siamese_network, cnn = siamese_net(input_shape, l2_penalization,
learning_rate)
# cnnh5 = r'D:\SpyderWorkSpace\GUI_Ganerator\models\trained_cnn.h5'
cnn.load_weights(r'D:\SpyderWorkSpace\GUI_Ganerator\models\trained_cnn.h5')
c_size = 200
layer_output = K.function([cnn.layers[0].input],
[cnn.get_layer('Dense1').output])
cd_img = r'D:\zhu\chen1\data\pick8\p_app_resize_Td_sts_c_resize512_noui1'
txt_dir = r'D:\zhu\chen1\data\pick8\aTrees_dict_app'
file_csv = r'F:\2017\zhu\RicoDataset\app_details.csv'
st_dir = r'D:\zhu\chen1\data\pick8\p_app_resize_Td_sts'
path_file_name = r'D:\SpyderWorkSpace\GUI_Ganerator\data\categories_app_emb'
appsl, appsd = get_s_app(file_csv, st_dir) # 按照category中app的数量
for (cat, cat_apps) in appsd.items():
print('\ncategory: ', cat)
print('\ncategory_apps: ', cat_apps)
train_uis = []
for app in os.listdir(cd_img):
if app in cat_apps:
app_dir = os.path.join(cd_img, app)
for ui in os.listdir(app_dir):
ui_dir = os.path.join(app_dir, ui)
train_uis.append(ui_dir) # 需要embedding的subtrees
# 输入数据，生成embedding
c_num = len(train_uis) / c_size # 迭代次数
x_train_embedding = []
if __name__ == '__main__':
random.seed(SEED)
file_csv = r'app_details.csv'
cd_img = r'.\p_app_Td_sts_resized'
txt_img = r'.\aTrees_dict_app'
st_dir = r'.\p_app_Td_sts'
db_dir = r'.\st_bank_app'
emb_file = r'.\data\categories_app_emb'
m_save_path = r'.\models' # 3 loss
NEGATIVE_FILE = '.\samples'
appsl, appsd = get_s_app(file_csv, st_dir)
appsl1 = []
for (k,v) in appsd.items():
appsl1.append([k,len(v)])
appsl1 = sorted(appsl1, key=lambda x: x[1], reverse=True)
_ns = []
_n = 0; _ns.append(_n) # News & Magazines
mul_loss = True
c_apps = []
c_cats = []
for _n in _ns:
c_cat = appsl1[_n][0]
c_cats.append(c_cat)