Keras CNN的准确率和损失是恒定的

Question

我正在使用ResNet50构建一个使用迁移学习的keras CNN模型。由于某些原因，我的准确率和损失在每个时代都是完全相同的。奇怪的是，我在类似的代码中看到了同样的行为，但在VGG19中却是如此。这让我相信问题不在于实际的模型代码，而在于预处理过程中的某个地方。我尝试了调整学习率，改变优化器，图像分辨率，冻结图层等，但分数没有变化。我查看了我的图像目录，检查我的两个不同的类是否混合在一起，而它们没有。问题出在哪里？我只想提前说声谢谢。

附言:我正在对大约2000张图片进行培训，有两个课程。

import numpy as np
import pandas as pd

import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras.models import Sequential, Model, load_model
from keras.layers import Conv2D, GlobalAveragePooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import applications
from keras import optimizers

img_height, img_width, img_channel = 400, 400, 3 #change chanel to 1 instead of three since it is black and white

base_model = applications.ResNet50(weights='imagenet', include_top=False, input_shape=(img_height, img_width, img_channel))

# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(512, activation='relu',name='fc-1')(x)
#x = Dropout(0.5)(x)
x = Dense(256, activation='relu',name='fc-2')(x)
#x = Dropout(0.5)(x)
# and a logistic layer -- let's say we have 2 classes
predictions = Dense(1, activation='softmax', name='output_layer')(x)

model = Model(inputs=base_model.input, outputs=predictions)
model.compile(loss='binary_crossentropy', optimizer=optimizers.SGD(lr=0.1),
              metrics=['accuracy'])

model.summary()

from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint

batch_size = 6

# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
        rescale=1./255,
        rotation_range=20,
        width_shift_range=0.1,
        height_shift_range=0.1,
        shear_range=0.1,
        zoom_range=0.1,
        horizontal_flip=True,
        vertical_flip=True)


test_datagen = ImageDataGenerator(rescale=1./255)

#possibely resize the image
train_generator = train_datagen.flow_from_directory(
        "../Train/",
        target_size=(img_height, img_width),
        batch_size=batch_size,
        class_mode='binary',
        shuffle=True
)

validation_generator = test_datagen.flow_from_directory(
        "../Test/",
        target_size=(img_height, img_width),
        batch_size=batch_size,
        class_mode='binary',
        shuffle=True)

epochs = 10

history = model.fit_generator(
        train_generator,
        steps_per_epoch=2046 // batch_size,
        epochs=epochs,
        validation_data=validation_generator,
        validation_steps=512 // batch_size,
        callbacks=[ModelCheckpoint('snapshots/ResNet50-transferlearning.model', monitor='val_acc', save_best_only=True)])

这是keras给出的输出：

Epoch 1/10
341/341 [==============================] - 59s 172ms/step - loss: 7.0517 - acc: 0.5577 - val_loss: 7.0334 - val_acc: 0.5588
Epoch 2/10
341/341 [==============================] - 57s 168ms/step - loss: 7.0517 - acc: 0.5577 - val_loss: 7.0334 - val_acc: 0.5588
Epoch 3/10
341/341 [==============================] - 56s 165ms/step - loss: 7.0517 - acc: 0.5577 - val_loss: 7.0334 - val_acc: 0.5588
Epoch 4/10
341/341 [==============================] - 57s 168ms/step - loss: 7.0517 - acc: 0.5577 - val_loss: 7.0334 - val_acc: 0.5588
Epoch 5/10
341/341 [==============================] - 57s 167ms/step - loss: 7.0517 - acc: 0.5577 - val_loss: 7.0334 - val_acc: 0.5588

Answer 1

最后一层应该有一个'sigmoid‘激活，而不是softmax，因为它是二进制分类。

Answer 2

predictions = Dense(1, activation='softmax', name='output_layer')(x)

密集层表示您想要分类的不同类的数量，因此对于二进制分类，您将需要2，其中您已经编写了1。

因此，将该行更改为。

 predictions = Dense(2, activation='softmax', name='output_layer')(x)

只需注意，始终尝试保留一个变量来处理类的数量，例如

predictions = Dense(num_classes, activation='softmax', name='output_layer')(x)

然后在代码的开头定义num_classes，以获得更好的灵活性和可读性。

您可以在此处查看关于密集层的文档：https://faroit.github.io/keras-docs/2.0.0/layers/core/