def main_slp_linear():
mnist_train = sio.loadmat('./mnist_train.mat')
mnist_test = sio.loadmat('./mnist_test.mat')
im_train, label_train = mnist_train['im_train'], mnist_train['label_train']
im_test, label_test = mnist_test['im_test'], mnist_test['label_test']
batch_size = 32
im_train, im_test = im_train / 255.0, im_test / 255.0
mini_batch_x, mini_batch_y = get_mini_batch(im_train, label_train,
batch_size)
w, b = train_slp_linear(mini_batch_x, mini_batch_y)
sio.savemat('slp_linear.mat', mdict={'w': w, 'b': b})
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
x = im_test[:, [i]]
y = fc(x, w, b)
l_pred = np.argmax(y)
confusion[l_pred,
label_test[0, i]] = confusion[l_pred, label_test[0, i]] + 1
if l_pred == label_test[0, i]:
acc = acc + 1
accuracy = acc / num_test
for i in range(10):
confusion[:, i] = confusion[:, i] / np.sum(confusion[:, i])
label_classes = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
visualize_confusion_matrix(
confusion, accuracy, label_classes,
'Single-layer Linear Perceptron Confusion Matrix')
def main_slp_linear():
mnist_train = sio.loadmat('./ReducedMNIST/mnist_train.mat')
mnist_test = sio.loadmat('./ReducedMNIST/mnist_test.mat')
im_train, label_train = mnist_train['im_train'], mnist_train['label_train']
im_test, label_test = mnist_test['im_test'], mnist_test['label_test']
batch_size = 32
im_train, im_test = im_train / 255.0, im_test / 255.0
mini_batch_x, mini_batch_y = get_mini_batch(im_train, label_train,
batch_size)
w, b = train_slp_linear(mini_batch_x, mini_batch_y)
sio.savemat('slp_linear.mat', mdict={'w': w, 'b': b})
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
x = im_test[:, [i]]
y = fc(x, w, b)
l_pred = np.argmax(y)
confusion[l_pred,
label_test[0, i]] = confusion[l_pred, label_test[0, i]] + 1
if l_pred == label_test[0, i]:
acc = acc + 1
accuracy = acc / num_test
for i in range(10):
confusion[:, i] = confusion[:, i] / np.sum(confusion[:, i])
return confusion, accuracy