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
def main_slp(retrain_tag):
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)
if retrain_tag:
w, b = train_slp(mini_batch_x, mini_batch_y)
sio.savemat('slp.mat', mdict={'w': w, 'b': b})
else:
data = sio.loadmat('slp.mat')
w, b = data['w'], data['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 Perceptron Confusion Matrix')
def main_mlp():
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)
w1, b1, w2, b2 = train_mlp(mini_batch_x, mini_batch_y)
sio.savemat('mlp.mat', mdict={'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2})
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
x = im_test[:, [i]]
pred1 = fc(x, w1, b1)
pred2 = relu(pred1)
y = fc(pred2, w2, b2)
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,
'Multi-layer Perceptron Confusion Matrix')
def main_mlp():
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)
w1, b1, w2, b2 = train_mlp(mini_batch_x, mini_batch_y)
sio.savemat('mlp.mat', mdict={'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2})
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
x = im_test[:, [i]]
pred1 = fc(x, w1, b1)
pred2 = relu(pred1)
y = fc(pred2, w2, b2)
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
def main_cnn():
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 = cnn.get_mini_batch(im_train, label_train,
batch_size)
# learning_rates = [.14, .16, .18]
# decay_rates = [.85, .9, .95]
# for l in learning_rates:
# for d in decay_rates:
w_conv, b_conv, w_fc, b_fc = cnn.train_cnn(mini_batch_x, mini_batch_y)
sio.savemat('cnn.mat',
mdict={
'w_conv': w_conv,
'b_conv': b_conv,
'w_fc': w_fc,
'b_fc': b_fc
})
# could use following two lines to replace above two lines if only want to check results
# data = sio.loadmat('cnn.mat')
# w_conv, b_conv, w_fc, b_fc = data['w_conv'], data['b_conv'], data['w_fc'], data['b_fc']
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
x = im_test[:, [i]].reshape((14, 14, 1), order='F')
pred1 = cnn.conv(x, w_conv, b_conv) # (14, 14, 3)
pred2 = cnn.relu(pred1) # (14, 14, 3)
pred3, maxes = cnn.pool2x2(pred2) # (7, 7, 3)
pred4 = cnn.flattening(pred3) # (147, 1)
y = cnn.fc(pred4, w_fc, b_fc) # (10, 1)
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
# print("Learning rate:", l, "Decay rate:", d, "Accuracy:", accuracy)
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,
'CNN Confusion Matrix')
def main_cnn(retrain_tag):
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)
if retrain_tag:
w_conv, b_conv, w_fc, b_fc = train_cnn(mini_batch_x, mini_batch_y)
sio.savemat('cnn.mat',
mdict={
'w_conv': w_conv,
'b_conv': b_conv,
'w_fc': w_fc,
'b_fc': b_fc
})
else:
data = sio.loadmat('cnn.mat')
w_conv, b_conv, w_fc, b_fc = data['w_conv'], data['b_conv'], data[
'w_fc'], data['b_fc']
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
x = im_test[:, [i]].reshape((14, 14, 1), order='F')
pred1 = conv(x, w_conv, b_conv) # (14, 14, 3)
pred2 = relu(pred1) # (14, 14, 3)
pred3 = pool2x2(pred2) # (7, 7, 3)
pred4 = flattening(pred3) # (147, 1)
y = fc(pred4, w_fc, b_fc) # (10, 1)
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,
'CNN Confusion Matrix')
def main_cnn():
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_conv, b_conv, w_fc, b_fc = train_cnn(mini_batch_x, mini_batch_y)
sio.savemat('cnn.mat',
mdict={
'w_conv': w_conv,
'b_conv': b_conv,
'w_fc': w_fc,
'b_fc': b_fc
})
# could use following two lines to replace above two lines if only want to check results
# data = sio.loadmat('cnn.mat')
# w_conv, b_conv, w_fc, b_fc = data['w_conv'], data['b_conv'], data['w_fc'], data['b_fc']
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
x = im_test[:, [i]].reshape((14, 14, 1), order='F')
pred1 = conv(x, w_conv, b_conv) # (14, 14, 3)
pred2 = relu(pred1) # (14, 14, 3)
pred3 = pool2x2(pred2) # (7, 7, 3)
pred4 = flattening(pred3) # (147, 1)
y = fc(pred4, w_fc, b_fc) # (10, 1)
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
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_batches_x, mini_batches_y = get_mini_batch(im_train, label_train,
batch_size)
# for batch_x, batch_y in zip(mini_batches_x, mini_batches_y):
# plt.imshow(batch_x[:, -1].reshape((14, 14), order='F'), cmap='gray')
# plt.show()
# print(batch_y[:, -1])
w, b = train_slp_linear(mini_batches_x, mini_batches_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_cnn():
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
# plt.imshow(mnist_train['im_train'][:, 0].reshape((14, 14), order='F'), cmap='gray')
# plt.show()
# x = im_train[:, 0].reshape((14, 14, 1), order='F')
# y = pool2x2(x)
# dl_dy = np.random.rand(7, 7, 1)
# dl_dx = pool2x2_backward(dl_dy, x, y)
# plt.imshow(x[:, :, 0], cmap='gray')
# plt.show()
# plt.imshow(y[:, :, 0], cmap='gray')
# plt.show()
# plt.imshow(dl_dy[:, :, 0], cmap='gray')
# plt.show()
# plt.imshow(dl_dx[:, :, 0], cmap='gray')
# plt.show()
# x = np.arange(25).reshape((5, 5, 1))
# w_conv = np.arange(27).reshape((3, 3, 1, 3))
# b_conv = np.arange(3).reshape((3, 1))
# y = conv(x, w_conv, b_conv)
# dl_dy = np.random.random((5, 5, 3))
# dl_dw, dl_db = conv_backward(dl_dy, x, w_conv, b_conv, y)
# print(x)
# print(w_conv)
# print(b_conv)
# print(y)
# print(dl_dw.shape)
# print(dl_db)
# exit(-1)
mini_batches_x, mini_batches_y = get_mini_batch(im_train, label_train,
batch_size)
w_conv, b_conv, w_fc, b_fc = train_cnn(mini_batches_x, mini_batches_y
# , im_test, label_test
)
sio.savemat('cnn.mat',
mdict={
'w_conv': w_conv,
'b_conv': b_conv,
'w_fc': w_fc,
'b_fc': b_fc
})
# could use following two lines to replace above two lines if only want to check results
# data = sio.loadmat('cnn.mat')
# w_conv, b_conv, w_fc, b_fc = data['w_conv'], data['b_conv'], data['w_fc'], data['b_fc']
acc = 0
confusion = np.zeros((10, 10))
num_test = im_test.shape[1]
for i in range(num_test):
print('Test # {}/{}: \r'.format(i + 1, num_test), end='')
x = im_test[:, [i]].reshape((14, 14, 1), order='F')
pred1 = conv(x, w_conv, b_conv) # (14, 14, 3)
pred2 = relu(pred1) # (14, 14, 3)
pred3 = pool2x2(pred2) # (7, 7, 3)
pred4 = flattening(pred3) # (147, 1)
y = fc(pred4, w_fc, b_fc) # (10, 1)
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
print(accuracy)
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,
'CNN Confusion Matrix')
