keras - 如何解决这个InternalError

import pandas as pd import numpy as np import tensorflow as tf path = '../1.데이터/' data1 = pd.read_csv(path + "test.csv", index_col = "PassengerId") data2 = pd.read_csv(path + "gender_submission.csv", index_col = "PassengerId") test_data = pd.merge(data1, data2, on = "PassengerId") test_data["Sex"] = pd.get_dummies(test_data["Sex"]) test_data["Embarked"] = pd.get_dummies(test_data["Embarked"]) inde = test_data[["Pclass", "Sex", "SibSp", "Parch", "Fare", "Embarked"]] sub = test_data["Survived"] X = tf.keras.layers.Input(shape=[6]) Y = tf.keras.layers.Dense(1, activation='softmax')(X) model = tf.keras.models.Model(X, Y) model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(inde, sub, epochs=1000, verbose=0) model.fit(inde, sub, epochs=10)

--------------------------------------------------------------------------- InternalError Traceback (most recent call last) <ipython-input-34-0d8442a43d4d> in <module> ----> 1 model.fit(inde, sub, epochs=1000, verbose=0) 2 model.fit(inde, sub, epochs=10) C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs) 817 max_queue_size=max_queue_size, 818 workers=workers, --> 819 use_multiprocessing=use_multiprocessing) 820 821 def evaluate(self, C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs) 340 mode=ModeKeys.TRAIN, 341 training_context=training_context, --> 342 total_epochs=epochs) 343 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN) 344 C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs) 126 step=step, mode=mode, size=current_batch_size) as batch_logs: 127 try: --> 128 batch_outs = execution_function(iterator) 129 except (StopIteration, errors.OutOfRangeError): 130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError? C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn) 96 # `numpy` translates Tensors to values in Eager mode. 97 return nest.map_structure(_non_none_constant_value, ---> 98 distributed_function(input_fn)) 99 100 return execution_function C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds) 566 xla_context.Exit() 567 else: --> 568 result = self._call(*args, **kwds) 569 570 if tracing_count == self._get_tracing_count(): C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds) 630 # Lifting succeeded, so variables are initialized and we can run the 631 # stateless function. --> 632 return self._stateless_fn(*args, **kwds) 633 else: 634 canon_args, canon_kwds = \ C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs) 2361 with self._lock: 2362 graph_function, args, kwargs = self._maybe_define_function(args, kwargs) -> 2363 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access 2364 2365 @property C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs) 1609 if isinstance(t, (ops.Tensor, 1610 resource_variable_ops.BaseResourceVariable))), -> 1611 self.captured_inputs) 1612 1613 def _call_flat(self, args, captured_inputs, cancellation_manager=None): C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager) 1690 # No tape is watching; skip to running the function. 1691 return self._build_call_outputs(self._inference_function.call( -> 1692 ctx, args, cancellation_manager=cancellation_manager)) 1693 forward_backward = self._select_forward_and_backward_functions( 1694 args, C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager) 543 inputs=args, 544 attrs=("executor_type", executor_type, "config_proto", config), --> 545 ctx=ctx) 546 else: 547 outputs = execute.execute_with_cancellation( C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 65 else: 66 message = e.message ---> 67 six.raise_from(core._status_to_exception(e.code, message), None) 68 except TypeError as e: 69 keras_symbolic_tensors = [ C:\tools\Anaconda3\envs\tensorflow2_py37\lib\site-packages\six.py in raise_from(value, from_value) InternalError: Blas GEMV launch failed: m=6, n=32 [[node model_7/dense_7/MatMul (defined at <ipython-input-34-0d8442a43d4d>:1) ]] [Op:__inference_distributed_function_1008] Function call stack: distributed_function

[name: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 4159448654357613034 , name: "/device:GPU:0" device_type: "GPU" memory_limit: 5077532672 locality { bus_id: 1 links { } } incarnation: 8467986699714318171 physical_device_desc: "device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1" ] tensorflow 2.1.0 keras 2.2.4-tf pandas 1.1.1 sklearn 0.23.2 scipy 1.5.2 numpy 1.19.1 matplotlib 3.3.1 h5py 2.10.0