Keras使用训练有素的InceptionV3模型+ CIFAR10遇到了有关批处理大小的错误 [英] Keras use trained InceptionV3 model + CIFAR10 got error about Batch Size
问题描述
我是机器学习和Keras等的新手.
I am new to Machine Learning and Keras etc.
尝试使用经过训练的模型来提高准确性，在我的情况下，我跟随Pluralsight上的Jerry Kurata使用InceptionV3，仅修改了最后一层以进行识别鸟类的训练.
Trying to use trained model to increase accuracy, in my case I followed Jerry Kurata on Pluralsight to use InceptionV3 and only modify the last layer to train for recognizing birds.
我拥有的数据集来自Keras内置的CIFAR10，这是官方教程
The dataset I have is from Keras built-in CIFAR10 and here is the official tutorial
这是错误消息:
F tensorflow/stream_executor/cuda/cuda_dnn.cc:516]检查失败: cudnnSetTensorNdDescriptor(handle_.get()，elem_type，nd，dims.data()， strides.data())== CUDNN_STATUS_SUCCESS(3 vs.0)batch_descriptor: {count:32个feature_map_count:288个空间:％d 0％d 0 value_min: 0.000000 value_max:0.000000布局:BatchDepthYX}已中止(核心已转储)
F tensorflow/stream_executor/cuda/cuda_dnn.cc:516] 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: 288 spatial: %d 0%d 0 value_min: 0.000000 value_max: 0.000000 layout: BatchDepthYX} Aborted (core dumped)
我从此处
CIFAR10(32 * 32)中的图像样本太小，这导致 问题
The image samples in CIFAR10 (32*32) is too small which cause this issue
但是我不知道如何解决它.
But I cannot figure out how to fix it.
这是我的代码:
import matplotlib.pyplot as plt
import keras
from keras import backend as K
with K.tf.device("/device:GPU:0"):
config = K.tf.ConfigProto(intra_op_parallelism_threads=4,
inter_op_parallelism_threads=4, allow_soft_placement=True,
device_count = {'CPU' : 1, 'GPU' : 1})
session = K.tf.Session(config=config)
K.set_session(session)
from keras.callbacks import EarlyStopping
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.datasets import cifar10
# "/device:GPU:0"
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def create_generator():
return ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
zca_epsilon=1e-06, # epsilon for ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
# randomly shift images horizontally (fraction of total width)
width_shift_range=0.1,
# randomly shift images vertically (fraction of total height)
height_shift_range=0.1,
shear_range=0., # set range for random shear
zoom_range=0., # set range for random zoom
channel_shift_range=0., # set range for random channel shifts
# set mode for filling points outside the input boundaries
fill_mode='nearest',
cval=0., # value used for fill_mode = "constant"
horizontal_flip=True, # randomly flip images
vertical_flip=False, # randomly flip images
# set rescaling factor (applied before any other transformation)
rescale=None,
# set function that will be applied on each input
preprocessing_function=None,
# image data format, either "channels_first" or "channels_last"
data_format=None,
# fraction of images reserved for validation (strictly between 0 and 1)
validation_split=0.0)
Training_Epochs = 1
Batch_Size = 32
Number_FC_Neurons = 1024
Num_Classes = 10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# 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)
# load cifar10 data here https://keras.io/datasets/
datagen = create_generator()
datagen.fit(x_train)
Inceptionv3_model = InceptionV3(weights='imagenet', include_top=False)
print('Inception v3 model without last FC loaded')
x = Inceptionv3_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(Number_FC_Neurons, activation='relu')(x)
predictions = Dense(Num_Classes, activation='softmax')(x)
model = Model(inputs=Inceptionv3_model.input, outputs=predictions)
# print(model.summary())
print('\nFine tuning existing model')
Layers_To_Freeze = 172
for layer in model.layers[:Layers_To_Freeze]:
layer.trainable = False
for layer in model.layers[Layers_To_Freeze:]:
layer.trainable = True
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
cbk_early_stopping = EarlyStopping(monitor='val_acc', mode='max')
print(len(x_train))
history_transfer_learning = model.fit_generator(
datagen.flow(x_train, y_train, batch_size=Batch_Size),
epochs=Training_Epochs,
validation_data=(x_test, y_test),
workers=4,
steps_per_epoch=len(x_train)//Batch_Size,
callbacks=[cbk_early_stopping]
)
model.save('incepv3_transfer_cifar10.h5', overwrite=True, include_optimizer=True)
# Score trained model.
scores = model.evaluate(x_test, 12, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
推荐答案
您所说的错误是输入大小的差异.预先训练的Imagenet模型比Cifar-10(32，32)拍摄更大的图像.
Your error as you said is the input size difference. The pre trained Imagenet model takes a bigger size of image than the Cifar-10 (32, 32).
您需要像这样手动指定模型的输入形状.
You need to specify the input_shape of the model before hand like this.
Inceptionv3_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(32, 32, 3))
For more explanation you can check this tutorial.
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