在keras中声明转换序列的输入形状?

我正在尝试对文本输入运行一个神经网络。这是一个二进制分类。以下是迄今为止我的工作代码:

df = pd.read_csv(pathname, encoding = "ISO-8859-1")
df = df[['content_cleaned', 'meaningful']] #Content cleaned: text, meaningful: label

X = df['content_cleaned']
y = df['meaningful']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=21) 

tokenizer = Tokenizer(num_words=100)
tokenizer.fit_on_texts(X_train)
X_train_encoded = tokenizer.texts_to_sequences(X_train)
X_test_encoded = tokenizer.texts_to_sequences(X_test)

max_len = 100
X_train = pad_sequences(X_train_encoded, maxlen=max_len)
X_test = pad_sequences(X_test_encoded, maxlen=max_len)


batch_size = 100
max_words = 100
input_dim = X_train.shape[1]  # Number of features
model = Sequential()
model.add(layers.Dense(10, activation='relu', input_shape=X_train.shape[1:]))




model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='categorical_crossentropy',
          optimizer='adam',
          metrics=['accuracy'])

history = model.fit(X_train, X_test,
                batch_size=batch_size,
                epochs=5,
                verbose=1,
                validation_split=0.1)

我的问题分为两部分。第一个是和 input_shape 创建层时。我对声明这个的语法感到困惑。运行此命令时:

print(X_train.shape)

我的体型是: (3609, 100) .

据我所知,这告诉我有3609个实例。从其他例子来看,我幼稚的假设是使用100,因为有100种类型(可能理解不正确)对应于 max_words 我初始化的。我相信在初始化 输入形状 .

第二个问题是在运行所有这些内容时出现错误消息(很可能是错误的 输入形状 )错误消息突出显示这一行代码:

 validation_split=0.1)

错误消息为:

ValueError: Error when checking target: expected dense_2 to have shape (None, 1) but got array with shape (1547, 1

我是不是不正确地处理这个问题?我对深入学习很陌生。