大多数的方法都用Encoder-Decoder框架作为一个引子来引出了Attention Model,拿一个Encoder-Decoder的分心模型例子来说,假如在做翻译,比如输入的是英文Tom chase Jerry,它会生成中文单词:“汤姆”,“追逐”,“杰瑞”。 在翻译“杰瑞”这个中文单词的时候,这三个英文单词对翻译“杰瑞”的贡献都一样,这就不是我
defadd_input_layer(self,):# 定义输入层xs变量 将xs三维数据转换成二维 #[None,n_steps,input_size]=>(batch*n_step,in_size)l_in_x=tf.reshape(self.xs,[-1,self.input_size],name='2_2D')#定义输入权重(in_size,cell_size)Ws_in=self._weight_variable([self.input_size,self.cell_size])#...
tmp_examples = [] for idx, example in enumerate(examples): seq, label = example # 将单词映射为字典索引的ID, 对于词典中没有的单词用[UNK]对应的ID进行替代 seq = [self.word2id_dict.get(word, self.word2id_dict['[UNK]']) for word in seq.split(" ")] label = int(label) tmp_exampl...
model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, y_train, epochs=100, batch_size=32) 3. XGBoost模型训练接下来,我们将使用XGBoost库构建XGBoost模型,并进行模型训练。```pythonimport xgboost as xgbfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics...
LSTM Example 首先先复习一下LSTM的内部构造,上面这张图和我之前文章里不太一样,但其实本质上都是一样的,不必纠结 假设传入到cell的input叫做$z$,操控input gate的信号记为$z_i$,控制forget gate的信号记为$z_f$,控制output gate的信号记为$z_o$,综合这些东西得到的output记为$a$,假设memory里面已经存了...
batch_size, num_channels, sequence_length)example_input=torch.randn(32,1,100)output=model(example...
First, we should create a new folder to store all the code being used in LSTM. $ mkdir code-input Create a LSTM model inside the directory. import torch from torch import nn class Rods(nn.Module): def __init__(self, dataset): ...
,htmeans the final output of the memory unit, controlled by the output gateotand the final cell statect. The LSTM model was constructed in Python 3.5 and was supported by modules including Tensorflow, Numpy, and Pandas. Model design In order to identify the risk meteorological factors of the...
in range(200): # 用seq预测res (序列-seq 结果-res 输入-xs) seq, res, xs = get_batch() # 第一步赋值 之后会更新cell_init_state if i == 0: feed_dict = { model.xs: seq, model.ys: res, # create initial state (前面cell_init_state已初始化state) } else: feed_dict = { model...
# 训练模型model.fit(X, y, epochs=50, batch_size=32)# 预测未来10周的销售数据future_dates = pd.date_range(start='2023-10-02', periods=10, freq='W')future_sales = []for i in range(10):input_data = sales_scaled[-10:].reshape(1, 10, 1)predicted_sales = model.predict(input_...