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sddwt
已经解决这个的问题了,
但是现在发现训练过程loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
没有修改网络,由于我缩减了分类到8个,请问该如何调整呢?
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sddwt
导出tfrecord时是正常的,
take的时候就报错了,
图片做了resize呀Traceback (most recent call last): File "train_simple.py", line 121, in <module> train() File "train_simple.py", line 49, in train for data in train_dataset.take(2): File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 747, in __next__ return self._next_internal() File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 739, in _next_internal return structure.from_compatible_tensor_list(self._element_spec, ret) File "/usr/lib64/python3.6/contextlib.py", line 99, in __exit__ self.gen.throw(type, value, traceback) File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/context.py", line 2116, in execution_mode executor_new.wait() File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/executor.py", line 69, in wait pywrap_tfe.TFE_ExecutorWaitForAllPendingNodes(self._handle) tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 12288 values, but the requested shape has 4096 [[{{node Reshape}}]]
resize取域:
# print(img.tobytes()) img = cv2.resize(img, (64, 64)) w = img.shape[0] h = img.shape[1] # cv2.imshow('', img)
求大佬帮助!