python - 无法通过第一个纪元 - 只是挂起 [Keras Transfer Learning Inception]

Question

我基本上使用了 Keras Inception 迁移学习 API 教程中的大部分代码，

https://faroit.github.io/keras-docs/2.0.0/applications/#inceptionv3

只需进行一些小改动以适合我的数据。

我正在使用 Tensorflow-gpu 1.4、Windows 7 和 Keras 2.03(？最新的 Keras)。

代码:

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K


img_width, img_height = 299, 299
train_data_dir = r'C:\Users\Moondra\Desktop\Keras Applications\data\train'
nb_train_samples = 8
nb_validation_samples = 100 
batch_size = 10
epochs = 5


train_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
zoom_range = 0.1,
rotation_range=15)



train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size = (img_height, img_width),
batch_size = batch_size, 
class_mode = 'categorical')  #class_mode = 'categorical'


# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)

# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(12, activation='softmax')(x)

# this is the model we will train
model = Model(input=base_model.input, output=predictions)

# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
    layer.trainable = False

# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# train the model on the new data for a few epochs
model.fit_generator(
train_generator,
steps_per_epoch = 5,
epochs = epochs)


# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.

# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
   print(i, layer.name)

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 172 layers and unfreeze the rest:
for layer in model.layers[:172]:
   layer.trainable = False
for layer in model.layers[172:]:
   layer.trainable = True

# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')

# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(
train_generator,
steps_per_epoch = 5,
epochs = epochs)

输出(不能超过第一个纪元):

 Epoch 1/5

1/5 [=====>........................] - ETA: 8s - loss: 2.4869
2/5 [===========>..................] - ETA: 3s - loss: 5.5591
3/5 [=================>............] - ETA: 1s - loss: 6.6299

4/5 [=======================>......] - ETA: 0s - loss: 8.4925

它只是卡在这里。

更新:

我用 tensorflow 1.3(降级一个版本)和 Keras 2.03(最新的 pip 版本)创建了一个虚拟环境，但仍然有同样的问题。

更新 2

我不认为这是一个内存问题，就好像我在 epoch 内更改步骤一样——它会一直运行到最后一步，只是卡住。

所以一个纪元中有 30 步，它将运行到 29。

5 步，它会运行到第 4 步，然后挂起。

更新3

还按照 Keras API 中的建议尝试了第 249 层。

Answer 1

显然这是一个通过 Keras 更新修复的错误(但是，有些人仍然遇到这个问题)