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Download osrs macbook airThe training data loader loads data in batches.
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Also note that the class with the second highest probability is often closest animal in terms of appearance to the actual class among all the rest of 9 classes.
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We use the Adam optimizer.
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Below are some of the classification results on new test data that were not used in training or validation.
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So we chose the model from the epoch which had a higher accuracy and a lower loss.
This increases memory requirements.
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Download osrs macbook airThe early stopping process can also be automated.
Download osrs macbook airThe order of the data is also shuffled.
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We use the first 60 images in each of these categories for training, the next 10 images for validation and the rest for testing in our experiments below.
Download osrs macbook airGradient computation is done using the autograd and back propagation, differentiating in the graph using chain rule.
We share a python notebook with the complete code and share important snippets in this post so that the reader can understand how it works.
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An exponential of the model outputs provides us the class probabilities and then we choose the class with the highest probability as our output class.
Download osrs macbook airSo it is essential to zero them out at the beginning of the training loop.
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Download osrs macbook airThe inputs go through the forwards pass, followed by the loss and accuracy computations for the batch and at the end of the loop, for the whole epoch.
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Download osrs macbook airWe use transfer learning to use the low level image features like edges, textures etc.
Compute the total loss for the batch and add it to valid_loss.
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Download osrs macbook airNormalize takes in a 3 channel Tensor and normalizes each channel by the input mean and standard deviation for the channel.
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Download osrs macbook airValidation is carried out in each epoch immediately after the training loop.
Download osrs macbook airTotal loss and accuracy is computed for the whole batch, which is then averaged over all the batches to get the loss and accuracy values for the whole epoch.
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Mean and standard deviation vectors are input as 3 element vectors.
When we train for multiple epochs, the models gets to see more variations of the input images with a new randomized variation of the transformation in each epoch.
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Download osrs macbook airConvert model to be used on GPU.
Define Optimizer and Loss Function.
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The code can then be used to train the whole dataset too.
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Download osrs macbook airLet us see below how to find the output class for a given test image.
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We use the Negative Loss Likelihood function as it can be used for classifying multiple classes.
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Since we do not need any gradient computation in the validation process, it is done within a torch.
Download osrs macbook airCompute total accuracy in the whole batch and add to train_acc.
Download osrs macbook airAs we see below, the class predicted with highest probability is often the correct one.
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Download osrs macbook airAs we can see below, we now need to train only around a tenth of the total number of model parameters.
Download osrs macbook airSo finally we have 600 training images, 100 validation images, 409 test images and 10 classes of animals.
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Download osrs macbook airMaintaining a separate validation set is important, so that we can stop the training at the right point and prevent overfitting.
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Find average training loss and training accuracy.
Download osrs macbook airAndrej Karpathy, Senior Director of AI at Tesla, said the following in his tweet.
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We try to insert some variations by introducing some randomness into the transformations.
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Download osrs macbook airRepeat this for every animal.
Note that with transfer learning, we need to compute gradients only for a small set of parameters that belong to the few newly added layers towards the end of the model.
Download osrs macbook airIn a future post, we will apply the same transfer learning approach on harder datasets solving harder real life problems.
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Loss and Accuracy within the epoch.
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Download osrs macbook airAdam is one the most popular optimizers because it can adapt the learning rate for each parameter individually.
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