def __init__(self, *, use_dice=False): self.graph = tf.Graph() self.tensor_info = {} self.use_dice = use_dice with self.graph.as_default(): # Main Inputs with tf.name_scope('Main_Inputs'): self.target_ph = tf.placeholder(tf.float32, [None, None], name='target_ph') self.lr_ph = tf.placeholder(tf.float32, [], name="lr_ph") # with tf.name_scope("Teacher_Info"): self.teacher_id_ph = tf.placeholder(tf.int32, [None, ], name="teacher_id_ph") self.student_count_ph = tf.placeholder(tf.int32, [None, ], name="student_count_ph") self.province_id_ph = tf.placeholder(tf.int32, shape=[None, ], name="province_id_ph") self.city_id_ph = tf.placeholder(tf.int32, shape=[None, ], name="city_id_ph") # TODO: binary 是否金牌讲师 self.core_type_ph = tf.placeholder(tf.int32, shape=[None, ], name="core_type_ph") # with tf.name_scope("Class_Info"): #今天 教的班级id self.class_id_ph = tf.placeholder(tf.int32, [None, ], name="class_id_ph") #课本版本 self.edition_id_ph = tf.placeholder(tf.int32, [None, ], name="edition_id_ph") self.grade_id_ph = tf.placeholder(tf.int32, [None, ], name="grade_id_ph") #老师教的所有班级 学生总人数, int连续特征 self.class_student_ph = tf.placeholder(tf.int32, [None, ], name="class_student_ph") #kefei #浮点连续特征 self.cap_avg_ph = tf.placeholder(tf.float32, [None, ], name="cap_avg_ph") self.cap_max_ph = tf.placeholder(tf.float32, [None, ], name="cap_max_ph") self.cap_min_ph = tf.placeholder(tf.float32, [None, ], name="cap_min_ph") # with tf.name_scope("Homework_Info"): #候选, 召回集 首先天宇会给一个初步刷选的作业集,好几组,每组进去很多题目,暂且不管 #粒度暂且放在一组,上,看做一个 作业, 特征属性两个chapters sections #这一组中,今天这个老师 id,,布置了某一组,则吧这个 label 为1 其他布置的 为0 这样就构造了样本 #另外chapters sections分别可能是几个数字®️的, 类似于多lable吧, 为了保持统一长度, 所以补零 self.today_chapters_ph = tf.placeholder(tf.int32, [None, None], name="today_chapters_ph") self.today_sections_ph = tf.placeholder(tf.int32, [None, None], name="today_sections_ph") #没用 self.today_chap_mask_ph = tf.placeholder(tf.float32, [None, None], name='today_chap_mask_ph') self.today_chap_len_ph = tf.placeholder(tf.int32, [None, ], name='today_chap_len_ph') self.today_sec_mask_ph = tf.placeholder(tf.float32, [None, None], name='today_sec_mask_ph') self.today_sec_len_ph = tf.placeholder(tf.int32, [None, ], name='today_sec_len_ph') #作业的风格 是这道题的 风格, 什么预习啊 什么深度啊,, self.today_style_ph = tf.placeholder(tf.int32, [None, ], name='today_style_ph') # TODO: use three dims to capture more history info #这个是 这个班级前三天 给布置的作业, 比如,昨天的,仍是两个特征来表征,chap sec 每个都是多个数字, #所以N N for fir in ['one', 'two', 'three', 'four','five','six','seven','eight','nine','ten','eleven','twelve','thirteen','fourteen']: key_s = "history_" + fir + "_sec_ph" sty = "style_" + fir + "_ph" key_c = "history_" + fir + "_chap_ph" setattr(self, key_c, tf.placeholder(tf.int32, [None, None], name=key_c)) setattr(self, key_s, tf.placeholder(tf.int32, [None, None], name=key_s)) setattr(self, sty, tf.placeholder(tf.int32, [None,], name=sty)) # TODO: All belows should consider the type and input # with tf.name_scope("Study_Info"): # TODO: study_vector_ph's type can change? #kefei 这个班级的学习能力 类似于期中考试, 这个班级 表征为 20维的向量 int self.study_vector_ph = tf.placeholder(tf.float32, [None, 20], name="study_vector_ph") #上面的结果 什么时候评测的 连续值,隔的天数 self.gap_days_ph = tf.placeholder(tf.int32, [None, ], name="gap_days_ph") self.analysis_avg_times_ph = tf.placeholder(tf.float32, [None, ], name="analysis_avg_times_ph") self.analysis_avg_rate_ph = tf.placeholder(tf.float32, [None, ], name="analysis_avg_rate_ph") self.analysis_avg_exp_score_ph = tf.placeholder(tf.float32, [None, ], name="analysis_avg_exp_score_ph") self.analysis_avg_exp_level_ph = tf.placeholder(tf.float32, [None, ], name="analysis_avg_exp_level_ph") # with tf.name_scope("Submit_Info"): 这个班级 一个月的app内 作业提交率 ,连续float特征 self.month_submit_rate_ph = tf.placeholder(tf.float32, [None, ], name="month_submit_rate_ph") # with tf.name_scope("Capacity_Info"): 地区区域整体能力 也是 float连续特征 self.region_capacity_ph = tf.placeholder(tf.float32, [None, ], name="region_capacity_ph") # with tf.name_scope("Prefer_Info"): #老师 在这个班级 上,,喜欢布置作业的 难度 和时间 float连续值特征 self.prefer_assign_time_avg_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_time_avg_ph") self.prefer_assign_time_var_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_time_var_ph") self.prefer_assign_rank_avg_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_rank_avg_ph") self.prefer_assign_rank_var_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_rank_var_ph") # with tf.name_scope("Register_Info"): 老师 注册app的 时间,int连续值特征 self.register_diff_ph = tf.placeholder(tf.int32, [None, ], name="register_diff_ph") # with tf.name_scope("HomeworkCount_Info"): 老师 布置了多少题目 int连续值特征 #是总共布置的吗 从注册app??? self.homework_count_ph = tf.placeholder(tf.int32, [None, ], name="homework_count_ph") # with tf.name_scope("Style_Info"): # TODO: use 3 dims 老师 作业 的风格 ? 一个特征域 # for fir in ["1", "2", "3", "4"]: # for sec in ["100", "010", "001", "110", "101", "011", "111"]: # key = "style_" + fir + "0" + sec + "_ph" # setattr(self, key, # tf.placeholder(tf.int32, [None, ], name=key)) # with tf.name_scope("WeekHomeworkCount_Info"): #这周 老师布置作业,,作业率,,怎么float ?? 连续值特征 self.week_count_ph = tf.placeholder(tf.float32, [None, ], name="week_count_ph") # with tf.name_scope("Reflect_Info"): # TODO: explore more graceful 映射 作业类目 self.reflect_value_ph = tf.placeholder(tf.int32, [None, None], name="reflect_value_ph") self.reflect_mask_ph = tf.placeholder(tf.float32, [None, None], name="reflect_mask_ph") self.reflect_len_ph = tf.placeholder(tf.int32, [None, ], name="reflect_len_ph") # with tf.name_scope("Lastdat_Info"): 昨天布置的 个数 int连续实值 self.lastday_count_ph = tf.placeholder(tf.int32, [None, ], name="lastday_count_ph") # Embedding layer with tf.name_scope('Main_Embedding_layer'): # almost done with tf.name_scope("Others"): # teacher with tf.name_scope("Teacher"): self.teacher_id_embeddings_var = tf.get_variable("teacher_id_embeddings_var", [N_TEACHER, EMBEDDING_DIM], ) # tf.summary.histogram('teacher_id_embeddings_var', self.teacher_id_embeddings_var) self.teacher_id_embedded = tf.nn.embedding_lookup(self.teacher_id_embeddings_var, self.teacher_id_ph, ) self.province_id_embeddings_var = tf.get_variable("province_id_embeddings_var", [N_PROVINCE, EMBEDDING_DIM]) # tf.summary.histogram('province_id_embeddings_var', self.province_id_embeddings_var) self.province_id_embedded = tf.nn.embedding_lookup(self.province_id_embeddings_var, self.province_id_ph) self.city_id_embeddings_var = tf.get_variable("city_id_embeddings_var", [N_CITY, EMBEDDING_DIM]) # tf.summary.histogram('city_id_embeddings_var', self.city_id_embeddings_var) self.city_id_embedded = tf.nn.embedding_lookup(self.city_id_embeddings_var, self.city_id_ph) self.core_type_embeddings_var = tf.get_variable("core_type_embeddings_var", [2, EMBEDDING_DIM]) # tf.summary.histogram('core_type_embeddings_var', self.core_type_embeddings_var) self.core_type_embedded = tf.nn.embedding_lookup(self.core_type_embeddings_var, self.core_type_ph) # just to use embedded for var,maybe tf.identify? self.student_count_embedded = get_self_or_expand_dims(self.student_count_ph) with tf.name_scope("Class"): self.class_id_embeddings_var = tf.get_variable("class_id_embeddings_var", [N_CLASS, EMBEDDING_DIM]) # tf.summary.histogram('class_id_embeddings_var', self.class_id_embeddings_var) self.class_id_embedded = tf.nn.embedding_lookup(self.class_id_embeddings_var, self.class_id_ph) self.edition_id_embeddings_var = tf.get_variable("edition_id_embeddings_var", [N_EDITION, EMBEDDING_DIM]) # tf.summary.histogram('edition_id_embeddings_var', self.edition_id_embeddings_var) self.edition_id_embedded = tf.nn.embedding_lookup(self.edition_id_embeddings_var, self.edition_id_ph) self.grade_id_embeddings_var = tf.get_variable("grade_id_embeddings_var", [N_GRADE, EMBEDDING_DIM]) # tf.summary.histogram('grade_id_embeddings_var', self.grade_id_embeddings_var) self.grade_id_embedded = tf.nn.embedding_lookup(self.grade_id_embeddings_var, self.grade_id_ph) # just to use embedded for var,maybe tf.identify? #连续值 dense 本身有意义的直接喂入 self.class_student_embedded = get_self_or_expand_dims(self.class_student_ph) self.cap_avg_embedded = get_self_or_expand_dims(self.cap_avg_ph) self.cap_max_embedded = get_self_or_expand_dims(self.cap_max_ph) self.cap_min_embedded = get_self_or_expand_dims(self.cap_min_ph) with tf.name_scope("Study"): # just to use embedded for var,maybe tf.identify? self.study_vector_embedded = self.study_vector_ph self.gap_days_embedded = get_self_or_expand_dims(self.gap_days_ph) with tf.name_scope("Study_analysis"): self.analysis_avg_times_embedded = get_self_or_expand_dims(self.analysis_avg_times_ph) self.analysis_avg_rate_embedded = get_self_or_expand_dims(self.analysis_avg_rate_ph) self.analysis_avg_exp_score_embedded = get_self_or_expand_dims(self.analysis_avg_exp_score_ph) self.analysis_avg_exp_level_embedded = get_self_or_expand_dims(self.analysis_avg_exp_level_ph) with tf.name_scope("Submit"): # just to use embedded for var,maybe tf.identify? self.month_submit_rate_embedded = get_self_or_expand_dims(self.month_submit_rate_ph) with tf.name_scope("Capacity"): # just to use embedded for var,maybe tf.identify? self.region_capacity_embedded = get_self_or_expand_dims(self.region_capacity_ph) with tf.name_scope("Prefer"): # just to use embedded for var,maybe tf.identify? self.prefer_assign_time_avg_embedded = get_self_or_expand_dims( self.prefer_assign_time_avg_ph) self.prefer_assign_time_var_embedded = get_self_or_expand_dims( self.prefer_assign_time_var_ph) self.prefer_assign_rank_avg_embedded = get_self_or_expand_dims( self.prefer_assign_rank_avg_ph) self.prefer_assign_rank_var_embedded = get_self_or_expand_dims( self.prefer_assign_rank_var_ph) with tf.name_scope("Register"): self.register_diff_embedded = get_self_or_expand_dims(self.register_diff_ph) with tf.name_scope("HomeworkCount"): self.homework_count_embedded = get_self_or_expand_dims(self.homework_count_ph) with tf.name_scope("WeekHomeworkCount"): self.week_count_embedded = get_self_or_expand_dims(self.week_count_ph) with tf.name_scope("Lastday"): self.lastday_count_embedded = get_self_or_expand_dims(self.lastday_count_ph) # TODO: homework and reflect and style # with tf.name_scope("Style"): # for fir in ["1", "2", "3", "4"]: # for sec in ["100", "010", "001", "110", "101", "011", "111"]: # key = "style_" + fir + "0" + sec + "_ph" # embed_key = "style_" + fir + "0" + sec + "_embedded" # setattr(self, embed_key, # get_self_or_expand_dims(getattr(self, key))) # homework with tf.name_scope("Homework"): self.style_embeddings_var = tf.get_variable("style_embeddings_var", [N_STYLE, EMBEDDING_DIM]) self.chapters_embeddings_var = tf.get_variable("chapters_embeddings_var", [N_CHAPTER, EMBEDDING_DIM]) self.sections_embeddings_var = tf.get_variable("sections_embeddings_var", [N_SECTION, EMBEDDING_DIM]) # tf.summary.histogram('homework_embeddings_var', self.homework_embeddings_var) self.today_chapters_embedded = get_mask_zero_embedded(self.chapters_embeddings_var, self.today_chapters_ph) self.today_sections_embedded = get_mask_zero_embedded(self.sections_embeddings_var, self.today_sections_ph) self.history_chap_embedded = get_history_sum_embedded(self) self.today_style_embedded = tf.nn.embedding_lookup(self.style_embeddings_var, self.today_style_ph) self.today_cha_rnn = get_rnn_sum(self.today_chapters_embedded,"rnncha") self.today_sec_rnn = get_rnn_sum(self.today_sections_embedded,"rnnsec") # reflect with tf.name_scope("Reflect"): self.reflect_embeddings_var = tf.get_variable("reflect_embeddings_var", [N_REFLECT, EMBEDDING_DIM]) # tf.summary.histogram('reflect_embeddings_var', self.reflect_embeddings_var) self.reflect_value_embedded = get_mask_zero_embedded(self.reflect_embeddings_var, self.reflect_value_ph) self.ref_rnn = get_rnn_sum(self.reflect_value_embedded,"ref")
def __init__(self, *, use_dice=False): self.graph = tf.Graph() self.tensor_info = {} self.use_dice = use_dice with self.graph.as_default(): # Main Inputs with tf.name_scope('Main_Inputs'): self.target_ph = tf.placeholder(tf.float32, [None, None], name='target_ph') self.lr_ph = tf.placeholder(tf.float32, [], name="lr_ph") # with tf.name_scope("Teacher_Info"): self.teacher_id_ph = tf.placeholder(tf.int32, [None, ], name="teacher_id_ph") self.student_count_ph = tf.placeholder(tf.int32, [None, ], name="student_count_ph") self.province_id_ph = tf.placeholder(tf.int32, shape=[None, ], name="province_id_ph") self.city_id_ph = tf.placeholder(tf.int32, shape=[None, ], name="city_id_ph") # TODO: binary self.core_type_ph = tf.placeholder(tf.int32, shape=[None, ], name="core_type_ph") # with tf.name_scope("Class_Info"): self.class_id_ph = tf.placeholder(tf.int32, [None, ], name="class_id_ph") self.edition_id_ph = tf.placeholder(tf.int32, [None, ], name="edition_id_ph") self.grade_id_ph = tf.placeholder(tf.int32, [None, ], name="grade_id_ph") self.class_student_ph = tf.placeholder(tf.int32, [None, ], name="class_student_ph") self.cap_avg_ph = tf.placeholder(tf.float32, [None, ], name="cap_avg_ph") self.cap_max_ph = tf.placeholder(tf.float32, [None, ], name="cap_max_ph") self.cap_min_ph = tf.placeholder(tf.float32, [None, ], name="cap_min_ph") # with tf.name_scope("Homework_Info"): self.today_chapters_ph = tf.placeholder(tf.int32, [None, None], name="today_chapters_ph") self.today_sections_ph = tf.placeholder(tf.int32, [None, None], name="today_sections_ph") self.today_chap_mask_ph = tf.placeholder(tf.float32, [None, None], name='today_chap_mask_ph') self.today_chap_len_ph = tf.placeholder(tf.int32, [None, ], name='today_chap_len_ph') self.today_sec_mask_ph = tf.placeholder(tf.float32, [None, None], name='today_sec_mask_ph') self.today_sec_len_ph = tf.placeholder(tf.int32, [None, ], name='today_sec_len_ph') self.today_style_ph = tf.placeholder(tf.int32, [None, ], name='today_style_ph') # TODO: use three dims to capture more history info for fir in ['one', 'two', 'three', 'four','five','six','seven','eight','nine','ten','eleven','twelve','thirteen','fourteen']: key = "history_" + fir + "_chap_ph" setattr(self, key, tf.placeholder(tf.int32, [None, None], name=key)) for fir in ['one', 'two', 'three', 'four','five','six','seven','eight','nine','ten','eleven','twelve','thirteen','fourteen']: key = "history_" + fir + "_sec_ph" setattr(self, key, tf.placeholder(tf.int32, [None, None], name=key)) # TODO: All belows should consider the type and input # with tf.name_scope("Study_Info"): # TODO: study_vector_ph's type can change? self.study_vector_ph = tf.placeholder(tf.float32, [None, 20], name="study_vector_ph") self.gap_days_ph = tf.placeholder(tf.int32, [None, ], name="gap_days_ph") self.month_submit_rate_ph = tf.placeholder(tf.float32, [None, ], name="month_submit_rate_ph") # with tf.name_scope("Capacity_Info"): self.region_capacity_ph = tf.placeholder(tf.float32, [None, ], name="region_capacity_ph") # with tf.name_scope("Prefer_Info"): self.prefer_assign_time_avg_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_time_avg_ph") self.prefer_assign_time_var_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_time_var_ph") self.prefer_assign_rank_avg_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_rank_avg_ph") self.prefer_assign_rank_var_ph = tf.placeholder(tf.float32, [None, ], name="prefer_assign_rank_var_ph") # with tf.name_scope("Register_Info"): self.register_diff_ph = tf.placeholder(tf.int32, [None, ], name="register_diff_ph") # with tf.name_scope("HomeworkCount_Info"): self.homework_count_ph = tf.placeholder(tf.int32, [None, ], name="homework_count_ph") for fir in ["1", "2", "3", "4"]: for sec in ["100", "010", "001", "110", "101", "011", "111"]: key = "style_" + fir + "0" + sec + "_ph" setattr(self, key, tf.placeholder(tf.int32, [None, ], name=key)) # with tf.name_scope("WeekHomeworkCount_Info"): self.week_count_ph = tf.placeholder(tf.float32, [None, ], name="week_count_ph") # with tf.name_scope("Reflect_Info"): # TODO: explore more graceful self.reflect_value_ph = tf.placeholder(tf.int32, [None, None], name="reflect_value_ph") self.reflect_mask_ph = tf.placeholder(tf.float32, [None, None], name="reflect_mask_ph") self.reflect_len_ph = tf.placeholder(tf.int32, [None, ], name="reflect_len_ph") # with tf.name_scope("Lastdat_Info"): self.lastday_count_ph = tf.placeholder(tf.int32, [None, ], name="lastday_count_ph") # Embedding layer with tf.name_scope('Main_Embedding_layer'): # almost done with tf.name_scope("Others"): # teacher with tf.name_scope("Teacher"): self.teacher_id_embeddings_var = tf.get_variable("teacher_id_embeddings_var", [N_TEACHER, EMBEDDING_DIM], ) # tf.summary.histogram('teacher_id_embeddings_var', self.teacher_id_embeddings_var) self.teacher_id_embedded = tf.nn.embedding_lookup(self.teacher_id_embeddings_var, self.teacher_id_ph, ) self.province_id_embeddings_var = tf.get_variable("province_id_embeddings_var", [N_PROVINCE, EMBEDDING_DIM]) # tf.summary.histogram('province_id_embeddings_var', self.province_id_embeddings_var) self.province_id_embedded = tf.nn.embedding_lookup(self.province_id_embeddings_var, self.province_id_ph) self.city_id_embeddings_var = tf.get_variable("city_id_embeddings_var", [N_CITY, EMBEDDING_DIM]) # tf.summary.histogram('city_id_embeddings_var', self.city_id_embeddings_var) self.city_id_embedded = tf.nn.embedding_lookup(self.city_id_embeddings_var, self.city_id_ph) self.core_type_embeddings_var = tf.get_variable("core_type_embeddings_var", [2, EMBEDDING_DIM]) # tf.summary.histogram('core_type_embeddings_var', self.core_type_embeddings_var) self.core_type_embedded = tf.nn.embedding_lookup(self.core_type_embeddings_var, self.core_type_ph) # just to use embedded for var,maybe tf.identify? self.student_count_embedded = get_self_or_expand_dims(self.student_count_ph) with tf.name_scope("Class"): self.class_id_embeddings_var = tf.get_variable("class_id_embeddings_var", [N_CLASS, EMBEDDING_DIM]) # tf.summary.histogram('class_id_embeddings_var', self.class_id_embeddings_var) self.class_id_embedded = tf.nn.embedding_lookup(self.class_id_embeddings_var, self.class_id_ph) self.edition_id_embeddings_var = tf.get_variable("edition_id_embeddings_var", [N_EDITION, EMBEDDING_DIM]) # tf.summary.histogram('edition_id_embeddings_var', self.edition_id_embeddings_var) self.edition_id_embedded = tf.nn.embedding_lookup(self.edition_id_embeddings_var, self.edition_id_ph) self.grade_id_embeddings_var = tf.get_variable("grade_id_embeddings_var", [N_GRADE, EMBEDDING_DIM]) # tf.summary.histogram('grade_id_embeddings_var', self.grade_id_embeddings_var) self.grade_id_embedded = tf.nn.embedding_lookup(self.grade_id_embeddings_var, self.grade_id_ph) # just to use embedded for var,maybe tf.identify? #连续值 dense 本身有意义的直接喂入 self.class_student_embedded = get_self_or_expand_dims(self.class_student_ph) self.cap_avg_embedded = get_self_or_expand_dims(self.cap_avg_ph) self.cap_max_embedded = get_self_or_expand_dims(self.cap_max_ph) self.cap_min_embedded = get_self_or_expand_dims(self.cap_min_ph) with tf.name_scope("Study"): # just to use embedded for var,maybe tf.identify? self.study_vector_embedded = self.study_vector_ph self.gap_days_embedded = get_self_or_expand_dims(self.gap_days_ph) with tf.name_scope("Submit"): # just to use embedded for var,maybe tf.identify? self.month_submit_rate_embedded = get_self_or_expand_dims(self.month_submit_rate_ph) with tf.name_scope("Capacity"): # just to use embedded for var,maybe tf.identify? self.region_capacity_embedded = get_self_or_expand_dims(self.region_capacity_ph) with tf.name_scope("Prefer"): # just to use embedded for var,maybe tf.identify? self.prefer_assign_time_avg_embedded = get_self_or_expand_dims( self.prefer_assign_time_avg_ph) self.prefer_assign_time_var_embedded = get_self_or_expand_dims( self.prefer_assign_time_var_ph) self.prefer_assign_rank_avg_embedded = get_self_or_expand_dims( self.prefer_assign_rank_avg_ph) self.prefer_assign_rank_var_embedded = get_self_or_expand_dims( self.prefer_assign_rank_var_ph) with tf.name_scope("Register"): self.register_diff_embedded = get_self_or_expand_dims(self.register_diff_ph) with tf.name_scope("HomeworkCount"): self.homework_count_embedded = get_self_or_expand_dims(self.homework_count_ph) with tf.name_scope("WeekHomeworkCount"): self.week_count_embedded = get_self_or_expand_dims(self.week_count_ph) with tf.name_scope("Lastday"): self.lastday_count_embedded = get_self_or_expand_dims(self.lastday_count_ph) # TODO: homework and reflect and style with tf.name_scope("Style"): for fir in ["1", "2", "3", "4"]: for sec in ["100", "010", "001", "110", "101", "011", "111"]: key = "style_" + fir + "0" + sec + "_ph" embed_key = "style_" + fir + "0" + sec + "_embedded" setattr(self, embed_key, get_self_or_expand_dims(getattr(self, key))) # homework with tf.name_scope("Homework"): self.style_embeddings_var = tf.get_variable("style_embeddings_var", [N_STYLE, EMBEDDING_DIM]) self.chapters_embeddings_var = tf.get_variable("chapters_embeddings_var", [N_CHAPTER, EMBEDDING_DIM]) self.sections_embeddings_var = tf.get_variable("sections_embeddings_var", [N_SECTION, EMBEDDING_DIM]) # tf.summary.histogram('homework_embeddings_var', self.homework_embeddings_var) self.today_chapters_embedded = get_mask_zero_embedded(self.chapters_embeddings_var, self.today_chapters_ph) self.today_sections_embedded = get_mask_zero_embedded(self.sections_embeddings_var, self.today_sections_ph) self.history_chap_embedded,self.history_sec_embedded = get_history_bgru_embedded(self) self.today_style_embedded = tf.nn.embedding_lookup(self.style_embeddings_var, self.today_style_ph) # reflect with tf.name_scope("Reflect"): self.reflect_embeddings_var = tf.get_variable("reflect_embeddings_var", [N_REFLECT, EMBEDDING_DIM]) # tf.summary.histogram('reflect_embeddings_var', self.reflect_embeddings_var) self.reflect_value_embedded = get_mask_zero_embedded(self.reflect_embeddings_var, self.reflect_value_ph)