated)
            print()
            topSeven = []
            contentSong = []
            fullAbc = ""
            count = 0
            if "X:" in generated:
                index = generated.find("X:")
                generated = generated[index:]
                genList = generated.split('\n')
                for line in genList:
                    if count > 6:
                        if line and generated[count+1]:
                            contentSong.append(line)
                        else:
                            contentSong.append(line)
                            break
                    if line.startswith(("X:","T:","%","S:","M:","L:","K:")):
                        topSeven.append(line)
                        count+=1
                if len(topSeven) == 7:
                    for x in topSeven:
                        fullAbc += x + "\n"
                    for x in contentSong:
                        fullAbc += x + "\n"
                    with open("good_reels.txt",'a') as f:
                        f.write("\n" + fullAbc)
                        f.close()
                    break
                    
                            
                

                    
                

解决方法

您在此代码中声明了两次 x。先来

x = np.zeros((len(sentences),maxlen,len(chars)),dtype=np.bool)
y = np.zeros((len(sentences),dtype=np.bool)

这里是第二个：

if len(topSeven) == 7:
    for x in topSeven:
        fullAbc += x + "\n"
    for x in contentSong:
        fullAbc += x + "\n"
    with open("good_reels.txt",'a') as f:
        f.write("\n" + fullAbc)
        f.close()
    break

在第一次循环迭代中，x 确实是一个 numpy.ndarray，它会按预期工作。当它到达第二个声明时，x 现在是一个 str，它也会按预期工作。

在第二次循环迭代中，x 当前为 str，而它预期为 numpy.ndarray，并且会给出错误。

要修复它，只需将 x 的第二个声明重命名为 c，例如，甚至删除它被声明的循环：

if len(topSeven) == 7:
    fullAbc += '\n'.join(topSeven)
    fullAbc += '\n'.join(contentSong)
    with open("good_reels.txt",'a') as f:
        f.write("\n" + fullAbc)
        f.close()
    break