Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the assignments page on the course website.
In this exercise you will:
In [1]:
# Run some setup code for this notebook.
from __future__ import absolute_import, division, print_function
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
import seaborn
seaborn.set()
# This is a bit of magic to make matplotlib figures appear inline
# in the notebook rather than in a new window.
%matplotlib inline
# set default size of plots
plt.rcParams['figure.figsize'] = (10.0, 8.0)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# Some more magic so that the notebook will reload external python
# modules;
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2
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# Load the raw CIFAR-10 data.
cifar10_dir = '../data/cifar10'
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
# As a sanity check, we print out the size of the training and test data.
print('Training data shape: ', X_train.shape)
print('Training labels shape: ', y_train.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
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# Visualize some examples from the dataset.
# We show a few examples of training images from each class.
classes = ['plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
num_classes = len(classes)
samples_per_class = 7
for y, cls in enumerate(classes):
idxs = np.flatnonzero(y_train == y)
idxs = np.random.choice(idxs, samples_per_class, replace=False)
for i, idx in enumerate(idxs):
plt_idx = i * num_classes + y + 1
plt.subplot(samples_per_class, num_classes, plt_idx)
plt.imshow(X_train[idx].astype('uint8'))
plt.axis('off')
if i == 0:
plt.title(cls)
plt.show()
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# Split the data into train, val, and test sets. In addition we will
# create a small development set as a subset of the training data;
# we can use this for development so our code runs faster.
num_training = 49000
num_validation = 1000
num_test = 1000
num_dev = 500
# Our validation set will be num_validation points from the original
# training set.
mask = range(num_training, num_training + num_validation)
X_val = X_train[mask]
y_val = y_train[mask]
# Our training set will be the first num_train points from the
#original training set.
mask = range(num_training)
X_train = X_train[mask]
y_train = y_train[mask]
# We will also make a development set, which is a small subset of
# the training set.
mask = np.random.choice(num_training, num_dev, replace=False)
X_dev = X_train[mask]
y_dev = y_train[mask]
# We use the first num_test points of the original test set as our
# test set.
mask = range(num_test)
X_test = X_test[mask]
y_test = y_test[mask]
print('Train data shape:', X_train.shape)
print('Train labels shape:', y_train.shape)
print('Validation data shape:', X_val.shape)
print('Validation labels shape:', y_val.shape)
print('Test data shape:', X_test.shape)
print('Test labels shape:', y_test.shape)
print('Dev data shape:', X_dev.shape)
print('Dev labels shape:', y_dev.shape)
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# Preprocessing: reshape the image data into rows
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_val = np.reshape(X_val, (X_val.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))
# As a sanity check, print out the shapes of the data
print('Training data shape:', X_train.shape)
print('Validation data shape:', X_val.shape)
print('Test data shape:', X_test.shape)
print('Dev data shape:', X_dev.shape)
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# Preprocessing: subtract the mean image
# first: compute the image mean based on the training data
mean_image = np.mean(X_train, axis=0)
print(mean_image[:10]) # print a few of the elements
plt.figure(figsize=(4, 4))
# visualize the mean image
plt.axis('off')
plt.imshow(mean_image.reshape((32, 32, 3)).astype('uint8'))
plt.show()
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# second: subtract the mean image from train and test data
X_train -= mean_image
X_val -= mean_image
X_test -= mean_image
X_dev -= mean_image
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# third: append the bias dimension of ones (i.e. bias trick)
# so that our SVM only has to worry about optimizing a single
# weight matrix W.
X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])
X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])
X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])
X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])
print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)
In [9]:
# Evaluate the naive implementation of the loss we provided for you:
from cs231n.classifiers.linear_svm import svm_loss_naive
import time
# generate a random SVM weight matrix of small numbers
W = np.random.randn(3073, 10) * 0.0001
loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)
print('loss:', loss )
#print('grad:', grad)
The grad returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function svm_loss_naive. You will find it helpful to interleave your new code inside the existing function.
To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:
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# Once you've implemented the gradient, recompute it with the code
# below and gradient check it with the function we provided for you.
# Compute the loss and its gradient at W.
loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)
# Numerically compute the gradient along several randomly chosen
# dimensions, and compare them with your analytically computed
# gradient. The numbers should match almost exactly along all
# dimensions.
from cs231n.gradient_check import grad_check_sparse
f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]
grad_numerical = grad_check_sparse(f, W, grad)
print('-----------')
# Do the gradient check once again with regularization turned on
# you didn't forget the regularization gradient did you?
loss, grad = svm_loss_naive(W, X_dev, y_dev, 1e2)
f = lambda w: svm_loss_naive(w, X_dev, y_dev, 1e2)[0]
grad_numerical = grad_check_sparse(f, W, grad)
It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? Hint: the SVM loss function is not strictly speaking differentiable
Your Answer: fill this in.
In [11]:
# Next implement the function svm_loss_vectorized;
# for now only compute the loss;
# we will implement the gradient in a moment.
tic = time.time()
loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)
toc = time.time()
print('Naive loss: {:e} computed in {:f}s'.format(
loss_naive, toc - tic))
from cs231n.classifiers.linear_svm import svm_loss_vectorized
tic = time.time()
loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)
toc = time.time()
print('Vectorized loss: {:e} computed in {:f}s'.format(
loss_vectorized, toc - tic))
# The losses should match but your vectorized implementation
# should be much faster.
print('difference: {:f}'.format(loss_naive - loss_vectorized))
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# Complete the implementation of svm_loss_vectorized, and compute
# the gradient of the loss function in a vectorized way.
# The naive implementation and the vectorized implementation should
# match, but the vectorized version should still be much faster.
tic = time.time()
_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)
toc = time.time()
print('Naive loss and gradient: computed in {:f}s'.format(toc - tic))
tic = time.time()
_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)
toc = time.time()
print('Vectorized loss and gradient: computed in {:f}s'.format(
toc - tic))
# The loss is a single number, so it is easy to compare the values
# computed by the two implementations. The gradient on the other
# hand is a matrix, so we use the Frobenius norm to compare them.
difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')
print('difference: {:f}'.format(difference))
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# In the file linear_classifier.py, implement SGD in the function
# LinearClassifier.train() and then run it with the code below.
from cs231n.classifiers import LinearSVM
svm = LinearSVM()
tic = time.time()
loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=5e4,
num_iters=1500, verbose=True)
toc = time.time()
print('That took {:f}s'.format(toc - tic))
In [14]:
# A useful debugging strategy is to plot the loss as a function of
# iteration number:
plt.plot(loss_hist)
plt.xlabel('Iteration number')
plt.ylabel('Loss value')
plt.show()
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# Write the LinearSVM.predict function and evaluate the performance
# on both the training and validation set
y_train_pred = svm.predict(X_train)
print('training accuracy: {:f}'.format(
np.mean(y_train == y_train_pred)))
y_val_pred = svm.predict(X_val)
print('validation accuracy: {:f}'.format(
np.mean(y_val == y_val_pred)))
In [16]:
# Use the validation set to tune hyperparameters (regularization
# strength and learning rate). You should experiment with different
# ranges for the learning rates and regularization strengths;
# if you are careful you should be able to get a classification
# accuracy of about 0.4 on the validation set.
# [djn] prev best learning rate [-7, -6.8, -6.6] (.398)
learning_rates = 10 ** np.array(
[-6.81, -6.78, -6.75, -6.72, -6.69, -6.66])
regularization_strengths = 10 ** np.array(
[4.35, 4.38, 4.41, 4.45, 4.48])
#learning_rates = [1e-7, 5e-5]
#regularization_strengths = [5e4, 1e5]
# results is dictionary mapping tuples of the form
# (learning_rate, regularization_strength) to tuples of the form
# (training_accuracy, validation_accuracy). The accuracy is simply
# the fraction of data points that are correctly classified.
results = {}
# The highest validation accuracy that we have seen so far.
best_val = -1
# The LinearSVM object that achieved the highest validation rate.
best_svm = None
####################################################################
# TODO: #
# Write code that chooses the best hyperparameters by tuning on #
# the validation set. For each combination of hyperparameters, #
# train a linear SVM on the training set, compute its accuracy on #
# the training and validation sets, and store these numbers in the #
# results dictionary. In addition, store the best validation #
# accuracy in best_val and the LinearSVM object that achieves this #
# accuracy in best_svm. #
# #
# Hint: You should use a small value for num_iters as you develop #
# your validation code so that the SVMs don't take much time to #
# train; once you are confident that your validation code works, #
# you should rerun the validation code with a larger value for #
# num_iters.
####################################################################
import itertools
n_iters = 800
combinations = itertools.product(learning_rates,
regularization_strengths)
for lr, reg in combinations:
svm = LinearSVM()
svm.train(X_train, y_train, learning_rate=lr, reg=reg,
num_iters=n_iters)
y_train_pred = svm.predict(X_train)
y_val_pred = svm.predict(X_val)
train_accuracy = np.mean(y_train == y_train_pred)
val_accuracy = np.mean(y_val == y_val_pred)
results[(lr, reg)] = (train_accuracy, val_accuracy)
if val_accuracy > best_val:
best_val = val_accuracy
best_svm = svm
####################################################################
# END OF YOUR CODE #
####################################################################
# Print out results.
for lr, reg in sorted(results):
train_accuracy, val_accuracy = results[(lr, reg)]
print('[LR={:e}, REG={:e}]\n\t'
'train-accuracy: {:f}, '
'val-accuracy: {:f}'.format(
lr, reg, train_accuracy, val_accuracy))
print('Best validation accuracy achieved during cross-validation: '
'{:f}'.format(best_val))
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# Visualize the cross-validation results
import math
x_scatter = [math.log10(x[0]) for x in results]
y_scatter = [math.log10(x[1]) for x in results]
# plot training accuracy
cm = plt.cm.viridis #[djn] colormap
marker_size = 100
colors = [results[x][0] for x in results]
plt.subplot(2, 1, 1)
plt.scatter(x_scatter, y_scatter, marker_size,
c=colors, cmap=cm)
plt.colorbar()
plt.xlabel('log learning rate')
plt.ylabel('log regularization strength')
plt.title('CIFAR-10 training accuracy')
# plot validation accuracy
colors = [results[x][1] for x in results] # default size of markers is 20
plt.subplot(2, 1, 2)
plt.scatter(x_scatter, y_scatter, marker_size,
c=colors, cmap=cm)
plt.colorbar()
plt.xlabel('log learning rate')
plt.ylabel('log regularization strength')
plt.title('CIFAR-10 validation accuracy')
plt.show()
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# Evaluate the best svm on test set
y_test_pred = best_svm.predict(X_test)
test_accuracy = np.mean(y_test == y_test_pred)
print('linear SVM on raw pixels final test set '
'accuracy: {:f}'.format(test_accuracy))
In [19]:
# Visualize the learned weights for each class.
# Depending on your choice of learning rate and regularization
# strength, these may or may not be nice to look at.
w = best_svm.W[:-1,:] # strip out the bias
w = w.reshape(32, 32, 3, 10)
w_min, w_max = np.min(w), np.max(w)
classes = ['plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
for i in xrange(10):
plt.subplot(2, 5, i + 1)
# Rescale the weights to be between 0 and 255
wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) \
/ (w_max - w_min)
plt.imshow(wimg.astype('uint8'))
plt.axis('off')
plt.title(classes[i])
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