Keras模型不是训练层，验证精度始终为0.5

Question

我的Keras CNN模型（基于AlexNet的一个实现）的训练精度总是接近0.5（在+-0.02以内），验证精度总是精确的0.5。它是一个二进制分类模型，其中train/val的分割大约为85/15，并且在这两个集合中，图像对每个类进行50/50的分割。

sz=224 # image width = height = 224
batch_size=64
train_data_dir = r"./crack_dataset/train"
validation_data_dir = r"./crack_dataset/validate"
nb_train_samples = 3416
nb_val_samples = 612

train_datagen = ImageDataGenerator(rescale=1./255)

validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_data_dir,
                                                    target_size = (sz, sz),
                                                    batch_size=batch_size,
                                                    class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(validation_data_dir,
                                                              target_size = (sz, sz),
                                                              batch_size=batch_size,
                                                              class_mode='binary')

# Create Model 
model = Sequential()

model.add(Conv2D(filters=96, input_shape=input_shape, kernel_size=(11,11), strides=(4,4), padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))

model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))

model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid', activation='relu'))

model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid', activation='relu'))

model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))

model.add(Flatten())
model.add(Dense(4096, input_shape=(256,), activation='relu'))
model.add(Dropout(0.4))

model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.4))

model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer=Adam(0.1), loss='binary_crossentropy', metrics=['accuracy'])

model.fit_generator(train_generator,
                    steps_per_epoch = nb_train_samples // batch_size, 
                    epochs=30,
                    validation_data=validation_generator,
                    validation_steps=nb_val_samples // batch_size)

Answer 1

问题是优化器的学习率，这个值太大了。正如注释中所建议的，应该将其设置为一个低值，接近于0。

您可以在下图中看到学习率如何影响分类精度：