machine-learning - `Check failed: cudnnSetTensorNdDescriptor` 当使用预训练的 Keras 模型进行迁移学习时

Question

我正在尝试将 Imagenet 预训练架构之一从 keras.applications 传输到 CIFAR-10，但出现 CUDA 错误(导致我的 jupyter 笔记本内核立即崩溃)当我尝试拟合我的模型时的最后一行)。可能出了什么问题？

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}

代码:

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')

Answer 1

检查 InceptionResnetV2 的输入要求网络:

It should have exactly 3 inputs channels, and width and height should be no smaller than 75

并且您正在尝试适应 32x32 的 CIFAR10 图像。