def initialize(self,
size,
outchn,
stride=1,
pad='SAME_LEFT',
dilation_rate=1,
activation=-1,
batch_norm=False,
affine=True,
usebias=True,
groups=1):
self.conv = L.conv2D(size, outchn, stride, pad, dilation_rate, usebias,
groups)
if batch_norm:
self.bn = L.BatchNorm(affine=affine)
self.batch_norm = batch_norm
self.activation = activation
self.act = L.Activation(activation)
def run():
# Fetch data
f1 = file('../data/mldata/mnist_data.pkl','rb')
mnist = pickle.load(f1)
f1.close()
split = 60000
X_train = np.reshape(mnist.data[:split], (-1,1,28,28))/255.0
Y_train = mnist.target[:split]
X_test = np.reshape(mnist.data[split:], (-1,1,28,28))/255.0
Y_test = mnist.target[split:]
n_classes = np.unique(Y_train).size
# Downsample training data
n_train_samples = 3000
train_idxs = np.random.random_integers(0, split-1, n_train_samples)
X_train = X_train[train_idxs, ...]
Y_train = Y_train[train_idxs, ...]
Y_train_one_hot = one_hot(Y_train)
print ('number of train samples: %d')%(n_train_samples)
print ('number of test samples: %d')%(X_test.shape[0])
# setup network
nn = NeuralNetwork(
layers = [
Layers.Convolution(
n_feats=12,
filter_shape=(5,5),
strides=(1,1),
weight_scale=0.1,
weight_decay=0.001),
Layers.Activation('relu'),
Layers.Pool(
pool_shape=(2,2),
strides=(2,2),
mode='max'),
Layers.Convolution(
n_feats=16,
filter_shape=(5,5),
strides=(1,1),
weight_scale=0.1,
weight_decay=0.001),
Layers.Activation('relu'),
Layers.Flatten(),
Layers.Linear(
n_out=n_classes,
weight_scale=0.1,
weight_decay=0.02),
Layers.Softmax()
]
)
#check gradient
# nn.check_gradients(X_train[:10], Y_train_one_hot[:10])
# Train neural network
t0 = time.time()
nn.train(X_train, Y_train_one_hot, learning_rate=0.05, max_iter=3, batch_size=32)
t1 = time.time()
print('Duration: %.1fs' % (t1-t0))
# Evaluate on test data
# Y_test_one_hot = one_hot(Y_test)
error = nn.error(X_test, Y_test)
print('Test error rate: %.4f' % error)
