为什么python模型中的fit命令出现错误

Question

我想训练模型。但是我犯了这个错误。我的数据集中有7个元素。我可以提供详细信息。

ValueError Traceback(最后一次调用(在((1模型.编译(优化器="dam"，损失="类别_交叉熵"，度量=["准确性"](---->2历史=模型拟合(train_X，one_hot_train，batch_size=7，历元=10，validation_split=0.2(

1帧/autograph_handler中的usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/funcc_graph.py(*args，**kwargs(1127 Exception as e:#pylint:disable=broad-except1128如果hasattr(e，"ag_error_metadata"(：->1129引发e.ag_error_metadata.to_exception(e(1130其他：1131提升

ValueError:在用户代码中：

File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 878, in train_function  *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 867, in step_function  **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in run_step  **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 810, in train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call  **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1665, in categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 4994, in categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
ValueError: Shapes (None, 2) and (None, 7) are incompatible

Answer 1

尝试重塑张量。

例如：

X = tf.constant(np.array([-7, -4, -1, 2,5,8,11,14,17,20]))  
y =    tf.constant(np.array([3, 6, 9, 12,15,18,21,24,27,30])) 
X=    tf.reshape(X,(10,1)) 
y = tf.reshape(y,(10,1))    
model.fit(X,y,epochs=10)

这个模型。它在X和y上不起作用，除非它们被这样重塑。