tensorflow - 在迁移学习 : InceptionV3 中，我的损失是 "nan"，准确度是 "0.0000e+00 "

Question

我正在研究迁移学习。我的用例是对两类图像进行分类。我使用 InceptionV3 对图像进行分类。在训练我的模型时，我在每个时期都会得到 nan 损失和 0.0000e+00 准确性。我使用 20 个 epoch，因为我的数据量很小:我有 1000 个图像用于训练，100 个图像用于测试，每批 5 条记录。

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

# 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)

x = Dense(512, activation='relu')(x)
x = Dense(32, activation='relu')(x)
# and a logistic layer -- we have 2 classes
predictions = Dense(1, activation='softmax')(x)

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


for layer in base_model.layers:
    layer.trainable = False

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
   layer.trainable = False
for layer in model.layers[249:]:
   layer.trainable = True

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory(
        'C:/Users/Desktop/Transfer/train/',
        target_size=(64, 64),
        batch_size=5,
        class_mode='binary')

test_set = test_datagen.flow_from_directory(
        'C:/Users/Desktop/Transfer/test/',
        target_size=(64, 64),
        batch_size=5,
        class_mode='binary')

model.fit_generator(
        training_set,
        steps_per_epoch=1000,
        epochs=20,
        validation_data=test_set,
        validation_steps=100)

Answer 1

听起来你的梯度正在爆炸。可能有以下几个原因:

• 检查您的输入是否正确生成。例如，使用 flow_from_directory
的 save_to_dir 参数
• 由于批量大小为 5，请将 steps_per_epoch 从 1000 修复为 1000/5=200
• 使用 sigmoid 激活而不是 softmax
• 在 Adam 中设置较低的学习率；为此，您需要单独创建优化器，例如 adam = Adam(0.0001) 并将其传递到 model.compile(..., optimizationr=adam)
• 尝试使用 VGG16 而不是 InceptionV3

当您尝试以上所有方法时，请告诉我们。