本专栏由机器之心SOTA!模型资源站出品,每周日于机器之心公众号持续更新。 本专栏将逐一盘点自然语言处理、计算机视觉等领域下的常见任务,并对在这些任务上取得过 SOTA 的经典模型逐一详解。前往 SOTA!模型资源站(sota.jiqizhixin.com)即可获取本文中包含的模型实现代码、预训练模型及API等资源。 本文将分 3 期进行...
model=NNLM()criterion=nn.CrossEntropyLoss()optimizer=optim.Adam(model.parameters(),lr=0.001)input_batch,target_batch=make_batch()input_batch=torch.LongTensor(input_batch)target_batch=torch.LongTensor(target_batch)# Trainingforepochinrange(5000):optimizer.zero_grad()output=model(input_batch)# outpu...
target_batch = torch.LongTensor(target_batch) # Training for epoch in range(5000): optimizer.zero_grad() output = model(input_batch) # output : [batch_size, n_class], target_batch : [batch_size] loss = criterion(output, target_batch) if (epoch + 1) % 1000 == 0: print('Epoch:...
optimizer= tf.train.AdamOptimizer(0.001).minimize(cost) prediction=tf.argmax(model, 1)#Traininginit =tf.global_variables_initializer() sess=tf.Session() sess.run(init) input_batch, target_batch=make_batch(sentences)forepochinrange(10000): _, loss= sess.run([optimizer, cost], feed_dict={X...
本专栏由机器之心SOTA!模型资源站出品,每周日于机器之心公众号持续更新。本专栏将逐一盘点自然语言处理、计算机视觉等领域下的常见任务,并对在这些任务上取得过 SOTA 的经典模型逐一详解。前往 SOTA!模型资源站(sota.jiqizhixin.com)即可获取本文中包含的模型实现代码、预训练模型及 API 等资源。
target_batch = torch.LongTensor(target_batch)# Trainingforepochinrange(5000): optimizer.zero_grad() output = model(input_batch)# output : [batch_size, n_class], target_batch : [batch_size]loss = criterion(output, target_batch)if(epoch +1) %1000==0:print('Epoch:','%04d'% (epoch ...
Training is achieved by looking forθthat maximizes the training corpus penalized log-likelihood,where R(θ) is a regularization term 参考文献 http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdfwww.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf...
master src .ipynb_checkpoints __pycache__ data nnlm.py nnlm_word_embeddings.zh.npy preprocessing.py result.py test_similarity.py tr_20190102.txt training_result.txt 不得其死_similar_test.txt 受業_similar_test.txt 太保_similar_test.txt ...
输出层 我们要明确任务是通过一个文本序列(分词后的序列)去预测下一个字出现的概率,tensorflow代码如下: AI检测代码解析 import argparse import math import time import numpy as np import tensorflow as tf from datetime import date from preprocessing import TextLoader def main(): parser = argparse.ArgumentPa...
# Trainingforepoch inrange(5000):forbatch_x,batch_y in loader:optimizer.zero_grad()output=model(batch_x)# output : [batch_size, n_class], batch_y : [batch_size] (LongTensor, not one-hot)loss=criterion(output,batch_y)if(epoch+1)%1000==0:print('Epoch:','%04d'%(epoch+1),'cost...