在Keras中，LSTM模型的fit方法不支持输入序列中包含None值。如果输入序列中存在None值，可以通过以下两种方法解决：

方法一：删除包含None值的样本 可以使用Python的列表解析来删除包含None值的样本，然后将处理后的序列传递给模型进行训练。示例代码如下：

import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 原始输入序列，包含None值
x_train = [[1, 2, None, 4], [5, 6, 7, 8], [None, 10, 11, 12]]
y_train = [1, 2, 3]

# 删除包含None值的样本
x_train = [x for x in x_train if None not in x]
y_train = [y for x, y in zip(x_train, y_train) if None not in x]

# 转换为numpy数组
x_train = np.array(x_train)
y_train = np.array(y_train)

# 创建LSTM模型
model = Sequential()
model.add(LSTM(32, input_shape=(4, 1)))
model.add(Dense(1))

# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(x_train.reshape(x_train.shape[0], x_train.shape[1], 1), y_train, epochs=10, batch_size=1)

方法二：填充None值为特定的值 可以将None值替换为特定的值，例如0或者其他合适的值，然后将处理后的序列传递给模型进行训练。示例代码如下：

import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 原始输入序列，包含None值
x_train = [[1, 2, None, 4], [5, 6, 7, 8], [None, 10, 11, 12]]
y_train = [1, 2, 3]

# 填充None值为0
x_train = [[0 if val is None else val for val in x] for x in x_train]

# 转换为numpy数组
x_train = np.array(x_train)
y_train = np.array(y_train)

# 创建LSTM模型
model = Sequential()
model.add(LSTM(32, input_shape=(4, 1)))
model.add(Dense(1))

# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(x_train.reshape(x_train.shape[0], x_train.shape[1], 1), y_train, epochs=10, batch_size=1)

以上两种方法都可以解决在Keras LSTM的fit方法中不支持包含None值的问题。根据具体的情况选择适合的方法处理None值。