tensorflow：模型是用形状（无，无，6）构造的，但是在形状不兼容的输入上被调用

完整的错误是

WARNING:tensorflow:Model was constructed with shape (None, None, 6) for input KerasTensor(type_spec=TensorSpec(shape=(None, None, 6), dtype=tf.float32, name='dense_input'), name='dense_input', description="created by layer 'dense_input'"), but it was called on an input with incompatible shape (None, 6).
2021-02-10 17:56:08.288146: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-02-10 17:56:08.544550: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
WARNING:tensorflow:Model was constructed with shape (None, None, 6) for input KerasTensor(type_spec=TensorSpec(shape=(None, None, 6), dtype=tf.float32, name='dense_4_input'), name='dense_4_input', description="created by layer 'dense_4_input'"), but it was called on an input with incompatible shape (None, 6).
WARNING:tensorflow:Model was constructed with shape (None, None, 6) for input KerasTensor(type_spec=TensorSpec(shape=(None, None, 6), dtype=tf.float32, name='dense_input'), name='dense_input', description="created by layer 'dense_input'"), but it was called on an input with incompatible shape (20, 6).
WARNING:tensorflow:Model was constructed with shape (None, None, 6) for input KerasTensor(type_spec=TensorSpec(shape=(None, None, 6), dtype=tf.float32, name='dense_input'), name='dense_input', description="created by layer 'dense_input'"), but it was called on an input with incompatible shape (20, 6).

我确定这是输入形状错误，这是相关代码：

def create_model(self):
    model = tf.keras.models.Sequential()  # a basic feed-forward model
    model.add(tf.keras.layers.Dense(6, activation=tf.nn.relu, input_shape=env.OBSERVATION_SPACE_VALUES))  # a simple fully-connected layer, 6 units, relu activation
    model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  # a simple fully-connected layer, 128 units, relu activation
    model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))  # a simple fully-connected layer, 64 units, relu activation
    model.add(tf.keras.layers.Dense(27, activation=tf.nn.softmax))  # our output layer. 27 units for 27 actions 
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


    return model

env.OBSERVATION_SPACE_VALUES为（N0NE，6）输入基本上是具有6个值的一维数组