[我正在尝试将Imagenet预训练的一种架构从keras.applications转移到CIFAR-10，但是我遇到了CUDA错误（当我尝试适应时，jupyter笔记本内核会在最后一行立即崩溃。我的模型）。可能出什么问题了]
输出：
2019-01-10 00:39:40.165264: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-01-10 00:39:40.495421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX TITAN X major: 5 minor: 2 memoryClockRate(GHz): 1.2405
pciBusID: 0000:01:00.0
totalMemory: 11.93GiB freeMemory: 11.63GiB
2019-01-10 00:39:40.495476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-10 00:39:40.819773: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-10 00:39:40.819812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-01-10 00:39:40.819819: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-01-10 00:39:40.820066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 11256 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0, compute capability: 5.2)
2019-01-10 00:39:40.844280: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-10 00:39:40.844307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-10 00:39:40.844313: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-01-10 00:39:40.844317: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-01-10 00:39:40.844520: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11256 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0, compute capability: 5.2)
[I 00:40:58.262 NotebookApp] Saving file at /Untitled.ipynb
2019-01-10 00:42:56.543392: F tensorflow/stream_executor/cuda/cuda_dnn.cc:542] Check failed: cudnnSetTensorNdDescriptor(handle_.get(), elem_type, nd, dims.data(), strides.data()) == CUDNN_STATUS_SUCCESS (3 vs. 0)batch_descriptor: {count: 32 feature_map_count: 320 spatial: 0 0 value_min: 0.000000 value_max: 0.000000 layout: BatchDepthYX}
产品编号：
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.preprocessing import image
from keras.layers import Dense, GlobalAveragePooling2D
from keras.models import Model
import keras.utils
import numpy as np
from keras.datasets import cifar10
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
# Define model
base_model = InceptionResNetV2(weights='imagenet', include_top=False)
x = base_model.output
print(x.shape)
x = GlobalAveragePooling2D()(x)
x = Dense(1024,activation='relu')(x)
preds = Dense(10,activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=preds)
# Only fine-tune last layer
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
检查对InceptionResnetV2网络输入的要求：
您解决了这个问题吗？我遇到了同样的问题，但这很奇怪，我可以运行我的网络，直到达到第N个（N不是常数），然后引发错误：“ 2019-09-24 21：03：16.109522：F tensorflow / stream_executor / cuda / cuda_dnn.cc：521]检查失败：cudnnSetTensorNdDescriptor（handle_.get（），elem_type，nd，dims.data（），strides.data（））== CUDNN_STATUS_SUCCESS（ 3 vs. 0）batch_descriptor：{计数：0 feature_map_count：32空间：16 112 128 value_min：0.000000 value_max：0.000000 layout：BatchDepthYX}“我检查了代码，似乎没有错误〜是什么原因引起的？ GPU内存还是cuda和cudnn的版本？如果您得到答复，请告诉我，非常感谢。我的电子邮件：[email protected]