def build_model(external_dim):
#创建模型时 首先指定进行组合时的参数 将配置分别放进不同的区域中（就是相近性的长度 ，周期性的长度等）
c_conf = (len_closeness, nb_flow, map_height,
map_width) if len_closeness > 0 else None
p_conf = (len_period, nb_flow, map_height,
map_width) if len_period > 0 else None
t_conf = (len_trend, nb_flow, map_height,
map_width) if len_period > 0 else None
'''
趋势性的数据暂时不要
t_conf = (len_trend, nb_flow, map_height,
map_width) if len_trend > 0 else None
'''
#根据不同的配置定义残差神经网络模型 这个stresnet是定义好的，传入关于不同方面的配置，最后会返回根据参数组合好的模型
model = stresnet(c_conf=c_conf,
p_conf=p_conf,
t_conf=t_conf,
external_dim=external_dim,
nb_residual_unit=nb_residual_unit)
adam = Adam(lr=lr) #接下来 定义学习率和损失函数值
model.compile(loss='mse', optimizer=adam, metrics=[metrics.rmse])
model.summary()
# from keras.utils.visualize_util import plot
# plot(model, to_file='model.png', show_shapes=True)
return model
def build_model(external_dim,
nb_residual_unit,
map_height=16,
map_width=8,
len_closeness=3,
len_period=1,
len_trend=1,
nb_flow=2,
lr=0.0001,
save_model_pic=False,
bn=False):
c_conf = (len_closeness, nb_flow, map_height,
map_width) if len_closeness > 0 else None
p_conf = (len_period, nb_flow, map_height,
map_width) if len_period > 0 else None
t_conf = (len_trend, nb_flow, map_height,
map_width) if len_trend > 0 else None
model = stresnet(c_conf=c_conf,
p_conf=p_conf,
t_conf=t_conf,
external_dim=external_dim,
nb_residual_unit=nb_residual_unit,
bn=bn,
bn2=bn)
# sgd = SGD(lr=lr, momentum=0.9, decay=5e-4, nesterov=True)
adam = Adam(lr=lr)
model.compile(loss='mse', optimizer=adam, metrics=[metrics.rmse])
# model.summary()
if (save_model_pic):
from keras.utils.vis_utils import plot_model
plot_model(model, to_file='TaxiBJ_model.png', show_shapes=True)
return model
def build_model(external_dim):
c_conf = (len_closeness, nb_flow, map_height,
map_width) if len_closeness > 0 else None
p_conf = (len_period, nb_flow, map_height,
map_width) if len_period > 0 else None
t_conf = (len_trend, nb_flow, map_height,
map_width) if len_trend > 0 else None
model = stresnet(c_conf=c_conf, p_conf=p_conf, t_conf=t_conf,
external_dim=external_dim, nb_residual_unit=nb_residual_unit)
adam = Adam(lr=lr)
model.compile(loss='mse', optimizer=adam, metrics=[metrics.rmse])
model.summary()
# from keras.utils.visualize_util import plot
# plot(model, to_file='model.png', show_shapes=True)
return model
def build_model(len_closeness, len_period, len_trend, nb_flow, map_height, map_width,
external_dim, nb_residual_unit, bn, bn2=False, save_model_pic=False, lr=0.00015):
c_conf = (len_closeness, nb_flow, map_height,
map_width) if len_closeness > 0 else None
p_conf = (len_period, nb_flow, map_height,
map_width) if len_period > 0 else None
t_conf = (len_trend, nb_flow, map_height,
map_width) if len_trend > 0 else None
model = stresnet(c_conf=c_conf, p_conf=p_conf, t_conf=t_conf,
external_dim=external_dim, nb_residual_unit=nb_residual_unit, bn=bn, bn2=bn2)
adam = Adam(lr=lr)
model.compile(loss='mse', optimizer=adam, metrics=[metrics.rmse])
# model.summary()
if (save_model_pic):
from keras.utils.vis_utils import plot_model
plot_model(model, to_file='TaxiNYC_model.png', show_shapes=True)
return model
