omarsar

!pip3 install torch torchvision

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GET _search
{
  "query": {
    "match_all": {}
  }
}


GET /

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GET blogs_fixed/_search
{
    "query": {
        "query_string" : {
            "query" : "(content:elasticsearch AND content:engineering) OR (title:elasticsearch AND title:engineering)"
        }
    }
}

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test_acc = 0.0
for i, (images, labels) in enumerate(testloader, 0):
    images = images.to(device)
    labels = labels.to(device)
    outputs = model(images)
    test_acc += get_accuracy(outputs, labels, BATCH_SIZE)
        
print('Test Accuracy: %.2f'%( test_acc/i))

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## train the model
for epoch in range(num_epochs):
    train_running_loss = 0.0
    train_acc = 0.0

    ## commence training
    model = model.train()

    ## training step
    for i, (images, labels) in enumerate(trainloader):

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## utility function to compute accuracy
def get_accuracy(output, target, batch_size):
    ''' Obtain accuracy for training round '''
    corrects = (torch.max(output, 1)[1].view(target.size()).data == target.data).sum()
    accuracy = 100.0 * corrects/batch_size
    return accuracy.item()

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learning_rate = 0.001
num_epochs = 5

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = MyModel()
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

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## test the model with 1 batch
model = MyModel()
for images, labels in trainloader:
    print("batch size:", images.shape)
    out = model(images)
    print(out.shape)
    break

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## the model
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.d1 = nn.Linear(28 * 28, 128)
        self.dropout = nn.Dropout(p=0.2)
        self.d2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = x.flatten(start_dim = 1)

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for images, labels in trainloader:
    print("Image batch dimensions:", images.shape)
    print("Image label dimensions:", labels.shape)
    break