Deep Learning with PyTorch 1.x. by Laura Mitchell

Author:Laura Mitchell [Laura Mitchell]
Language: eng
Format: epub
Tags: COM016000 - COMPUTERS / Computer Vision and Pattern Recognition, COM004000 - COMPUTERS / Intelligence (AI) and Semantics, COM037000 - COMPUTERS / Machine Theory
Publisher: Packt
Published: 2019-11-29T08:22:57+00:00

Once the network is created, we can train the model using the same code as seen in the previous examples. The following is the code for training the model:

model = IMDBRnn(n_vocab,n_hidden,3,bs=32)

model = model.cuda()

optimizer = optim.Adam(model.parameters(),lr=1e-3)

def fit(epoch,model,data_loader,phase='training',volatile=False):

if phase == 'training':

model.train()

if phase == 'validation':

model.eval()

volatile=True

running_loss = 0.0

running_correct = 0

for batch_idx , batch in enumerate(data_loader):

text , target = batch.text , batch.label

if is_cuda:

text,target = text.cuda(),target.cuda()

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