FRESHLY CHECK CNN MATCHED.txt

FRESHLY CHECK CNN MATCHED.txt

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A newly initialized model shouldn't be too sure of itself, the output logits should all have similar magnitudes. To confirm this you can check that the exponential of the mean loss is approximately equal to the vocabulary size. A much higher loss means the model is sure of its wrong answers, and is badly initialized:

From there we begin looping over the filters and create a set of CONV => RELU > BN => POOL layers. Each iteration of the loop appends these layers. Be sure to check out Chapter 11 from the Starter Bundle of Deep Learning for Computer Vision with Python for more information on these layer types if you are unfamiliar.

On Lines 61 and 62, a check is made to see if the regression node should be appended; it is then added in accordingly. Again, we will not be conducting regression at the end of this branch either. Regression will be performed on the head of the multi-input, mixed data network (the very bottom of Figure 7).

At the start of the training, FasterRCNN will print the pretrained model loading status (per-layer).If facing with bad mAP with the model, we can double check this log to see if the pretrained modelis loaded properly or not.

Sometimes a training job can be interrupted due to some reason (e.g., system crash). In these cases,there is no need to redo the training from the start. We can resume the interrupted trainingfrom the last checkpoint(saved .tlt model during training). In this case, set theresume_from_model path in spec file to point to the last checkpoint and re-run the trainingto resume the job.

This verifies a few things. First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. In my case, I constantly make silly mistakes of doing Dense(1,activation='softmax') vs Dense(1,activation='sigmoid') for binary predictions, and the first one gives garbage results.

You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. This is especially useful for checking that your data is correctly normalized. As an example, if you expect your output to be heavily skewed toward 0, it might be a good idea to transform your expected outputs (your training data) by taking the square roots of the expected output. This will avoid gradient issues for saturated sigmoids, at the output.

Double check your input data. See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. Have a look at a few input samples, and the associated labels, and make sure they make sense. Check that the normalized data are really normalized (have a look at their range). Also, real-world datasets are dirty: for classification, there could be a high level of label noise (samples having the wrong class label) or for multivariate time series forecast, some of the time series components may have a lot of missing data (I've seen numbers as high as 94% for some of the inputs).

Finally, the best way to check if you have training set issues is to use another training set. If you're doing image classification, instead than the images you collected, use a standard dataset such CIFAR10 or CIFAR100 (or ImageNet, if you can afford to train on that). These data sets are well-tested: if your training loss goes down here but not on your original data set, you may have issues in the data set.

the opposite test: you keep the full training set, but you shuffle the labels. The only way the NN can learn now is by memorising the training set, which means that the training loss will decrease very slowly, while the test loss will increase very quickly. In particular, you should reach the random chance loss on the test set. This means that if you have 1000 classes, you should reach an accuracy of 0.1%. If you don't see any difference between the training loss before and after shuffling labels, this means that your code is buggy (remember that we have already checked the labels of the training set in the step before).

If the model isn't learning, there is a decent chance that your backpropagation is not working. But there are so many things can go wrong with a black box model like Neural Network, there are many things you need to check. I think Sycorax and Alex both provide very good comprehensive answers. Just want to add on one technique haven't been discussed yet.

A standard neural network is composed of layers. Before checking that the entire neural network can overfit on a training example, as the other answers suggest, it would be a good idea to first check that each layer, or group of layers, can overfit on specific targets.

For example, let $\alpha(\cdot)$ represent an arbitrary activation function, such that $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$ represents a classic fully-connected layer, where $\mathbf x \in \mathbb R^d$ and $\mathbf W \in \mathbb R^{k \times d}$. Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. Then, let $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$ be a loss function. Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. If the loss decreases consistently, then this check has passed.

Download this sample_cnn.csv file and upload it on the contest page to generate your results and check your ranking on the leaderboard. This will give you a benchmark solution to get you started with any Image Classification problem!

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Evaluating our trained model is very similar to what we did in section 2, where we evaluated the ZSL model. We call the model and generate candidate summaries and compare them to the reference summaries by calculating the ROUGE scores. But now, the model sits in Amazon S3 in a file called model.tar.gz (to find the exact location, you can check the training job on the console). So how do we access the model to generate summaries?

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