user, item, _, seq, _ = data.next_batch() # data.step += 1 user = user.to(device) # (batch_size,) item = item.to(device) text = seq[:, :1].to(device) # bos, (batch_size, 1) for idx in range(seq.size(1)): # produce a word at each step outputs = model(user, item, text, None) last_token = outputs.logits[:, -1, :] # the last token, (batch_size, ntoken) word_prob = torch.softmax(last_token, dim=-1) token = torch.argmax(word_prob, dim=1, keepdim=True) # (batch_size, 1), pick the one with the largest probability text = torch.cat([text, token], 1) # (batch_size, len++) ids = text[:, 1:].tolist() # remove bos, (batch_size, seq_len) idss_predict.extend(ids) text = torch.cat([text, token], 1)

这是一行Python代码，其中data是一个数据集对象，next_batch()是数据集对象的一个方法，用于获取下一批数据。这行代码将返回一个元组，其中包含五个变量：user、item、_、seq和_。其中user和item是表示用户和物品的ID的向量，seq是表示用户历史行为序列的向量，_是一个占位符，表示该位置不需要使用。

相关问题

def model(self): num_classes = self.config.get("CNN_training_rule", "num_classes") seq_length = self.config.get("CNN_training_rule", "seq_length") conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") model_input = keras.layers.Input((seq_length,1), dtype='float64') # conv1形状[batch_size, seq_length, conv1_num_filters] conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input) conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1) max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2) full_connect = keras.layers.Dense(hidden_dim)(max_poolinged) droped = keras.layers.Dropout(dropout_keep_prob)(full_connect) relued = keras.layers.ReLU()(droped) model_output = keras.layers.Dense(num_classes, activation="softmax")(relued) model = keras.models.Model(inputs=model_input, outputs=model_output) # model.compile(loss="categorical_crossentropy", # optimizer="adam", # metrics=["accuracy"]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) return model给这段代码每行加上注释

def model(self):
    # 获取配置文件中的参数
    num_classes = self.config.get("CNN_training_rule", "num_classes") #分类数
    seq_length = self.config.get("CNN_training_rule", "seq_length") #序列长度
    conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") #第一层卷积核数量
    conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") #第一层卷积核大小
    conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") #第二层卷积核数量
    conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") #第二层卷积核大小
    hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") #全连接层隐藏层大小
    dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") #dropout保留率

    # 定义模型输入
    model_input = keras.layers.Input((seq_length,1), dtype='float64')

    # 第一层卷积
    conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input)

    # 第二层卷积
    conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1)

    # 全局最大池化
    max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2)

    # 全连接层
    full_connect = keras.layers.Dense(hidden_dim)(max_poolinged)

    # dropout层
    droped = keras.layers.Dropout(dropout_keep_prob)(full_connect)

    # relu激活层
    relued = keras.layers.ReLU()(droped)

    # 输出层
    model_output = keras.layers.Dense(num_classes, activation="softmax")(relued)

    # 定义模型
    model = keras.models.Model(inputs=model_input, outputs=model_output)

    # 编译模型
    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])

    # 打印模型结构
    print(model.summary())

    return model

seq_segment = [0] * len(fact_tokens_) seq_idx = self.tokenizer.convert_tokens_to_ids(fact_tokens_) seq_padding = [0] * (self.max_seq_len - len(seq_idx)) seq_mask = [1] * len(seq_idx) + seq_padding seq_idx = seq_idx + seq_padding seq_segment = seq_segment + seq_padding assert len(seq_idx) == self.max_seq_len assert len(seq_mask) == self.max_seq_len assert len(seq_segment) == self.max_seq_len token_id_full.append(seq_idx) token_id_full.append(seq_mask) token_id_full.append(seq_segment) labels_num = len(self.labels2id) labels_tensor = torch.FloatTensor(labels_num).fill_(0) if self.inference == False: for label in labels: labels_tensor[self.labels2id[label]] = 1 token_id_full.append(labels_tensor) contens.append(token_id_full) return contens

这段代码是用于准备模型的输入数据的。首先将输入的文本转换为token序列，然后对序列进行padding操作，使其长度为固定的max_seq_len。接着生成对应的mask和segment，将它们和token序列一起作为模型的输入。最后，如果是训练模式，则将标签转换为one-hot向量，作为模型的输出。整个过程将所有文本的数据整合在一起返回。