def BaseCapsNet(input_shape, n_class, routings):
# assemble encoder
x = Input(shape=input_shape)
l = x
l = Conv2D(256, (9, 9), strides=(2, 2), activation='relu',
padding="same")(l)
l = BatchNormalization()(l)
l = Conv2D(256, (9, 9), strides=(2, 2), activation='relu',
padding="same")(l)
l = BatchNormalization()(l)
l = ConvertToCaps()(l)
l = Conv2DCaps(16,
6,
kernel_size=(3, 3),
strides=(2, 2),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = FlattenCaps()(l)
digits_caps = CapsuleLayer(num_capsule=10,
dim_capsule=8,
routings=routings,
channels=0,
name='digit_caps')(l)
l = CapsToScalars(name='capsnet')(digits_caps)
m_capsnet = models.Model(inputs=x, outputs=l, name='capsnet_model')
y = layers.Input(shape=(n_class, ))
masked_by_y = Mask()(
[digits_caps, y]
) # The true label is used to mask the output of capsule layer. For training
masked = Mask()(digits_caps)
# Decoder Network
decoder = models.Sequential(name='decoder')
decoder.add(Dense(input_dim=80, activation="relu", output_dim=8 * 8 * 16))
decoder.add(Reshape((8, 8, 16)))
decoder.add(BatchNormalization(momentum=0.8))
decoder.add(
layers.Deconvolution2D(64, 3, 3, subsample=(1, 1), border_mode='same'))
decoder.add(
layers.Deconvolution2D(32, 3, 3, subsample=(2, 2), border_mode='same'))
decoder.add(
layers.Deconvolution2D(16, 3, 3, subsample=(2, 2), border_mode='same'))
decoder.add(
layers.Deconvolution2D(3, 3, 3, subsample=(1, 1), border_mode='same'))
decoder.add(Activation("relu"))
decoder.add(layers.Reshape(target_shape=(32, 32, 3), name='out_recon'))
train_model = models.Model(
[x, y], [m_capsnet.output, decoder(masked_by_y)])
eval_model = models.Model(x, [m_capsnet.output, decoder(masked)])
train_model.summary()
return train_model, eval_model
def DeepCapsNetTwoPath(input_shape1,input_shape2, n_class, routings):
# assemble encoder
x33 = Input(shape=input_shape1)
x15 = Input(shape=input_shape2,name='2_0')
path1 = Conv2D(128, (5, 5), strides=(1, 1), activation='relu', padding="same",name='1_1')(x33) # common conv layer
path1=MaxPooling2D(pool_size=(2, 2),name='1_2')(path1)
path1=Dropout(0.5)(path1)
path1 = ConvertToCaps()(path1)
path1 = Conv2DCaps(16, 8, kernel_size=(5, 5), strides=(2, 2), r_num=1, b_alphas=[1, 1, 1],name='1_3')(path1)
path1 = Conv2DCaps(16, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1],name='1_4')(path1)
path2 = Conv2D(128, (5, 5), strides=(1, 1), activation='relu', padding="same",name='2_1')(x15) # common conv layer
path2 = ConvertToCaps()(path2)
path2 = Conv2DCaps(16, 8, kernel_size=(5, 5), strides=(2, 2), r_num=1, b_alphas=[1, 1, 1],name='2_2')(path2)
path2 = Conv2DCaps(16, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1],name='2_3')(path2)
x = layers.Add()([path1, path2])
l=x
# l = Conv2D(128, (5, 5), strides=(1, 1), activation='relu', padding="same")(l) # common conv layer
# # l = BatchNormalization()(l)
# l = ConvertToCaps()(l)
# l = Conv2DCaps(32, 4, kernel_size=(3, 3), strides=(2, 2), r_num=1, b_alphas=[1, 1, 1])(l)
# l_skip = Conv2DCaps(32, 4, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = Conv2DCaps(32, 4, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = Conv2DCaps(32, 4, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = layers.Add()([l, l_skip])
l = Conv2DCaps(16, 8, kernel_size=(5, 5), strides=(2, 2), r_num=1, b_alphas=[1, 1, 1],name='3_1')(l)
l_skip = Conv2DCaps(16, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1],name='3_2')(l)
l = Conv2DCaps(16, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1],name='3_3')(l)
l = layers.Add()([l, l_skip])
# l = Conv2DCaps(32, 8, kernel_size=(3, 3), strides=(2, 2), r_num=1, b_alphas=[1, 1, 1])(l)
# l_skip = Conv2DCaps(32, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = Conv2DCaps(32, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = Conv2DCaps(32, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = layers.Add()([l, l_skip])
# l1 = l
# l = Conv2DCaps(32, 8, kernel_size=(3, 3), strides=(2, 2), r_num=1, b_alphas=[1, 1, 1])(l)
# l_skip = ConvCapsuleLayer3D(kernel_size=3, num_capsule=32, num_atoms=8, strides=1, padding='same', routings=3)(l)
# l = Conv2DCaps(32, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = Conv2DCaps(32, 8, kernel_size=(3, 3), strides=(1, 1), r_num=1, b_alphas=[1, 1, 1])(l)
# l = layers.Add()([l, l_skip])
# l2 = l
la = FlattenCaps()(l)
# la = FlattenCaps()(l2)
# lb = FlattenCaps()(l1)
# l = layers.Concatenate(axis=-2)([la, lb])
# l = Dropout(0.4)(l)
digits_caps = CapsuleLayer(num_capsule=n_class, dim_capsule=16, routings=routings, channels=0, name='digit_caps')(la)
l = CapsToScalars(name='capsnet')(digits_caps)
m_capsnet = models.Model(inputs=[x33,x15], outputs=l, name='capsnet_model')
y = Input(shape=(n_class,))
masked_by_y = Mask_CID()([digits_caps, y])
masked = Mask_CID()(digits_caps)
# Decoder Network
decoder = models.Sequential(name='decoder')
decoder.add(Dense(input_dim=16, activation="relu", output_dim=8 *8 * 16))
decoder.add(Reshape((8, 8, 16)))
decoder.add(BatchNormalization(momentum=0.8))
decoder.add(Deconvolution2D(64, 3, 3, subsample=(1, 1), border_mode='same'))
decoder.add(Deconvolution2D(32, 3, 3, subsample=(2, 2), border_mode='same'))
decoder.add(Deconvolution2D(16, 3, 3, subsample=(1, 1), border_mode='same'))
decoder.add(Deconvolution2D(1, 3, 3, subsample=(2, 2), border_mode='valid'))
decoder.add(Activation("relu"))
decoder.add(Reshape(target_shape=(33, 33, 1), name='out_recon'))
train_model= models.Model([x33,x15, y], [m_capsnet.output, decoder(masked_by_y)])
eval_model = models.Model([x33,x15], [m_capsnet.output, decoder(masked)])
train_model.summary()
return train_model, eval_model
def DeepCapsNet(input_shape, n_class, routings):
# assemble encoder
x = Input(shape=input_shape)
l = x
l = Conv2D(128, (3, 3), strides=(1, 1), activation='relu',
padding="same")(l) # common conv layer
l = BatchNormalization()(l)
l = ConvertToCaps()(l)
l = Conv2DCaps(32,
4,
kernel_size=(3, 3),
strides=(2, 2),
r_num=1,
b_alphas=[1, 1, 1])(l)
l_skip = Conv2DCaps(32,
4,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = Conv2DCaps(32,
4,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = Conv2DCaps(32,
4,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = layers.Add()([l, l_skip])
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(2, 2),
r_num=1,
b_alphas=[1, 1, 1])(l)
l_skip = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = layers.Add()([l, l_skip])
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(2, 2),
r_num=1,
b_alphas=[1, 1, 1])(l)
l_skip = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = layers.Add()([l, l_skip])
l1 = l
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(2, 2),
r_num=1,
b_alphas=[1, 1, 1])(l)
l_skip = ConvCapsuleLayer3D(kernel_size=3,
num_capsule=32,
num_atoms=8,
strides=1,
padding='same',
routings=3)(l)
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = Conv2DCaps(32,
8,
kernel_size=(3, 3),
strides=(1, 1),
r_num=1,
b_alphas=[1, 1, 1])(l)
l = layers.Add()([l, l_skip])
l2 = l
la = FlattenCaps()(l2)
lb = FlattenCaps()(l1)
l = layers.Concatenate(axis=-2)([la, lb])
# l = Dropout(0.4)(l)
digits_caps = CapsuleLayer(num_capsule=n_class,
dim_capsule=32,
routings=routings,
channels=0,
name='digit_caps')(l)
l = CapsToScalars(name='capsnet')(digits_caps)
m_capsnet = models.Model(inputs=x, outputs=l, name='capsnet_model')
y = Input(shape=(n_class, ))
masked_by_y = Mask_CID()([digits_caps, y])
masked = Mask_CID()(digits_caps)
# Decoder Network
decoder = models.Sequential(name='decoder')
decoder.add(Dense(input_dim=32, activation="relu", output_dim=8 * 8 * 16))
decoder.add(Reshape((8, 8, 16)))
decoder.add(BatchNormalization(momentum=0.8))
decoder.add(Deconvolution2D(64, 3, 3, subsample=(1, 1),
border_mode='same'))
decoder.add(Deconvolution2D(32, 3, 3, subsample=(2, 2),
border_mode='same'))
decoder.add(Deconvolution2D(16, 3, 3, subsample=(2, 2),
border_mode='same'))
decoder.add(Deconvolution2D(8, 3, 3, subsample=(2, 2), border_mode='same'))
decoder.add(Deconvolution2D(3, 3, 3, subsample=(1, 1), border_mode='same'))
decoder.add(Activation("relu"))
decoder.add(Reshape(target_shape=(64, 64, 3), name='out_recon'))
train_model = models.Model(
[x, y], [m_capsnet.output, decoder(masked_by_y)])
eval_model = models.Model(x, [m_capsnet.output, decoder(masked)])
train_model.summary()
return train_model, eval_model