Deep Learning with TensorFlow

Credits: Forked from TensorFlow by Google

Setup

Refer to the setup instructions.

Exercise 3

Previously in 2_fullyconnected.ipynb, you trained a logistic regression and a neural network model.

The goal of this exercise is to explore regularization techniques.

In [ ]:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import cPickle as pickle
import numpy as np
import tensorflow as tf

First reload the data we generated in notmist.ipynb.

In [ ]:
pickle_file = 'notMNIST.pickle'

with open(pickle_file, 'rb') as f:
  save = pickle.load(f)
  train_dataset = save['train_dataset']
  train_labels = save['train_labels']
  valid_dataset = save['valid_dataset']
  valid_labels = save['valid_labels']
  test_dataset = save['test_dataset']
  test_labels = save['test_labels']
  del save  # hint to help gc free up memory
  print 'Training set', train_dataset.shape, train_labels.shape
  print 'Validation set', valid_dataset.shape, valid_labels.shape
  print 'Test set', test_dataset.shape, test_labels.shape
Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (18724, 28, 28) (18724,)

Reformat into a shape that's more adapted to the models we're going to train:

  • data as a flat matrix,
  • labels as float 1-hot encodings.
In [ ]:
image_size = 28
num_labels = 10

def reformat(dataset, labels):
  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
  # Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]
  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
  return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print 'Training set', train_dataset.shape, train_labels.shape
print 'Validation set', valid_dataset.shape, valid_labels.shape
print 'Test set', test_dataset.shape, test_labels.shape
Training set (200000, 784) (200000, 10)
Validation set (10000, 784) (10000, 10)
Test set (18724, 784) (18724, 10)
In [ ]:
def accuracy(predictions, labels):
  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
          / predictions.shape[0])

Problem 1

Introduce and tune L2 regularization for both logistic and neural network models. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. In TensorFlow, you can compue the L2 loss for a tensor t using nn.l2_loss(t). The right amount of regularization should improve your validation / test accuracy.



Problem 2

Let's demonstrate an extreme case of overfitting. Restrict your training data to just a few batches. What happens?



Problem 3

Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced during training, not evaluation, otherwise your evaluation results would be stochastic as well. TensorFlow provides nn.dropout() for that, but you have to make sure it's only inserted during training.

What happens to our extreme overfitting case?



Problem 4

Try to get the best performance you can using a multi-layer model! The best reported test accuracy using a deep network is 97.1%.

One avenue you can explore is to add multiple layers.

Another one is to use learning rate decay:

global_step = tf.Variable(0)  # count the number of steps taken.
learning_rate = tf.train.exponential_decay(0.5, step, ...)
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)