mobilenet_cfg = [['s1', 64], ['s2', 128], ['s1', 128], ['s2', 256], ['s1', 256], ['s2', 512], ['s1', 512], ['s1', 512],
                 ['s1', 512], ['s1', 512], ['s1', 512],
                 # ['s2',1024],['s2',1024]
                 ]
# 类别数
num_classes = 10

# #***实验对比条件***##
# 是否使用预训练模型
pretrained = False
# 是否使用数据增强
data_augmentation = True
# 权重参数初始化
# kernel_init =
# 权重正则化
kernel_regularizer = None
# 是否通过fc_cfg的'D'，来表示使用dropout ,如[256,'D',128,'D']
fc_cfg = [256, 128]
# batch_size
batch_size = 128
# 输入大小
input_shape_select = (32, 32, 3)
# 激活函数
activation = 'relu'
# BN层使用
batch_normalization = True
# 优化算法
optimizer = 'adam'
# 损失函数
loss_function = 'sparse_categorical_crossentropy'
# 评估指标
metrics = 'accuracy'

# weight_decay = 5e-4
# learning_rate = 1e-2
# dropout_rate = 0.5
# epoch_num = 200
