# Liniarize II
nn = FeedForward([
LinearizeLayer(32, 32, 3),
FullyConnected(32 * 32 * 3, 300, identity),
Tanh(),
FullyConnected(300, 200, identity),
Tanh(),
FullyConnected(200, 10, identity),
SoftMax()
])
# # Convolutional I
nn = FeedForward([
ConvolutionalLayer(3, 32, 32, 6, 5, 1),
MaxPoolingLayer(2),
ReluLayer(),
ConvolutionalLayer(6, 14, 14, 16, 5, 1),
MaxPoolingLayer(2),
ReluLayer(),
LinearizeLayer(16, 5, 5),
FullyConnected(400, 300, relu),
FullyConnected(300, 10, relu),
SoftMax()
])
# Convolutional II
# nn = FeedForward([
# ConvolutionalLayer(3, 32, 32, 6, 5, 1),
# ReluLayer(),
# ConvolutionalLayer(6, 28, 28, 16, 5, 1),
def __init__(self):
# Lenet
# input: 28x28
# conv1: (5x5x6)@s1p2 -> 28x28x6 {(28-5+2x2)/1+1}
# maxpool2: (2x2)@s2 -> 14x14x6 {(28-2)/2+1}
# conv3: (5x5x16)@s1p0 -> 10x10x16 {(14-5)/1+1}
# maxpool4: (2x2)@s2 -> 5x5x16 {(10-2)/2+1}
# conv5: (5x5x120)@s1p0 -> 1x1x120 {(5-5)/1+1}
# fc6: 120 -> 84
# fc7: 84 -> 10
# softmax: 10 -> 10
lr = 0.01
self.layers = []
self.layers.append(
ConvolutionLayer(inputs_channel=1,
num_filters=6,
width=5,
height=5,
padding=2,
stride=1,
learning_rate=lr,
name='conv1'))
self.layers.append(ReLu())
self.layers.append(
MaxPoolingLayer(width=2, height=2, stride=2, name='maxpool2'))
self.layers.append(
ConvolutionLayer(inputs_channel=6,
num_filters=16,
width=5,
height=5,
padding=0,
stride=1,
learning_rate=lr,
name='conv3'))
self.layers.append(ReLu())
self.layers.append(
MaxPoolingLayer(width=2, height=2, stride=2, name='maxpool4'))
self.layers.append(
ConvolutionLayer(inputs_channel=16,
num_filters=120,
width=5,
height=5,
padding=0,
stride=1,
learning_rate=lr,
name='conv5'))
self.layers.append(ReLu())
self.layers.append(Flatten())
self.layers.append(
FullyConnectedLayer(num_inputs=120,
num_outputs=84,
learning_rate=lr,
name='fc6'))
self.layers.append(ReLu())
self.layers.append(
FullyConnectedLayer(num_inputs=84,
num_outputs=10,
learning_rate=lr,
name='fc7'))
self.layers.append(Softmax())
self.lay_num = len(self.layers)
