我正在使用预处理来标记数据并使用它们来拟合模型

我的资源：https://machinelearningmastery.com/prepare-text-data-deep-learning-keras

这是我的代码（我想从负面词汇中检测出正面词汇）：

from keras.preprocessing.text import one_hotfrom keras.preprocessing.text import text_to_word_sequenceimport pandas as pdimport numpy as npfrom keras.layers import Densefrom keras.models import Sequentialfrom keras.preprocessing.text import Tokenizerfrom tensorflow.keras.layers.experimental import preprocessingdata = ["Good", "Great", 'Bad', 'Awefull']t = Tokenizer()# fit the tokenizer on the documentst.fit_on_texts(data)# integer encode documentsx = t.texts_to_matrix(data, mode='count')print(x)y = np.array([[1],[1],[0],[0]])print(y)model = Sequential()model.add(Dense(16, input_dim=2, activation='relu'))model.add(Dense(16, activation='relu'))model.add(Dense(1, activation='sigmoid'))model.compile(loss='mean_squared_error',    optimizer='adam',    metrics=['binary_accuracy'])model.fit(x, y, verbose=2, epochs=500)model.save("good_or_bad.h5")

我遇到了这个错误：

raise ValueError(    ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 2 but received input with shape [None, 5]

我该如何解决这个错误？

回答：

更改这一行：

model.add(Dense(16, input_dim=5, activation='relu'))