Implementing a Neural Network
In this exercise we will develop a neural network with fullyconnected layers to perform classification, and test it out on the CIFAR10 dataset.


We will use the class TwoLayerNet
in the file cs231n/classifiers/neural_net.py
to represent instances of our network. The network parameters are stored in the instance variable self.params
where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation.


Forward pass: compute scores
Open the file cs231n/classifiers/neural_net.py
and look at the method TwoLayerNet.loss
. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters.
Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs.


Your scores:
[[0.81233741 1.27654624 0.70335995]
[0.17129677 1.18803311 0.47310444]
[0.51590475 1.01354314 0.8504215 ]
[0.15419291 0.48629638 0.52901952]
[0.00618733 0.12435261 0.15226949]]
correct scores:
[[0.81233741 1.27654624 0.70335995]
[0.17129677 1.18803311 0.47310444]
[0.51590475 1.01354314 0.8504215 ]
[0.15419291 0.48629638 0.52901952]
[0.00618733 0.12435261 0.15226949]]
Difference between your scores and correct scores:
3.68027204961e08
Forward pass: compute loss
In the same function, implement the second part that computes the data and regularizaion loss.


Difference between your loss and correct loss:
1.79412040779e13
Backward pass
Implement the rest of the function. This will compute the gradient of the loss with respect to the variables W1
, b1
, W2
, and b2
. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:


W1 max relative error: 3.669858e09
W2 max relative error: 3.440708e09
b2 max relative error: 3.865028e11
b1 max relative error: 2.738422e09
Train the network
To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function TwoLayerNet.train
and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement TwoLayerNet.predict
, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.
Once you have implemented the method, run the code below to train a twolayer network on toy data. You should achieve a training loss less than 0.2.


Final training loss: 0.0171496079387
Load the data
Now that you have implemented a twolayer network that passes gradient checks and works on toy data, it’s time to load up our favorite CIFAR10 data so we can use it to train a classifier on a real dataset.


Train data shape: (49000L, 3072L)
Train labels shape: (49000L,)
Validation data shape: (1000L, 3072L)
Validation labels shape: (1000L,)
Test data shape: (1000L, 3072L)
Test labels shape: (1000L,)
Train a network
To train our network we will use SGD with momentum. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate.


iteration 0 / 1000: loss 2.302954
iteration 100 / 1000: loss 2.302550
iteration 200 / 1000: loss 2.297648
iteration 300 / 1000: loss 2.259602
iteration 400 / 1000: loss 2.204170
iteration 500 / 1000: loss 2.118565
iteration 600 / 1000: loss 2.051535
iteration 700 / 1000: loss 1.988466
iteration 800 / 1000: loss 2.006591
iteration 900 / 1000: loss 1.951473
Validation accuracy: 0.287
Debug the training
With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn’t very good.
One strategy for getting insight into what’s wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.
Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized.




Tune your hyperparameters
What’s wrong?. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.
Tuning. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.
Approximate results. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.
Experiment: You goal in this exercise is to get as good of a result on CIFAR10 as you can, with a fullyconnected Neural Network. For every 1% above 52% on the Test set we will award you with one extra bonus point. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.).


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cs231n\classifiers\neural_net.py:104: RuntimeWarning: overflow encountered in exp
exp_scores = np.exp(scores)
cs231n\classifiers\neural_net.py:105: RuntimeWarning: invalid value encountered in divide
a2 = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
cs231n\classifiers\neural_net.py:107: RuntimeWarning: divide by zero encountered in log
correct_log_probs = np.log(a2[range(N), y])
cs231n\classifiers\neural_net.py:81: RuntimeWarning: invalid value encountered in maximum
a1 = np.maximum(0, z1)
cs231n\classifiers\neural_net.py:131: RuntimeWarning: invalid value encountered in less_equal
dhidden[z1 <= 0] = 0
cs231n\classifiers\neural_net.py:247: RuntimeWarning: invalid value encountered in maximum
a1 = np.maximum(0, z1) # pass through ReLU activation function
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best validation accuracy achieved during crossvalidation: 0.540000


Run on the test set
When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%.
We will give you extra bonus point for every 1% of accuracy above 52%.


Test accuracy: 0.531
Codes

