def executeSVM(X_train, y_train, OX_test, oy_test):
learning_rates = [1e-5, 1e-8]
regularization_strengths = [10e2, 10e4]
results = {}
best_val = -1
best_svm = None
# X_train = getCIFAR_as_32Pixels_Image(X_train)
# OX_test = getCIFAR_as_32Pixels_Image(OX_test)
accuracy = []
totalAccuracy = 0.0
## Implementing Cross Validation
crossValidObj = CrossValidation(5, X_train, y_train)
foldsGen = crossValidObj.generateTrainAndTest()
for i in range(5):
next(foldsGen)
X_test = OX_test
X_train = crossValidObj.train
y_train = crossValidObj.labels_train
X_val = crossValidObj.test
y_val = crossValidObj.labels_test
# 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))
# Normalize the data: subtract the mean image
mean_image = np.mean(X_train, axis=0)
X_train -= mean_image
X_val -= mean_image
X_test -= mean_image
# Add bias dimension and transform into columns
X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))]).T
X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))]).T
X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))]).T
SVM_sgd = SVM()
losses_sgd = SVM_sgd.train(X_train,
y_train,
method='sgd',
batch_size=200,
learning_rate=1e-6,
reg=1e5,
num_iters=1000,
verbose=False,
vectorized=True)
y_train_pred_sgd = SVM_sgd.predict(X_train)[0]
print('Training accuracy: %f' % (np.mean(y_train == y_train_pred_sgd)))
y_val_pred_sgd = SVM_sgd.predict(X_val)[0]
print('Validation accuracy: %f' % (np.mean(y_val == y_val_pred_sgd)))
i = 0
interval = 5
for learning_rate in np.linspace(learning_rates[0],
learning_rates[1],
num=interval):
i += 1
print('The current iteration is %d/%d' % (i, interval))
for reg in np.linspace(regularization_strengths[0],
regularization_strengths[1],
num=interval):
svm = SVM()
svm.train(X_train,
y_train,
method='sgd',
batch_size=200,
learning_rate=learning_rate,
reg=reg,
num_iters=1000,
verbose=False,
vectorized=True)
y_train_pred = svm.predict(X_train)[0]
y_val_pred = svm.predict(X_val)[0]
train_accuracy = np.mean(y_train == y_train_pred)
val_accuracy = np.mean(y_val == y_val_pred)
results[(learning_rate, reg)] = (train_accuracy, val_accuracy)
if val_accuracy > best_val:
best_val = val_accuracy
best_svm = svm
else:
pass
# Print out the results
for learning_rate, reg in sorted(results):
train_accuracy, val_accuracy = results[(learning_rate, reg)]
print('learning rate %e and regularization %e, \n \
the training accuracy is: %f and validation accuracy is: %f.\n' %
(learning_rate, reg, train_accuracy, val_accuracy))
print(accuracy)
y_test_predict_result = best_svm.predict(X_test)
y_test_predict = y_test_predict_result[0]
test_accuracy = np.mean(oy_test == y_test_predict)
accuracy.append(test_accuracy)
totalAccuracy += test_accuracy
print('The test accuracy is: %f' % test_accuracy)
print(accuracy)
avgAccuracy = totalAccuracy / 5.0
print('Average Accuracy: %f' % avgAccuracy)
def execute_softmax(X_train,y_train,OX_test,oy_test):
learning_rates = [1e-5, 1e-8]
regularization_strengths = [10e2, 10e4]
results = {}
best_val = -1
best_softmax = None
# X_train = getCIFAR_as_32Pixels_Image(X_train)
# OX_test = getCIFAR_as_32Pixels_Image(OX_test)
accuracy = []
totalAccuracy = 0.0
## Implementing Cross Validation
crossValidObj = CrossValidation(5, X_train, y_train)
foldsGen = crossValidObj.generateTrainAndTest()
for i in range(5):
next(foldsGen)
X_test = OX_test
X_train = crossValidObj.train
y_train = crossValidObj.labels_train
X_val = crossValidObj.test
y_val = crossValidObj.labels_test
# 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))
# Normalize the data: subtract the mean image
mean_image = np.mean(X_train, axis = 0)
X_train -= mean_image
X_val -= mean_image
X_test -= mean_image
# Add bias dimension and transform into columns
X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))]).T
X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))]).T
X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))]).T
softmax_sgd = Softmax()
tic = time.time()
losses_sgd = softmax_sgd.train(X_train, y_train, method='sgd', batch_size=200, learning_rate=1e-6,
reg = 1e5, num_iters=1000, verbose=False, vectorized=True)
toc = time.time()
y_train_pred_sgd = softmax_sgd.predict(X_train)[0]
print('Training accuracy: %f' % (np.mean(y_train == y_train_pred_sgd)))
y_val_pred_sgd = softmax_sgd.predict(X_val)[0]
print('Validation accuracy: %f' % (np.mean(y_val == y_val_pred_sgd)))
# Choose the best hyperparameters by tuning on the validation set
i = 0
interval = 5
for learning_rate in np.linspace(learning_rates[0], learning_rates[1], num=interval):
i += 1
print('The current iteration is %d/%d' % (i, interval))
for reg in np.linspace(regularization_strengths[0], regularization_strengths[1], num=interval):
softmax = Softmax()
softmax.train(X_train, y_train, method='sgd', batch_size=200, learning_rate=learning_rate,
reg = reg, num_iters=1000, verbose=False, vectorized=True)
y_train_pred = softmax.predict(X_train)[0]
y_val_pred = softmax.predict(X_val)[0]
train_accuracy = np.mean(y_train == y_train_pred)
val_accuracy = np.mean(y_val == y_val_pred)
results[(learning_rate, reg)] = (train_accuracy, val_accuracy)
if val_accuracy > best_val:
best_val = val_accuracy
best_softmax = softmax
else:
pass
# Print out the results
for learning_rate, reg in sorted(results):
train_accuracy,val_accuracy = results[(learning_rate, reg)]
print('learning rate %e and regularization %e, \n \
the training accuracy is: %f and validation accuracy is: %f.\n' % (learning_rate, reg, train_accuracy, val_accuracy))
y_test_predict_result = best_softmax.predict(X_test)
y_test_predict = y_test_predict_result[0]
test_accuracy = np.mean(oy_test == y_test_predict)
accuracy.append(test_accuracy)
totalAccuracy+=test_accuracy
print('The test accuracy is: %f' % test_accuracy)
print(accuracy)
avgAccuracy = totalAccuracy / 5.0
print('Average Accuracy: %f' % avgAccuracy)
