def __init__(self,
input_shape,
border_mode='half',
num_classes=10,
name='VGG19'):
super(VGG19, self).__init__(input_shape,
fc=False,
bn=True,
border_mode=border_mode,
num_classes=num_classes,
name=name)
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '/maxpool4'))
self.append(
nn.SoftmaxLayer(self.output_shape, num_classes, name + '/softmax'))
def __init__(self,
input_shape,
fc=True,
num_classes=1000,
first_output=16,
growth_rate=12,
num_blocks=3,
depth=40,
dropout=False,
name='DenseNet'):
super(DenseNet, self).__init__(input_shape=input_shape,
layer_name=name)
self.append(
nn.ConvolutionalLayer(self.input_shape,
first_output,
3,
activation='linear',
layer_name=name + 'pre_conv'))
n = (depth - 1) // num_blocks
for b in range(num_blocks):
self.append(
nn.DenseBlock(self.output_shape,
num_conv_layer=n - 1,
growth_rate=growth_rate,
dropout=dropout,
layer_name=name + 'dense_block_%d' % b))
if b < num_blocks - 1:
self.append(
nn.DenseBlock(self.output_shape,
True,
None,
None,
dropout,
layer_name=name +
'dense_block_transit_%d' % b))
self.append(
nn.BatchNormLayer(self.output_shape, layer_name=name + 'post_bn'))
if fc:
self.append(
nn.GlobalAveragePoolingLayer(input_shape,
name + '_glbavgpooling'))
self.append(
nn.SoftmaxLayer(self.output_shape, num_classes,
name + '_softmax'))
def __init__(self, input_shape, fc=True, num_classes=1000, name='vgg19'):
super(VGG19, self).__init__(input_shape=input_shape, layer_name=name)
self.fc = fc
self.append(
nn.Conv2DLayer(self.output_shape,
64,
3,
no_bias=False,
layer_name=name + '_conv1_1'))
self.append(
nn.Conv2DLayer(self.output_shape,
64,
3,
no_bias=False,
layer_name=name + '_conv1_2'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool0'))
self.append(
nn.Conv2DLayer(self.output_shape,
128,
3,
no_bias=False,
layer_name=name + '_conv2_1'))
self.append(
nn.Conv2DLayer(self.output_shape,
128,
3,
no_bias=False,
layer_name=name + '_conv2_2'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool1'))
self.append(
nn.Conv2DLayer(self.output_shape,
256,
3,
no_bias=False,
layer_name=name + '_conv3_1'))
self.append(
nn.Conv2DLayer(self.output_shape,
256,
3,
no_bias=False,
layer_name=name + '_conv3_2'))
self.append(
nn.Conv2DLayer(self.output_shape,
256,
3,
no_bias=False,
layer_name=name + '_conv3_3'))
self.append(
nn.Conv2DLayer(self.output_shape,
256,
3,
no_bias=False,
layer_name=name + '_conv3_4'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool2'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv4_1'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv4_2'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv4_3'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv4_4'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool3'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv5_1'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv5_2'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv5_3'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
no_bias=False,
layer_name=name + '_conv5_4'))
if fc:
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool4'))
self.append(
nn.FCLayer(self.output_shape, 4096, layer_name=name + '_fc1'))
self.append(
nn.FCLayer(self.output_shape, 4096, layer_name=name + '_fc2'))
self.append(
nn.SoftmaxLayer(self.output_shape, num_classes,
name + '_softmax'))
def __init__(self,
input_shape,
fc=True,
bn=False,
dropout=True,
border_mode='half',
num_classes=1000,
name='vgg16'):
super(VGG16, self).__init__(input_shape=input_shape, layer_name=name)
self.fc = fc
self.append(
nn.Conv2DLayer(self.output_shape,
64,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv1',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn1') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu1'))
self.append(
nn.Conv2DLayer(self.output_shape,
64,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv2',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn2') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu2'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool0'))
self.append(
nn.Conv2DLayer(self.output_shape,
128,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv3',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn3') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu3'))
self.append(
nn.Conv2DLayer(self.output_shape,
128,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv4',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn4') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu4'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool1'))
self.append(
nn.Conv2DLayer(self.output_shape,
256,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv5',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn5') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu5'))
self.append(
nn.Conv2DLayer(self.output_shape,
256,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv6',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn6') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu6'))
self.append(
nn.Conv2DLayer(self.output_shape,
256,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv7',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn7') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu7'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool2'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv8',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn8') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu8'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv9',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn9') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu9'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv10',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn10') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu10'))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool3'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv11',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn11') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu11'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv12',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn11') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu12'))
self.append(
nn.Conv2DLayer(self.output_shape,
512,
3,
nn.HeNormal('relu'),
activation=None,
no_bias=False,
layer_name=name + '/conv13',
border_mode=border_mode))
self.append(
nn.BatchNormLayer(self.output_shape, name + '/bn13') if bn else nn.
ActivationLayer(self.output_shape, layer_name=name + '/relu13'))
if fc:
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '_maxpool4'))
self.append(
nn.FCLayer(self.output_shape, 4096, layer_name=name + '_fc1'))
if dropout:
self.append(
nn.DropoutLayer(self.output_shape,
drop_prob=.5,
layer_name=name + '/dropout1'))
self.append(
nn.FCLayer(self.output_shape, 4096, layer_name=name + '_fc2'))
if dropout:
self.append(
nn.DropoutLayer(self.output_shape,
drop_prob=.5,
layer_name=name + '/dropout2'))
self.append(
nn.SoftmaxLayer(self.output_shape, num_classes,
name + '_softmax'))
def __init__(self,
input_shape,
num_classes=10,
name='vgg19 mean interp padding'):
super(VGG19MeanInterpPadding, self).__init__(input_shape=input_shape,
layer_name=name)
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
64,
3,
activation=None,
layer_name=name + '/conv1'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn1'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
64,
3,
activation=None,
layer_name=name + '/conv2'))
self.append(
nn.BatchNormLayer(self.output_shape,
name + '/bn2',
activation=None))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '/maxpool0'))
self.append(
nn.ActivationLayer(self.output_shape, 'relu', name + '/relu2'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
128,
3,
activation=None,
layer_name=name + '/conv3'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn3'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
128,
3,
activation=None,
layer_name=name + '/conv4'))
self.append(
nn.BatchNormLayer(self.output_shape,
name + '/bn4',
activation=None))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '/maxpool1'))
self.append(
nn.ActivationLayer(self.output_shape, 'relu', name + '/relu4'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
256,
3,
activation=None,
layer_name=name + '/conv5'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn5'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
256,
3,
activation=None,
layer_name=name + '/conv6'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn6'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
256,
3,
activation=None,
layer_name=name + '/conv7'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn7'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
256,
3,
activation=None,
layer_name=name + '/conv7_1'))
self.append(
nn.BatchNormLayer(self.output_shape,
name + '/bn7_1',
activation=None))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '/maxpool2'))
self.append(
nn.ActivationLayer(self.output_shape, 'relu', name + '/relu8'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv8'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn8'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv9'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn9'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv10'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn10'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv10_1'))
self.append(
nn.BatchNormLayer(self.output_shape,
name + '/bn10_1',
activation=None))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '/maxpool3'))
self.append(
nn.ActivationLayer(self.output_shape, 'relu', name + '/relu11'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv11'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn11'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv12'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn12'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv13'))
self.append(nn.BatchNormLayer(self.output_shape, name + '/bn13'))
self.append(
Conv2DMeanInterpPaddingLayer(self.output_shape,
512,
3,
activation=None,
layer_name=name + '/conv13_1'))
self.append(
nn.BatchNormLayer(self.output_shape,
name + '/bn13_1',
activation=None))
self.append(
nn.MaxPoolingLayer(self.output_shape, (2, 2),
layer_name=name + '/maxpool4'))
self.append(
nn.ActivationLayer(self.output_shape, 'relu', name + '/relu14'))
self.append(
nn.SoftmaxLayer(self.output_shape, num_classes, name + '/softmax'))