我在使用Keras进行一些强化学习（我主要使用Torch，这是我第二次或第三次使用Keras），以下是简化的代码

model=keras.models.Sequential([    keras.layers.Dense(10,activation='relu',input_shape=[4],name='layer1'),    keras.layers.Dense(4,activation='softmax',name='layer2'),    ])

然后我对一些数据进行调用

obs=tf.convert_to_tensor([x1,y1,x2,y2],dtype=tf.float32)pred=model(obs)

其中x1等是整数，我得到了以下错误

WARNING:tensorflow:Model was constructed with shape Tensor("layer1_input:0", shape=(None, 4), dtype=float32) for input (None, 4), but it was re-called on a Tensor with incompatible shape (4,).Traceback (most recent call last):  File "C:\Users\milok\ev_rl.py", line 131, in <module>    all_rewards,all_grads = play_multiple(env,n_episodes_per_update,n_max_steps,model,loss_fn)  File "C:\Users\milok\ev_rl.py", line 101, in play_multiple    obs,reward,grad = take_step(env,obs,model,loss_fn)  File "C:\Users\milok\ev_rl.py", line 81, in take_step    pred=model(obs.as_tensor())  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 822, in __call__    outputs = self.call(cast_inputs, *args, **kwargs)  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py", line 267, in call    return super(Sequential, self).call(inputs, training=training, mask=mask)  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 717, in call    convert_kwargs_to_constants=base_layer_utils.call_context().saving)  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 891, in _run_internal_graph    output_tensors = layer(computed_tensors, **kwargs)  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 822, in __call__    outputs = self.call(cast_inputs, *args, **kwargs)  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\keras\layers\core.py", line 1142, in call    outputs = gen_math_ops.mat_mul(inputs, self.kernel)  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\ops\gen_math_ops.py", line 5615, in mat_mul    _ops.raise_from_not_ok_status(e, name)  File "C:\Users\milok\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 6606, in raise_from_not_ok_status    six.raise_from(core._status_to_exception(e.code, message), None)  File "<string>", line 3, in raise_fromtensorflow.python.framework.errors_impl.InvalidArgumentError: In[0] is not a matrix. Instead it has shape [4] [Op:MatMul]```

回答：

在计算预测时要注意管理批次维度… 你必须传递给模型一个维度为（批次大小, 特征数）的对象

model=tf.keras.models.Sequential([    tf.keras.layers.Dense(10,activation='relu',input_shape=[4],name='layer1'),    tf.keras.layers.Dense(4,activation='softmax',name='layer2'),    ])### Error ###obs=tf.constant([1,2,3,4],dtype=tf.float32) pred=model(obs)### OK ###obs=tf.constant([[1,2,3,4]],dtype=tf.float32)pred=model(obs)