在自定义网络中调用model.fit(train_X, epochs=5, batch_size=64, verbose=2)进行训练报错。
The loss value printed in the log is the current step, and the metric is the average value of previous step.
Epoch 1/5
---------------------------------------------------------------------------ValueError Traceback (most recent call last) in
14 model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()),loss=paddle.nn.CrossEntropyLoss(),metrics=paddle.metric.Accuracy())
15
---> 16 model.fit(train_X, epochs=5, batch_size=64, verbose=2)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model.py in fit(self, train_data, eval_data, batch_size, epochs, eval_freq, log_freq, save_dir, save_freq, verbose, drop_last, shuffle, num_workers, callbacks)
1490 for epoch in range(epochs):
1491 cbks.on_epoch_begin(epoch)
-> 1492 logs = self._run_one_epoch(train_loader, cbks, 'train')
1493 cbks.on_epoch_end(epoch, logs)
1494
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model.py in _run_one_epoch(self, data_loader, callbacks, mode, logs)
1797 if mode != 'predict':
1798 outs = getattr(self, mode + '_batch')(data[:len(self._inputs)],
-> 1799 data[len(self._inputs):])
1800 if self._metrics and self._loss:
1801 metrics = l[0] for l in outs[0]
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model.py in train_batch(self, inputs, labels)
938 print(loss)
939 """
--> 940 loss = self._adapter.train_batch(inputs, labels)
941 if fluid.in_dygraph_mode() and self._input_info is None:
942 self._update_inputs()
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model.py in train_batch(self, inputs, labels)
652 else:
653 outputs = self.model.network.forward(
--> 654 * [to_variable(x) for x in inputs])
655
656 losses = self.model._loss(*(to_list(outputs) + labels))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/container.py in forward(self, input)
84 def forward(self, input):
85 for layer in self._sub_layers.values():
---> 86 input = layer(input)
87 return input
88
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py incall(self, *inputs,**kwargs)
882 self._built = True
883
--> 884 outputs = self.forward(*inputs,**kwargs)
885
886 for forward_post_hook in self._forward_post_hooks.values():
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/rnn.py in forward(self, inputs, initial_states, sequence_length)
1033 if self.could_use_cudnn:
1034 # Add CPU kernel and dispatch in backend later
-> 1035 return self._cudnn_impl(inputs, initial_states, sequence_length)
1036
1037 states = split_states(initial_states, self.num_directions == 2,
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/rnn.py in _cudnn_impl(self, inputs, initial_states, sequence_length)
977 def _cudnn_impl(self, inputs, initial_states, sequence_length):
978 if not self.time_major:
--> 979 inputs = paddle.tensor.transpose(inputs, [1, 0, 2])
980 out = self._helper.create_variable_for_type_inference(inputs.dtype)
981 state = [
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/nn.py in transpose(x, perm, name)
5469 """
5470 if in_dygraph_mode():
-> 5471 out, _ = core.ops.transpose2(x, 'axis', perm)
5472 return out
5473
ValueError: (InvalidArgument) The input tensor's dimension should be equal to the axis's size. But received input tensor's dimension is 2, axis's size is 3
[Hint: Expected x_rank == axis_size, but received x_rank:2 != axis_size:3.] (at /paddle/paddle/fluid/operators/transpose_op.cc:47)
[Hint: If you need C++ stacktraces for debugging, please set FLAGS_call_stack_level=2.]
[operator < transpose2 > error]

附Tensor类形状
(965, 1, 6)