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,这就不是我想要的...
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...
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])#...
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...
(1 for univariate forecasting) n_hidden: number of neurons in each hidden layer n_outputs: number of outputs to predict for each training example n_deep_layers: number of hidden dense layers after the lstm layer sequence_len: number of steps to look back at for prediction dropout: float (...
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): ...
Dense(256, activation='relu')) 添加普通的全连接层 model.add(Dropout(0.5)) model.add(Dense...
fetches["eval_op"] = eval_op#传入的是m.train_op,即m内的张量self._train_op#以下的for循环,是为了计算perplexityforstepinrange(model.input.epoch_size):#次数为epoch_size#每次循环前,生成训练用的空feed_dictfeed_dict ={}fori, (c, h)inenumerate(model.initial_state): ...
新建python3文件——text_generation_lstm 一、import 相关模块并查看版本 importmatplotlib as mplimportmatplotlib.pyplot as plt#%matplotlib inlineimportnumpy as npimportsklearnimportpandas as pdimportosimportsysimporttimeimporttensorflow as tffromtensorflowimportkerasprint(tf.__version__)print(sys.version_info...
# 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_...