我用Keras训练了一个模型，并想对其进行评估。但我总是遇到这个错误。我在这里找到一个解决方案 TensorFlow: InternalError: Blas SGEMM launch failed，但这只是针对TensorFlow的解决方案。
Using TensorFlow backend.2017-11-01 10:40:49.120525: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.2017-11-01 10:40:49.120546: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.2017-11-01 10:40:49.120553: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.2017-11-01 10:40:49.120557: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.2017-11-01 10:40:49.120562: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.2017-11-01 10:40:49.266103: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2017-11-01 10:40:49.266511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties: name: GeForce 940MXmajor: 5 minor: 0 memoryClockRate (GHz) 1.189pciBusID 0000:01:00.0Total memory: 1.96GiBFree memory: 1.78GiB2017-11-01 10:40:49.266528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 2017-11-01 10:40:49.266534: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y 2017-11-01 10:40:49.266542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0)x_train shape: (50000, 32, 32, 3)50000 train samples10000 test samples2017-11-01 10:40:54.162805: E tensorflow/stream_executor/cuda/cuda_blas.cc:366] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED2017-11-01 10:40:54.162825: W tensorflow/stream_executor/stream.cc:1756] attempting to perform BLAS operation using StreamExecutor without BLAS supportTraceback (most recent call last): File "/home/viktor/PycharmProjects/ProjectSSD/test.py", line 39, in <module> scores = model.evaluate(x_test_bin, y_test, verbose=1) File "/home/viktor/.local/lib/python2.7/site-packages/keras/models.py", line 896, in evaluate sample_weight=sample_weight) File "/home/viktor/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1657, in evaluate steps=steps) File "/home/viktor/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1339, in _test_loop batch_outs = f(ins_batch) File "/home/viktor/.local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 2273, in __call__ **self.session_kwargs) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 895, in run run_metadata_ptr) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1124, in _run feed_dict_tensor, options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1321, in _do_run options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1340, in _do_call raise type(e)(node_def, op, message)tensorflow.python.framework.errors_impl.InternalError: Blas GEMM launch failed : a.shape=(32, 2304), b.shape=(2304, 512), m=32, n=512, k=2304 [[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]Caused by op u'dense_1/MatMul', defined at: File "/home/viktor/PycharmProjects/ProjectSSD/test.py", line 13, in <module> model = load_model(save_dir + '/' + model_name) File "/home/viktor/.local/lib/python2.7/site-packages/keras/models.py", line 239, in load_model model = model_from_config(model_config, custom_objects=custom_objects) File "/home/viktor/.local/lib/python2.7/site-packages/keras/models.py", line 313, in model_from_config return layer_module.deserialize(config, custom_objects=custom_objects) File "/home/viktor/.local/lib/python2.7/site-packages/keras/layers/__init__.py", line 54, in deserialize printable_module_name='layer') File "/home/viktor/.local/lib/python2.7/site-packages/keras/utils/generic_utils.py", line 139, in deserialize_keras_object list(custom_objects.items()))) File "/home/viktor/.local/lib/python2.7/site-packages/keras/models.py", line 1214, in from_config model.add(layer) File "/home/viktor/.local/lib/python2.7/site-packages/keras/models.py", line 475, in add output_tensor = layer(self.outputs[0]) File "/home/viktor/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 602, in __call__ output = self.call(inputs, **kwargs) File "/home/viktor/.local/lib/python2.7/site-packages/keras/layers/core.py", line 841, in call output = K.dot(inputs, self.kernel) File "/home/viktor/.local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 998, in dot out = tf.matmul(x, y) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1844, in matmul a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1289, in _mat_mul transpose_b=transpose_b, name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2630, in create_op original_op=self._default_original_op, op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1204, in __init__ self._traceback = self._graph._extract_stack() # pylint: disable=protected-accessInternalError (see above for traceback): Blas GEMM launch failed : a.shape=(32, 2304), b.shape=(2304, 512), m=32, n=512, k=2304 [[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]这是代码：首先加载模型。加载数据集。准备数据进行评估。然后进行评估。
from __future__ import print_functionimport kerasfrom keras.datasets import cifar10from keras.models import load_modelimport numpy as npimport osnum_classes = 10save_dir = os.path.join(os.getcwd(), 'examples/saved_models')model_name = 'keras_cifar10_trained_model.h5'model = load_model(save_dir + '/' + model_name)# The data, shuffled and split between train and test sets:(x_train, y_train), (x_test, y_test) = cifar10.load_data()print('x_train shape:', x_train.shape)print(x_train.shape[0], 'train samples')print(x_test.shape[0], 'test samples')# Convert class vectors to binary class matrices.y_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)x_train_float = x_train.astype('float32')x_test_float = x_test.astype('float32')x_train_bin = x_train_float / 255x_test_bin = x_test_float / 255# Score trained model.scores = model.evaluate(x_test_bin, y_test, verbose=1)print('Test loss:', scores[0])print('Test accuracy:', scores[1])我发现其他人也有同样的问题，如果他们同时运行两个会话。当执行以下代码行时：
model = load_model(save_dir + '/' + model_name)GPU使用率会增加。（使用watch -n 0.5 nvidia-smi监控）这可能是问题所在吗？
回答：
@[隐藏人名]，对不起浪费了你的时间。
错误来自代码的一部分，与cv2有关。我已经开启了一个新问题。
cv2, keras, InternalError (see above for traceback): Blas GEMM launch failed
非常感谢你，你帮助我更接近解决方案了
