def model_graph(self, itemseq_input, train=True):
model_para = self.model_para
context_seq = itemseq_input[:, 0:-1]
label_seq = itemseq_input[:, 1:]
self.context_embedding = tf.nn.embedding_lookup(self.allitem_embeddings,
context_seq, name="context_embedding")
dilate_input = self.context_embedding
for layer_id, dilation in enumerate(model_para['dilations']):
dilate_input = ops.nextitnet_residual_block(dilate_input, dilation,
layer_id, model_para['dilated_channels'],
model_para['kernel_size'], causal=True, train=train)
return label_seq, dilate_input
def model_graph(self, itemseq_input, train=True):
model_para = self.model_para
context_seq = itemseq_input[:, 0:-1] #取所有行，除了最后一列
label_seq = itemseq_input[:, 1:] #取所有行，除了第一列
self.context_embedding = tf.nn.embedding_lookup(
self.allitem_embeddings, context_seq, name="context_embedding")
#就是根据train_inputs中的id，寻找embeddings中的对应元素。比如，train_inputs=[1,3,5]，则找出embeddings中下标为1,3,5的向量组成一个矩阵返回。
dilate_input = self.context_embedding
for layer_id, dilation in enumerate(model_para['dilations']):
dilate_input = ops.nextitnet_residual_block(
dilate_input,
dilation,
layer_id,
model_para['dilated_channels'],
model_para['kernel_size'],
causal=True,
train=train)
return label_seq, dilate_input
def model_graph(self, itemseq_input, train):
model_para = self.model_para
self.context_embedding = tf.nn.embedding_lookup(
self.allitem_embeddings, itemseq_input, name="context_embedding")
#positional embedding
if self.model_para['has_positionalembedding']:
pos_emb = self.embedding(tf.tile(
tf.expand_dims(tf.range(tf.shape(itemseq_input)[1]), 0),
[tf.shape(self.itemseq_input)[0], 1]),
max_position=model_para['max_position'],
num_units=self.embedding_width,
zero_pad=False,
scale=False,
l2_reg=0.0,
scope="dec_pos",
with_t=False)
dilate_input = tf.concat([self.context_embedding, pos_emb], -1)
else:
dilate_input = self.context_embedding
residual_channels = dilate_input.get_shape().as_list()[-1]
for layer_id, dilation in enumerate(model_para['dilations']):
dilate_input = ops.nextitnet_residual_block(
dilate_input,
dilation,
layer_id,
residual_channels,
model_para['kernel_size'],
causal=False,
train=train)
return dilate_input