class DigitClassifier():
def __init__(self):
self.svm = LinearSVM()
self.fit()
def fit(self):
minst = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = minst.load_data()
num_train = 15000
num_val = 1000
num_dev = 500
num_test = 10000
# Validation set
mask = range(num_train, num_train + num_val)
X_val = X_train[mask]
y_val = y_train[mask]
# Train set
mask = range(num_train)
X_train = X_train[mask]
y_train = y_train[mask]
# Small training set (development set)
mask = np.random.choice(num_train, num_dev, replace=False)
X_dev = X_train[mask]
y_dev = y_train[mask]
# Preprocessing: reshape the images 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))
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))])
tic = time.time()
loss_hist = self.svm.train(X_train,
y_train,
learning_rate=1e-7,
reg=2.5e4,
num_iters=1500,
verbose=True)
toc = time.time()
print('That took %fs' % (toc - tic))
# plt.plot(loss_hist)
# plt.xlabel('Iteration number')
# plt.ylabel('Loss value')
# plt.show()
def predict(self, x):
return self.svm.predict(x)
# 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（对应元素的平方和再开方） to compare them.
#difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')
#print('difference: %f' % difference)
'''
# In the file linear_classifier.py, implement SGD in the function
# LinearClassifier.train() and then run it with the code below.
from linear_classifier import LinearSVM
svm = LinearSVM()
tic = time.time()
loss_hist = svm.train(X_train,
y_train,
learning_rate=1e-7,
reg=2.5e4,
num_iters=1500,
verbose=True)
toc = time.time()
print('That took %fs' % (toc - tic))
'''''
# 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()
'''
# Write the LinearSVM.predict function and evaluate the performance on both the
# training and validation set
# almost exactly along all dimensions.
f = lambda w: svm_loss_vectorized(w, X_dev, y_dev, 0.0)[0]
grad_numerical = grad_check_sparse(f, W, grad)
# do the gradient check once again with regularization turned on
# you didn't forget the regularization gradient did you?
loss, grad = svm_loss_vectorized(W, X_dev, y_dev, 5e1)
f = lambda w: svm_loss_vectorized(w, X_dev, y_dev, 5e1)[0]
grad_numerical = grad_check_sparse(f, W, grad)
# In the file linear_classifier.py, implement SGD in the function
# LinearClassifier.train() and then run it with the code below.
svm=LinearSVM()
tic=time.time()
loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,
num_iters=1500, verbose=True)
toc=time.time()
print('That tooks %fs'%(toc-tic))
"""
# 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.savefig('/home/hongyin/file/cs231n-assignment1/picFaster.jpg')
"""
# Write the LinearSVM.predict function and evaluate the performance on both the
# training and validation set
y_train_pred = svm.predict(X_train)
def cross_validation(X_train, y_train, X_val, y_val):
#############################################################################################
# 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.
#############################################################################################
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 = {}
best_val = -1 # The highest validation accuracy that we have seen so far.
best_svm = None # The LinearSVM object that achieved the highest validation rate.
################################################################################
# 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. #
################################################################################
iters = 2000 #100
for lr in learning_rates:
for rs in regularization_strengths:
svm = LinearSVM()
svm.train(X_train,
y_train,
learning_rate=lr,
reg=rs,
num_iters=iters)
y_train_pred = svm.predict(X_train)
acc_train = np.mean(y_train == y_train_pred)
y_val_pred = svm.predict(X_val)
acc_val = np.mean(y_val == y_val_pred)
results[(lr, rs)] = (acc_train, acc_val)
if best_val < acc_val:
best_val = acc_val
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 train accuracy: %f val accuracy: %f' %
(lr, reg, train_accuracy, val_accuracy))
print('best validation accuracy achieved during cross-validation: %f' %
best_val)
return results, best_svm
print('grad_numerical: ', grad_numerical)
# do the gradient check once again with regularization turned on
# you didn't forget the regularization gradient did you?
svmDevObj2 = SVM(W, X_dev, y_dev, 5e1)
loss, grad = svmDevObj2.svm_loss_naive()
f = lambda w: svmDevObj2.svm_loss_naive()[0]
grad_numerical = grad_check_sparse(f, W, grad)
print('grad_numerical: ', grad_numerical)
#####################
linearSVM = LinearSVM()
tic = time.time()
loss_hist = linearSVM.train(X_train,
y_train,
learning_rate=1e-7,
reg=2.5e4,
num_iters=1500,
verbose=True)
toc = time.time()
print('That took %fs' % (toc - tic))
# 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()
# Write the LinearSVM.predict function and evaluate the performance on both the
# training and validation set
y_train_pred = linearSVM.predict(X_train)
b1[i] = a
b2 = np.zeros((test_num, 256))
for i in range(test_num):
a = np.bincount(test_images[i].astype(int), minlength=256).reshape(1, -1)
b2[i] = a
b3 = np.zeros((val_num, 256))
for i in range(val_num):
a = np.bincount(test_images[i].astype(int), minlength=256).reshape(1, -1)
b3[i] = a
#=================================================================================
# loss,grad=svm.svm_loss_naive(w,train_images,train_labels,reg)
# print(loss, grad)
svm = LinearSVM() #创建分类器对象，此时W为空
loss_hist = svm.train(train_images,
train_labels,
learning_rate=1e-7,
reg=2.5e4,
num_iters=1500,
verbose=True) #此时svm对象中有W
y_train_pred = svm.predict(train_images)
print('training accuracy: %f' % (np.mean(train_labels == y_train_pred)))
# y_val_pred = svm.predict(val_images)
# print('validation accuracy: %f'%(np.mean(val_labels==y_val_pred)))
# 超参数调优（交叉验证）
learning_rates = [1.4e-7, 1.5e-7, 1.6e-7]
# for循环的简化写法12个
regularization_strengths = [(1 + i * 0.1) * 1e4
for i in range(-3, 3)] + [(2 + i * 0.1) * 1e4
for i in range(-3, 3)]
results = {} # 字典
# tune hyperparameters
learningRates = [1e-7]
regularization = [5e3]
# iteration = [3000, 4000, 5000, 6000, 7000, 8000]
iteration = [6000]
bestParams = []
bestValAcc = 0
bestSvm = None
for eta in learningRates:
for r in regularization:
for t in iteration:
svm = LinearSVM()
svm.train(X_train,
y_train,
learning_rate=eta,
reg=r,
num_iters=t,
verbose=True)
y_train_pred = svm.predict(X_train)
y_val_pred = svm.predict(X_val)
trainAcc = np.mean(y_train == y_train_pred)
valAcc = np.mean(y_val == y_val_pred)
print 'iteration: %d train accuracy: %.4f val accuracy: %.4f' % (
t, trainAcc, valAcc)
if valAcc > bestValAcc:
bestParams = [eta, r, t]
bestValAcc = valAcc
bestSvm = svm
print 'best validation accuracy achieved: %.4f' % bestValAcc
print bestParams
# (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
# lr = learning rate , reg = regularization_strength
for lr in learning_rates:
for reg in regularization_strengths:
# new a svm
svm = LinearSVM()
# train with training set
svm.train(X_train, y_train, learning_rate=lr, reg=reg, num_iters=500)
# get training set accuracy
y_train_pred = svm.predict(X_train)
training_accuracy = np.mean(y_train_pred == y_train)
#print('Training set accuracy is %f' % (training_accuracy,))
# get validation set accuracy
y_val_pred = svm.predict(X_val)
validation_accuracy = np.mean(y_val_pred == y_val)
#print('Validation set accuracy is %f' % (validation_accuracy,))
# store the results
results[(lr, reg)] = (training_accuracy, validation_accuracy)
if validation_accuracy > best_val:
best_val = validation_accuracy
best_svm = svm
"""
for lr, reg in sorted(results):
# grad_check_sparse(f, W, grad)
# time_start = time.time()
# loss_naive, gradient_navie = svm_loss_naive(W, X_dev, y_dev, 5e-6)
# time_end = time.time()
# print ('Naive loss: ', loss_naive, ' use time: ', time_end - time_start)
#
# time_start = time.time()
# loss_vector, gradient_vector = svm_loss_vectorized(W, X_dev, y_dev, 5e-6)
# time_end = time.time()
# print ('Vector loss: ', loss_vector, ' use time: ', time_end - time_start)
# print ('different loss: ', loss_vector - loss_naive)
from linear_classifier import LinearSVM
svm = LinearSVM()
time_start = time.time()
loss_histroy = svm.train(X_train, y_train, learning_rate=1.5e-7, reg=3.25e4, num_iters=1500, batch_size=5000,
verbose=True)
time_end = time.time()
print ('train take time: ', time_end - time_start)
# 将损失和循环次数画出来，有利于debug
plt.plot(loss_histroy)
plt.xlabel('Iteration number')
plt.ylabel('Loss value')
plt.show()
y_val_pred = svm.predict(X_val)
print ('accuracy: %f' % (np.mean(y_val_pred == y_val)))
print('Vectorized loss and gradient: computed in %fs' % (toc - tic))
# The loss is a single number, so it is easy to compare the values computed
# by the two implemetations. The gradient on the other hand is a matrix, so
# we use the Frobenius norm to compare them.
diffrence = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')
print('difference: %f' % diffrence)
# ### Stochastic Gradient Descent
#
# We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss.
svm = LinearSVM()
tic = time.time()
loss_hist = svm.train(X_train,
y_train,
learning_rate=1e-7,
reg=2.5e4,
num_iters=1500,
verbose=True)
toc = time.time()
print('That took %fs ' % (toc - tic))
# 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()
# Write the LinearSVM.predict function and evaluate the performance on both the
# training and validation set
learning_rates = [5e-8, 1e-7, 5e-7, 1e-6]
regularization_strengths = [1e3, 5e3, 1e4, 5e4, 1e5, 5e5]
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
for strength in regularization_strengths:
for rate in learning_rates:
svm = LinearSVM()
svm.train(x_train,
y_train,
learning_rate=rate,
reg=strength,
num_iters=1500,
verbose=True)
y_train_pred = svm.predict(x_train)
train_accuracy = np.mean(y_train == y_train_pred)
y_valid_pred = svm.predict(x_val)
val_accuracy = np.mean(y_val == y_valid_pred)
results[(rate, strength)] = (train_accuracy, val_accuracy)
if val_accuracy > best_val:
best_val = val_accuracy
best_svm = svm
for lr, reg in sorted(results):
train_accuracy, val_accuracy = results[(lr, reg)]
print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (
learning_rates = [1.4e-7, 1.5e-7, 1.6e-7]
range = range(-3, 3)
regularization_strengths = [(1 + i * 0.1) * 1e4
for i in range] + [(2 + 0.1 * i) * 1e4
for i in range]
results = {}
best_val = -1
best_svm = None
for rs in regularization_strengths:
for lr in learning_rates:
svm = LinearSVM()
loss_hist = svm.train(train_data,
train_labels,
lr,
rs,
num_iters=3000)
train_labels_pred = svm.predict(train_data)
train_accuracy = np.mean(train_labels == train_labels_pred)
val_labels_pred = svm.predict(val_data)
val_accuracy = np.mean(val_labels == val_labels_pred)
if val_accuracy > best_val:
best_val = val_accuracy
best_svm = svm
results[(lr, rs)] = train_accuracy, val_accuracy
print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (
lr, rs, train_accuracy, val_accuracy)
for lr, reg in sorted(results):
train_accuracy, val_accuracy = results[(lr, reg)]