# A simple strided convnet
layers = [
Conv((4, 4, 1, 20), strides=2, activation=lkrelu, filter_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (28*28 + 13*13*20)) ),
Conv((5, 5, 20, 40), strides=2, activation=lkrelu, filter_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (13*13*20 + 5*5*40)) ),
Flatten((5, 5, 40)),
FullyConnected((5*5*40, 100), activation=sigmoid, weight_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (5*5*40 + 100.))),
FullyConnected((100, 10), activation=linear, weight_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (110.)))
]
lr = 0.001
k = 2000
net = Network(layers, lr=lr, loss=cross_entropy)
(train_data_X, train_data_Y), v, (tx, ty) = mnist_loader.load_data('./data/mnist.pkl.gz')
train_data_Y = one_hot(train_data_Y, size=10)
ty = one_hot(ty, size=10)
train_data_X = np.reshape(train_data_X, [-1, 28, 28, 1])
tx = np.reshape(tx, [-1, 28, 28, 1])
for epoch in xrange(100000):
shuffled_index = np.random.permutation(train_data_X.shape[0])
batch_train_X = train_data_X[shuffled_index[:batch_size]]
batch_train_Y = train_data_Y[shuffled_index[:batch_size]]
net.train_step((batch_train_X, batch_train_Y))
loss = np.sum(cross_entropy.compute((net.forward(batch_train_X), batch_train_Y)))
print 'Epoch: %d loss : %f' % (epoch, loss)
if epoch % 1000 == 1:
print 'Accuracy on first 500 test set\'s batch : %f' % accuracy(net, tx[:500], ty[:500])
if epoch % 5000 == 5000 - 1:
print 'Accuracy over all test set %f' % accuracy(net, tx, ty)
# If you want to continue training a network uncomment the line below
# net = pickle.load(open("network.nn", "rb"))
train_data_X = np.reshape(train_data_X, [-1, 28, 28, 3])
try:
for epoch in range(100000):
shuffled_index = np.random.permutation(train_data_X.shape[0])
# Get minibatch of training data
batch_train_X = train_data_X[shuffled_index[:batch_size]]
batch_train_Y = train_data_Y[shuffled_index[:batch_size]]
net.train_step((batch_train_X, batch_train_Y))
loss = np.sum(
mse.compute((net.forward(batch_train_X), batch_train_Y)))
if epoch % 200 == 0:
# Print average loss of neural network
print(
accuracy(net, train_data_X, train_data_Y) /
len(train_data_X))
pickle.dump(net, open("network.nn", "wb"))
except KeyboardInterrupt:
print(accuracy(net, train_data_X, train_data_Y))
pickle.dump(net, open("network.nn", "wb"))
print(accuracy(net, train_data_X, train_data_Y))
pickle.dump(net, open("network.nn", "wb"))