train_data = 2352 train = veccc_dv[:train_data, :] test = veccc_dv[train_data:, :] train_X = train[:, :6] train_Y = train[:, 6:] test_X = test[:, :6] test_Y = test[:, 6:] #模型搭建 model = Sequential() input_shape = (6, 3) model.add(LSTM(64, input_shape=input_shape)) model.add(Dense(3, activation='softmax')) model.add(Dropout(0.25)) #模型编译 model.compile(loss='mean_squared_error', optimizer='adam') #模型训练 history = model.fit(train_X, train_Y, epochs=50, validation_data=(test_X, test_Y), verbose=3, shuffle=False) tf.keras.backend.print_tensor(input_shape)请依据错误提示修改代码

根据您提供的错误提示，应该是 input_shape 与输入数据的形状不匹配导致的问题。您可以尝试修改代码如下：

train_data = 2352 
train = veccc_dv[:train_data, :]
test = veccc_dv[train_data:, :]
train_X = train[:, :6]
train_Y = train[:, 6:]
test_X = test[:, :6]
test_Y = test[:, 6:]

# 模型搭建
model = Sequential()
input_shape = (train_X.shape[1], train_X.shape[2])  # 修改 input_shape 为输入数据的形状
model.add(LSTM(64, input_shape=input_shape))
model.add(Dense(3, activation='softmax'))
model.add(Dropout(0.25))

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

# 模型训练
history = model.fit(train_X, train_Y, epochs=50, validation_data=(test_X, test_Y), verbose=3, shuffle=False)

tf.print(input_shape)  # 使用 tf.print 打印 input_shape

这里将 input_shape 修改为了 `(train_X.shape[1], train_X.shape[2])`，即输入数据的形状，同时使用 `tf.print` 打印出 input_shape。您可以根据具体情况调整参数。