[help]ValueError: 无法将 NumPy 数组转换为张量（不支持的对象类型列表）

Question

我目前正在学习机器学习课程，但此时我陷入了困境。我的代码也和导师展示的完全一样，但他一直在屏幕外更改代码，谁能帮忙

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import preprocessing
import numpy

imdb = keras.datasets.imdb
(train_data, train_lables), (test_data, test_lables) = imdb.load_data(num_words=10000)

_word_index = imdb.get_word_index()

word_index = {k:(v+3) for k,v in _word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"]  = 2
word_index["<UNUSED>"] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

def decode_review(text):
    return " ".join([reverse_word_index.get(i, "?") for i in text])

train_data=keras.preprocessing.sequence.pad_sequences(train_data, value=word_index["<PAD>"], padding="post",maxlen=250)

train_data=keras.preprocessing.sequence.pad_sequences(test_data, value=word_index["<PAD>"], padding="post",maxlen=250)

model = keras.Sequential()
model.add(keras.layers.Embedding(88000, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation="relu"))
model.add(keras.layers.Dense(1, activation="sigmoid"))

model.summary()

model.compile(optimizer="adam",loss="binary_crossentropy",metrics=["accuracy"])

x_val = train_data[:10000]
x_train = train_data[10000:]

y_val = train_lables[:10000]
y_train = train_lables[10000:]

fitModel = model.fit(x_train, y_train,epochs=40,batch_size=512,validation_data=(x_val, y_val),verbose=1)

results = model.evaluate(test_data, test_lables)
```

Answer 1

在这里：

train_data = keras.preprocessing.sequence.pad_sequences(test_data, value=word_index["<PAD>"], padding="post",maxlen=250)
^^^^^^^^^^                                              ^^^^^^^^^

这应该以test_data 开头。由于test_data 永远不会被填充，它最终会得到不同长度的列表，不能转换为ndarray，然后不能转换为张量。