def capsule_net(input_shape,n_class,routings):
x=layers.Input(shape=input_shape)
conv1=layers.Conv2D(filters=256,kernel_size=9,strides=1,padding='valid',
activation='relu',name='conv1')(x)
primary_capsule=PrimaryCap(conv1,dim_capsule=8,n_channels=32,
kernel_size=9,strides=2,padding='valid')
digit_capsule=CapsuleLayer(output_dim_capsules=16,routings=routings,
output_num_capsules=n_class,name='digit_capsule')(primary_capsule)
output_capsules=Length(name='capsule_net')(digit_capsule)
y=layers.Input(shape=(n_class,))
masked_with_y=Mask()([digit_capsule,y])
masked=Mask(digit_capsule)
decoder=models.Sequential(name='decoder')
decoder.add(layers.Dense(units=512,activation='relu',input_dim=16*n_class))
decoder.add(layers.Dense(units=1024,activation='relu'))
decoder.add(layers.Dense(units=np.prod(input_shape),activation='softmax'))
decoder.add(layers.Reshape(target_shape=input_shape,name='output_reconstruction'))
train_model=models.Model([x,y],[output_capsules,decoder(masked_with_y)])
eval_model=models.Model(x,[output_capsules,decoder(masked)])
noise = layers.Input(shape=(n_class, 16))
noised_digitcaps = layers.Add()([digit_capsule, noise])
masked_noised_y = Mask()([noised_digitcaps, y])
manipulate_model = models.Model([x, y, noise], decoder(masked_noised_y))
return train_model, eval_model, manipulate_model
output=Conv1D(filters,
filter_size,
padding='valid',
activation='relu',
strides=1)(dropout)
dropout=Dropout(0.5)(output)
output=Conv1D(filters,
filter_size,
padding='valid',
activation='relu',
strides=1)(dropout)
dropout=Dropout(0.5)(output)
# output=GlobalAveragePooling1D()(dropout)
# dropout=Dropout(0.25)(output)
primary_caps = PrimaryCap(dropout, dim_vector=dim_capsule1, n_channels=3, kernel_size=9, strides=2, padding='same',
name="primary_caps")
primary_caps = BatchNormalization()(primary_caps)
primary_caps = Dropout(0.3)(primary_caps)
# Layer 3: Capsule layer. Routing algorithm works here.
category_caps = CategoryCap(num_capsule=num_capsule1, dim_vector=dim_capsule1, num_routing=num_routing, name='category_caps')(
primary_caps)
category_caps = BatchNormalization()(category_caps)
category_caps = Dropout(0.3)(category_caps)
print('category_caps',category_caps)
category_caps = Length(name='out_caps')(category_caps)
output=Dense(1,activation='sigmoid')(category_caps)
model=Model(inputs=inputs,outputs=[output])