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)