def main():
# image shape: N x H x W, pixel [0, 255]
# label shape: N x 10
with np.load('mnist.npz', allow_pickle=True) as f:
x_train, y_train = f['x_train'], f['y_train']
x_test, y_test = f['x_test'], f['y_test']
plt.imshow(x_train[59999], cmap='gray')
plt.show()
print(x_train.shape, x_train[0].max(),
x_train[0].min()) #(60000, 28, 28) 255 0 5
print(x_test.shape, x_test[0].max(),
x_test[0].min()) #(10000, 28, 28) 255 0 7
x_train = normalize_image(x_train)
x_test = normalize_image(x_test)
y_train = one_hot_labels(y_train)
y_test = one_hot_labels(y_test)
net = LeNet()
net.fit(x_train,
y_train,
x_test,
y_test,
epoches=10,
batch_size=16,
lr=1e-3)
accu = net.evaluate(x_test, labels=y_test)
print("final accuracy {}".format(accu))
def main():
# image shape: N x H x W, pixel [0, 255]
# label shape: N x 10
with np.load('mnist.npz', allow_pickle=True) as f:
x_train, y_train = f['x_train'], f['y_train']
x_test, y_test = f['x_test'], f['y_test']
print(x_train.shape, x_train[0].max(),
x_train[0].min()) #(60000, 28, 28) 255 0 5
print(x_test.shape, x_test[0].max(),
x_test[0].min()) #(10000, 28, 28) 255 0 7
x_train = normalize_image(x_train)
x_test = normalize_image(x_test)
y_train = one_hot_labels(y_train)
y_test = one_hot_labels(y_test)
lr = 1.5e-4
batch_size = 8
net = LeNet()
avgtime, accuracy = \
net.fit(x_train, y_train, x_test, y_test, epoches=5, batch_size=batch_size, lr=lr)
accu = net.evaluate(x_test, labels=y_test)
print('avgtime: ', avgtime)
#print('accuracy: ', accuracy)
'''
plt.plot(accuracy)
plt.savefig('lr={}.jpg'.format(lr))
plt.show()
'''
print("final accuracy {}".format(accu))
def main():
batch_size = 128
epoch = 15
data = DATA()
model = LeNet(data.input_shape, data.num_classes)
hist = model.fit(data.x_train,
data.y_train,
batch_size=batch_size,
epochs=epoch,
validation_split=0.2)
score = model.evaluate(data.x_test, data.y_test, batch_size=batch_size)
print()
print('Test Loss= ', score)
plot_loss(hist)
plt.show()
from lenet import LeNet
from sklearn.cross_validation import train_test_split
from keras.optimizers import SGD
from keras.utils import np_utils
from sys import argv
print("[INFO] loading DATA...")
dataset = np.load(argv[1])
labelset = np.load(argv[2])
data = dataset[:, :, :, np.newaxis]
(trainData, testData, trainLabels, testLabels) = train_test_split(
data, labelset, test_size=0.2)
trainLabels = np_utils.to_categorical(trainLabels)
testLabels = np_utils.to_categorical(testLabels)
print("[INFO] compiling model...")
opt = SGD(lr=0.01)
model = LeNet(width=dataset.shape[1], height=dataset.shape[2], depth=1, classes=len(np.unique(labelset)))
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
model.fit(trainData, trainLabels, batch_size=128, nb_epoch=20, verbose=1)
print("[INFO] evaluating...")
(loss, accuracy) = model.evaluate(testData, testLabels,
batch_size=128, verbose=1)
print("\n[INFO] accuracy: {:.2f}%".format(accuracy * 100))
