def create_model(session, forward_only):
"""建立模型"""
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
model = s2s_model.S2SModel(data_utils.dim, data_utils.dim, buckets,
FLAGS.size, FLAGS.dropout, FLAGS.num_layers,
FLAGS.max_gradient_norm, FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.num_samples,
forward_only, dtype)
return model
def create_model(session, forward_only):
dtype = tf.float32
model = s2s_model.S2SModel(
data_utils.dim,
data_utils.dim,
buckets,
512,
1.0,
1,
5.0,
64,
0.0003,
512,
forward_only,
dtype
)
return model
def create_model(session, forward_only):
#forward_only即代表是否是训练还是预测，因为预测时只做向前传播训练时双向传播
"""建立模型"""
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
model = s2s_model.S2SModel(
data_utils.dim, #字典中字的总数
data_utils.dim,
buckets, #即那四个问答字数的桶，[(5,15),(10,20),(15,25),(20,30)]
FLAGS.size,
FLAGS.dropout,
FLAGS.num_layers, #纵向上2个lstm，横向上是不同时间状态的变化。
FLAGS.max_gradient_norm, #最大的梯度截断
FLAGS.batch_size, #64
FLAGS.learning_rate, #0.01
FLAGS.num_samples, #512
forward_only, #True则为训练，False则为测试
dtype)
return model
def create_model(session, forward_only):
"""建立模型"""
# 是否使用16位浮点数（默认32位）
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
model = s2s_model.S2SModel(
data_utils.dim, # dictionary.json 字典总长度6865
data_utils.dim,
buckets, # [(5, 15), (10, 20), (15, 25), (20, 30)]
FLAGS.size, # 512 LSTM每层神经元数量
FLAGS.dropout, # 1.0 每层输出DROPOUT的大小
FLAGS.num_layers, # 2 LSTM的层数
FLAGS.max_gradient_norm, # 5.0 梯度最大阈值
FLAGS.batch_size, # 64 批量梯度下降的批量大小
FLAGS.learning_rate, # 0.0003 学习率
FLAGS.num_samples, # 512 分批softmax的样本量
forward_only, # 是否仅仅前向传播 test时 true, train时false
dtype
)
return model
