def get_model(protos_per_class=1):
inputs = Input(shape=input_shape)
diss = TangentDistance(linear_factor=None,
squared_dissimilarity=True,
projected_atom_shape=12,
signal_output='signals')
caps = Capsule(prototype_distribution=(protos_per_class, 10))
caps.add(
InputModule(signal_shape=(-1, np.prod(input_shape)),
trainable=False,
init_diss_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(NearestCompetition())
output = caps(inputs)[1]
# pre-train the model over 10000 random digits
idx = np.random.randint(0, len(x_train) - 1, (min(10000, len(x_train)), ))
pre_train_model = Model(inputs=inputs, outputs=diss.input[0])
diss_input = pre_train_model.predict(x_train[idx, :],
batch_size=batch_size)
diss.pre_training(diss_input, y_train[idx], capsule_inputs_are_equal=True)
# define model and return
model = Model(inputs, output)
return model
def get_model():
inputs = Input(shape=input_shape)
# get the dissimilarity either GLVQ, GTLVQ, or GMLVQ
if args.mode == 'glvq':
diss = MinkowskiDistance(linear_factor=None,
squared_dissimilarity=True,
signal_output='signals')
elif args.mode == 'gtlvq':
diss = RestrictedTangentDistance(linear_factor=None,
squared_dissimilarity=True,
signal_output='signals',
projected_atom_shape=1)
elif args.mode == 'gmlvq':
# get identity matrices for the matrix initialization as we do not
# use the standard routine of anysma for the initialization
matrix_init = np.repeat(np.expand_dims(np.eye(2), 0),
repeats=np.sum(protos_per_class),
axis=0)
matrix_init.astype(K.floatx())
diss = OmegaDistance(linear_factor=None,
squared_dissimilarity=True,
signal_output='signals',
matrix_scope='local',
matrix_constraint='OmegaNormalization',
matrix_initializer=lambda x: matrix_init)
# define capsule network
caps = Capsule(prototype_distribution=protos_per_class)
caps.add(
InputModule(signal_shape=(-1, np.prod(input_shape)),
trainable=False,
init_diss_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(NearestCompetition())
output = caps(inputs)[1]
# pre-train the model and overwrite the standard initialization matrix
# for GMLVQ
if args.mode == 'gmlvq':
_, matrices = diss.get_weights()
pre_train_model = Model(inputs=inputs, outputs=diss.input[0])
diss_input = pre_train_model.predict(x_train, batch_size=batch_size)
diss.pre_training(diss_input, y_train, capsule_inputs_are_equal=True)
if args.mode == 'gmlvq':
# set identity matrices
centers, _ = diss.get_weights()
diss.set_weights([centers, matrices])
# define model and return
model = Model(inputs, output)
return model
def get_model(protos_per_class=1):
inputs = Input(shape=input_shape)
diss = OmegaDistance(linear_factor=None,
squared_dissimilarity=True,
matrix_scope='global',
matrix_constraint='OmegaNormalization',
signal_output='signals',
matrix_initializer='identity')
caps = Capsule(prototype_distribution=(protos_per_class, 10))
caps.add(InputModule(signal_shape=(-1, np.prod(input_shape)),
trainable=False,
init_diss_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(NearestCompetition())
output = caps(inputs)[1]
# pre-train the model over 10000 random digits
# skip the svd for GMLVQ
_, matrix = diss.get_weights()
idx = np.random.randint(0, len(x_train) - 1, (min(10000, len(x_train)),))
pre_train_model = Model(inputs=inputs, outputs=diss.input[0])
diss_input = pre_train_model.predict(x_train[idx, :],
batch_size=batch_size)
diss.pre_training(diss_input, y_train[idx], capsule_inputs_are_equal=True)
# set identity matrices
centers, _ = diss.get_weights()
diss.set_weights([centers, matrix])
# define model and return
model = Model(inputs, output)
return model
def get_model():
inputs = Input(shape=input_shape)
diss = TangentDistance(linear_factor=None,
squared_dissimilarity=True,
projected_atom_shape=15,
signal_output='signals')
# compute the prototype distribution
proto_distrib = list(np.minimum(
np.ceil(np.sum(y_train, 0) / 100).astype('int'), 5))
# class 'Buildings-Grass-Trees-Drives'
proto_distrib[-2] = 2
# class 'Corn-mintill'
proto_distrib[2] = 4
print('proto_distrib: ' + str(proto_distrib))
# define capsule network
caps = Capsule(prototype_distribution=proto_distrib)
caps.add(InputModule(signal_shape=(-1, np.prod(input_shape)),
trainable=False,
init_diss_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(NearestCompetition())
output = caps(inputs)[1]
# pre-train the model
pre_train_model = Model(inputs=inputs, outputs=diss.input[0])
diss_input = pre_train_model.predict(x_train, batch_size=batch_size)
diss.pre_training(diss_input, y_train, capsule_inputs_are_equal=True)
# define model and return
model = Model(inputs, output)
return model
def CapsNet(input_shape, n_class, routings):
""" Initialize the CapsNet"""
x = layers.Input(shape=input_shape)
# Layer 1: Just a conventional Conv2D layer
digitcaps = layers.Conv2D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)
# Layer 2: Conv2D layer with `squash` activation, then reshape to [None, num_capsule, dim_capsule]
primary_caps = Capsule(name='PrimaryCaps')
primary_caps.add(layers.Conv2D(filters=8 * 32, kernel_size=9, strides=2, padding='valid', name='primarycap_conv2d'))
primary_caps.add(layers.Reshape(target_shape=[-1, 8], name='primarycap_reshape'))
primary_caps.add(layers.Lambda(squash, name='primarycap_squash'))
digitcaps = primary_caps(digitcaps)
# Layer 3: Capsule layer. Routing algorithm works here.
digit_caps = Capsule(name='digitcaps', prototype_distribution=(1, n_class))
digit_caps.add(InputModule(signal_shape=None, init_diss_initializer='zeros', trainable=False))
digit_caps.add(LinearTransformation(output_dim=16, scope='local'))
digit_caps.add(DynamicRouting(iterations=routings, name='capsnet'))
digitcaps = digit_caps(digitcaps)
# Decoder network.
y = layers.Input(shape=(n_class,))
masked_by_y = Mask()([digitcaps[0], y]) # The true label is used to mask the output of capsule layer. For training
masked = Mask()([digitcaps[0], digitcaps[2]]) # Mask using the capsule with maximal length. For prediction
# Shared Decoder model in training and prediction
decoder = models.Sequential(name='decoder')
decoder.add(layers.Dense(512, activation='relu', input_dim=16 * n_class))
decoder.add(layers.Dense(1024, activation='relu'))
decoder.add(layers.Dense(np.prod(input_shape), activation='sigmoid'))
decoder.add(layers.Reshape(target_shape=input_shape, name='out_recon'))
# Models for training and evaluation (prediction)
train_model = models.Model([x, y], [digitcaps[2], decoder(masked_by_y)])
eval_model = models.Model(x, [digitcaps[2], decoder(masked)])
# manipulate model
noise = layers.Input(shape=(n_class, 16))
noised_digitcaps = layers.Add()([digitcaps[0], 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
x_train, y_train = tecator.load_data()
x_train = x_train.astype('float32')
y_train = to_categorical(y_train, num_classes).astype('float32')
inputs = Input((x_train.shape[1], ))
# define MLVQ prototypes
diss = OmegaDistance(linear_factor=None,
squared_dissimilarity=True,
matrix_scope='global',
matrix_constraint='OmegaNormalization',
signal_output='signals')
caps = Capsule(prototype_distribution=(1, num_classes))
caps.add(
InputModule(signal_shape=x_train.shape[1],
trainable=False,
dissimilarity_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(Classification(probability_transformation='flip', name='lvq_capsule'))
outputs = caps(inputs)
# pre-train the model
pre_train_model = Model(inputs=inputs, outputs=diss.input[0])
diss_input = pre_train_model.predict(x_train, batch_size=batch_size)
diss.pre_training(diss_input, y_train, capsule_inputs_are_equal=True)
def LvqCapsNet(input_shape):
input_img = Input(shape=input_shape)
# Block 1
caps0 = Capsule()
caps0.add(Conv2D(32 + 1, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps0.add(BatchNormalization())
caps0.add(Activation('relu'))
caps0.add(Dropout(0.25))
x = caps0(input_img)
# Block 2
caps1 = Capsule()
caps1.add(Conv2D(64 + 1, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps1.add(BatchNormalization())
caps1.add(Activation('relu'))
caps1.add(Dropout(0.25))
x = caps1(x)
# Block 3
caps2 = Capsule()
caps2.add(Conv2D(64 + 1, (3, 3), padding='same', kernel_initializer=RandomNormal(stddev=0.01)))
caps2.add(BatchNormalization())
caps2.add(Activation('relu'))
caps2.add(Dropout(0.25))
x = caps2(x)
# Block 4
caps3 = Capsule(prototype_distribution=32)
caps3.add(Conv2D(64 + 1, (5, 5), strides=2, padding='same', kernel_initializer=RandomNormal(stddev=0.01)))
caps3.add(BatchNormalization())
caps3.add(Activation('relu'))
caps3.add(Dropout(0.25))
x = caps3(x)
# Block 5
caps4 = Capsule()
caps4.add(Conv2D(32 + 1, (3, 3), padding='same', kernel_initializer=RandomNormal(stddev=0.01)))
caps4.add(Dropout(0.25))
x = caps4(x)
# Block 6
caps5 = Capsule()
caps5.add(Conv2D(64 + 1, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps5.add(Dropout(0.25))
x = caps5(x)
# Block 7
caps6 = Capsule()
caps6.add(Conv2D(64 + 1, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps6.add(SplitModule())
caps6.add(Activation('relu'), scope_keys=1)
caps6.add(Flatten(), scope_keys=1)
x = caps6(x)
# Caps1
caps7 = Capsule(prototype_distribution=(1, 8 * 8))
caps7.add(InputModule(signal_shape=(-1, 64), init_diss_initializer=None, trainable=False))
diss7 = TangentDistance(squared_dissimilarity=False, epsilon=1.e-12, linear_factor=0.66, projected_atom_shape=16)
caps7.add(diss7)
caps7.add(GibbsRouting(norm_axis='channels', trainable=False))
x = caps7(x)
# Caps2
caps8 = Capsule(prototype_distribution=(1, 4 * 4))
caps8.add(Reshape((8, 8, 64)))
caps8.add(Conv2D(64, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps8.add(Conv2D(64, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps8.add(InputModule(signal_shape=(8 * 8, 64), init_diss_initializer=None, trainable=False))
diss8 = TangentDistance(projected_atom_shape=16, squared_dissimilarity=False,
epsilon=1.e-12, linear_factor=0.66, signal_output='signals')
caps8.add(diss8)
caps8.add(GibbsRouting(norm_axis='channels', trainable=False))
x = caps8(x)
# Caps3
digit_caps = Capsule(prototype_distribution=(1, 10))
digit_caps.add(Reshape((4, 4, 64)))
digit_caps.add(Conv2D(128, (3, 3), padding='same', kernel_initializer=glorot_normal()))
digit_caps.add(InputModule(signal_shape=128, init_diss_initializer=None, trainable=False))
diss = RestrictedTangentDistance(projected_atom_shape=16, epsilon=1.e-12, squared_dissimilarity=False,
linear_factor=0.66, signal_output='signals')
digit_caps.add(diss)
digit_caps.add(GibbsRouting(norm_axis='channels', trainable=False,
diss_regularizer=MaxValue(alpha=0.0001)))
digit_caps.add(DissimilarityTransformation(probability_transformation='neg_softmax', name='lvq_caps'))
digitcaps = digit_caps(x)
# intermediate model for Caps2; used for visualizations
input_diss8 = [Input((4, 4, 64)), Input((16,))]
model_vis_caps2 = models.Model(input_diss8, digit_caps(list_to_dict(input_diss8)))
# Decoder network.
y = layers.Input(shape=(10,))
masked_by_y = Mask()([digitcaps[0], y])
# Shared Decoder model in training and prediction
decoder = models.Sequential(name='decoder')
decoder.add(layers.Dense(512, activation='relu', input_dim=128 * 10))
decoder.add(layers.Dense(1024, activation='relu'))
decoder.add(layers.Dense(np.prod((28, 28, 1)), activation='sigmoid'))
decoder.add(layers.Reshape(target_shape=(28, 28, 1), name='out_recon'))
# Models for training and evaluation (prediction)
model = models.Model([input_img, y], [digitcaps[2], decoder(masked_by_y)])
return model, decoder, model_vis_caps2
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
y_train = to_categorical(y_train.astype('float32'))
y_test = to_categorical(y_test.astype('float32'))
if not os.path.exists(save_dir):
os.mkdir(save_dir)
inputs = Input(x_train.shape[1:])
# define MLVQ prototypes
diss = MinkowskiDistance(linear_factor=None,
squared_dissimilarity=True,
signal_output='signals')
caps = Capsule(prototype_distribution=(protos_per_class, num_classes))
caps.add(
InputModule(signal_shape=(1, ) + x_train.shape[1:],
trainable=False,
init_diss_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(NearestCompetition(use_for_loop=False))
caps.add(
DissimilarityTransformation(probability_transformation='flip',
name='lvq_capsule'))
outputs = caps(inputs)
# pre-train the model over 10000 random digits
idx = np.random.randint(0, len(x_train) - 1, (min(10000, len(x_train)), ))
def LvqCapsNet(input_shape, n_class):
"""
A LVQ-Capsule network on MNIST with Minkowski distance, neg_exp as probability transformation and pre-training.
"""
x = layers.Input(shape=input_shape)
# Layer 1: Just a conventional Conv2D layer
digitcaps = layers.Conv2D(filters=256,
kernel_size=9,
strides=1,
padding='valid',
activation='relu')(x)
digitcaps = layers.Conv2D(filters=8 * 32,
kernel_size=9,
strides=2,
padding='valid')(digitcaps)
# LVQ-Capsule
digit_caps = Capsule(name='digitcaps', prototype_distribution=(1, n_class))
digit_caps.add(
InputModule(signal_shape=8, dissimilarity_initializer='zeros'))
digit_caps.add(LinearTransformation(output_dim=16, scope='local'))
diss = MinkowskiDistance(prototype_initializer='zeros',
squared_dissimilarity=False,
name='minkowski_distance')
digit_caps.add(diss)
digit_caps.add(
GibbsRouting(norm_axis='channels',
trainable=False,
diss_regularizer='max_distance'))
digit_caps.add(
Classification(probability_transformation='neg_exp', name='lvq_caps'))
digitcaps = digit_caps(digitcaps)
# Decoder network.
y = layers.Input(shape=(n_class, ))
masked_by_y = Mask()([digitcaps[0], y])
masked = Mask()([digitcaps[0], digitcaps[2]])
# Shared Decoder model in training and prediction
decoder = models.Sequential(name='decoder')
decoder.add(layers.Dense(512, activation='relu', input_dim=16 * n_class))
decoder.add(layers.Dense(1024, activation='relu'))
decoder.add(layers.Dense(np.prod(input_shape), activation='sigmoid'))
decoder.add(layers.Reshape(target_shape=input_shape, name='out_recon'))
# Models for training and evaluation (prediction)
train_model = models.Model([x, y], [digitcaps[2], decoder(masked_by_y)])
eval_model = models.Model(x, [digitcaps[2], decoder(masked)])
# manipulate model
noise = layers.Input(shape=(n_class, 16))
noised_digitcaps = layers.Add()([digitcaps[0], 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, decoder
def LvqCapsNet(input_shape):
input_img = layers.Input(shape=input_shape)
# Block 1
caps0 = Capsule()
caps0.add(
Conv2D(32 + 2, (3, 3),
padding='same',
kernel_initializer=glorot_normal(),
input_shape=x_train.shape[1:]))
caps0.add(BatchNormalization())
caps0.add(Activation('relu'))
caps0.add(Dropout(0.25))
x = caps0(input_img)
# Block 2
caps1 = Capsule()
caps1.add(
Conv2D(64 + 2, (3, 3),
padding='same',
kernel_initializer=glorot_normal()))
caps1.add(BatchNormalization())
caps1.add(Activation('relu'))
caps1.add(Dropout(0.25))
x = caps1(x)
# Block 3
caps2 = Capsule()
caps2.add(
Conv2D(64 + 2, (3, 3),
padding='same',
kernel_initializer=RandomNormal(stddev=0.01)))
caps2.add(BatchNormalization())
caps2.add(Activation('relu'))
caps2.add(Dropout(0.25))
x = caps2(x)
# Block 4
caps3 = Capsule()
caps3.add(
Conv2D(64 + 2, (5, 5),
strides=2,
padding='same',
kernel_initializer=RandomNormal(stddev=0.01)))
caps3.add(BatchNormalization())
caps3.add(Activation('relu'))
caps3.add(Dropout(0.25))
x = caps3(x)
# Block 5
caps4 = Capsule()
caps4.add(
Conv2D(32 + 2, (3, 3),
padding='same',
kernel_initializer=RandomNormal(stddev=0.01)))
caps4.add(Dropout(0.25))
x = caps4(x)
# Block 6
caps5 = Capsule()
caps5.add(
Conv2D(64 + 2, (3, 3),
padding='same',
kernel_initializer=glorot_normal()))
caps5.add(Dropout(0.25))
x = caps5(x)
# Block 7
caps6 = Capsule()
caps6.add(
Conv2D(64 + 2, (3, 3),
padding='same',
kernel_initializer=glorot_normal()))
caps6.add(SplitModule(index=-2))
caps6.add(Activation('relu'), scope_keys=1)
caps6.add(Flatten(), scope_keys=1)
x = caps6(x)
# Caps1
caps7 = Capsule(prototype_distribution=(1, 8 * 8), sparse_signal=True)
caps7.add(
InputModule(signal_shape=(-1, 32),
init_diss_initializer=None,
trainable=False))
diss7 = TangentDistance(squared_dissimilarity=False,
epsilon=1.e-12,
linear_factor=0.66,
projected_atom_shape=16,
matrix_scope='global')
caps7.add(diss7)
caps7.add(GibbsRouting(norm_axis='channels', trainable=False))
x = caps7(x)
# Caps2
caps8 = Capsule(prototype_distribution=(1, 4 * 4), sparse_signal=True)
caps8.add(Reshape((8, 8, 32)))
caps8.add(
Conv2D(64, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps8.add(
Conv2D(64, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps8.add(
InputModule(signal_shape=(8 * 8, 64),
init_diss_initializer=None,
trainable=False))
diss8 = TangentDistance(projected_atom_shape=16,
squared_dissimilarity=False,
epsilon=1.e-12,
linear_factor=0.66,
signal_output='signals',
matrix_scope='global')
caps8.add(diss8)
caps8.add(GibbsRouting(norm_axis='channels', trainable=False))
x = caps8(x)
# Caps3
digit_caps = Capsule(prototype_distribution=(1, 5))
digit_caps.add(Reshape((4, 4, 64)))
digit_caps.add(
Conv2D(128, (3, 3), padding='same',
kernel_initializer=glorot_normal()))
digit_caps.add(
InputModule(signal_shape=128,
init_diss_initializer=None,
trainable=False))
diss = RestrictedTangentDistance(projected_atom_shape=16,
epsilon=1.e-12,
squared_dissimilarity=False,
linear_factor=0.66,
signal_output='signals')
digit_caps.add(diss)
digit_caps.add(
GibbsRouting(norm_axis='channels',
trainable=False,
diss_regularizer=MaxValue(alpha=0.0001)))
digit_caps.add(
DissimilarityTransformation(probability_transformation='neg_softmax',
name='lvq_caps'))
digitcaps = digit_caps(x)
model = models.Model(input_img, digitcaps[2])
return model
def LvqCapsNet(input_shape):
input_img = layers.Input(shape=input_shape)
# Block 1
caps1 = Capsule()
caps1.add(
Conv2D(32 + 1, (3, 3),
padding='same',
kernel_initializer=glorot_normal()))
caps1.add(BatchNormalization())
caps1.add(Activation('relu'))
caps1.add(Dropout(0.25))
x = caps1(input_img)
# Block 2
caps2 = Capsule()
caps2.add(
Conv2D(64 + 1, (3, 3),
padding='same',
kernel_initializer=glorot_normal()))
caps2.add(BatchNormalization())
caps2.add(Activation('relu'))
caps2.add(Dropout(0.25))
x = caps2(x)
# Block 3
caps3 = Capsule()
caps3.add(
Conv2D(64 + 1, (3, 3),
padding='same',
kernel_initializer=RandomNormal(stddev=0.01)))
caps3.add(BatchNormalization())
caps3.add(Activation('relu'))
caps3.add(Dropout(0.25))
x = caps3(x)
# Block 4
caps4 = Capsule(prototype_distribution=32)
caps4.add(
Conv2D(64 + 1, (5, 5),
strides=2,
padding='same',
kernel_initializer=RandomNormal(stddev=0.01)))
caps4.add(BatchNormalization())
caps4.add(Activation('relu'))
caps4.add(Dropout(0.25))
x = caps4(x)
# Block 5
caps5 = Capsule()
caps5.add(
Conv2D(32 + 1, (3, 3),
padding='same',
kernel_initializer=RandomNormal(stddev=0.01)))
caps5.add(Dropout(0.25))
x = caps5(x)
# Block 6
caps6 = Capsule()
caps6.add(
Conv2D(64 + 1, (3, 3),
padding='same',
kernel_initializer=glorot_normal()))
caps6.add(Dropout(0.25))
x = caps6(x)
# Block 7
x = Conv2D(64 + 1, (3, 3),
padding='same',
kernel_initializer=glorot_normal())(x)
x = [crop(3, 0, 64)(x), crop(3, 64, 65)(x)]
x[1] = Activation('relu')(x[1])
x[1] = Flatten()(x[1])
# Caps1
caps7 = Capsule(prototype_distribution=(1, 8 * 8))
caps7.add(
InputModule(signal_shape=(16 * 16, 64),
dissimilarity_initializer=None,
trainable=False))
diss7 = TangentDistance(squared_dissimilarity=False,
epsilon=1.e-12,
linear_factor=0.66,
projected_atom_shape=16)
caps7.add(diss7)
caps7.add(GibbsRouting(norm_axis='channels', trainable=False))
x = caps7(list_to_dict(x))
# Caps2
caps8 = Capsule(prototype_distribution=(1, 4 * 4))
caps8.add(Reshape((8, 8, 64)))
caps8.add(
Conv2D(64, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps8.add(
Conv2D(64, (3, 3), padding='same', kernel_initializer=glorot_normal()))
caps8.add(
InputModule(signal_shape=(8 * 8, 64),
dissimilarity_initializer=None,
trainable=False))
diss8 = TangentDistance(projected_atom_shape=16,
squared_dissimilarity=False,
epsilon=1.e-12,
linear_factor=0.66,
signal_output='signals')
caps8.add(diss8)
caps8.add(GibbsRouting(norm_axis='channels', trainable=False))
x = caps8(x)
# Caps3
digit_caps = Capsule(prototype_distribution=(1, 10))
digit_caps.add(Reshape((4, 4, 64)))
digit_caps.add(
Conv2D(128, (3, 3), padding='same',
kernel_initializer=glorot_normal()))
digit_caps.add(
InputModule(signal_shape=128,
dissimilarity_initializer=None,
trainable=False))
diss = RestrictedTangentDistance(projected_atom_shape=16,
epsilon=1.e-12,
squared_dissimilarity=False,
linear_factor=0.66,
signal_output='signals')
digit_caps.add(diss)
digit_caps.add(
GibbsRouting(norm_axis='channels',
trainable=False,
diss_regularizer=MaxDistance(alpha=0.0001)))
digit_caps.add(
Classification(probability_transformation='neg_softmax',
name='lvq_caps'))
digitcaps = digit_caps(x)
model = models.Model(input_img, digitcaps[2])
return model
x_test = np.expand_dims(x_test.astype('float32') / 255., -1)
y_train = to_categorical(y_train.astype('float32'))
y_test = to_categorical(y_test.astype('float32'))
if not os.path.exists(save_dir):
os.mkdir(save_dir)
inputs = Input(x_train.shape[1:])
# define rTDLVQ prototypes
diss = RestrictedTangentDistance(projected_atom_shape=num_tangents,
linear_factor=None,
squared_dissimilarity=True,
signal_output='signals')
caps = Capsule(prototype_distribution=(1, num_classes))
caps.add(
InputModule(signal_shape=(1, ) + x_train.shape[1:4],
trainable=False,
dissimilarity_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(NearestCompetition(use_for_loop=False))
caps.add(
Classification(probability_transformation='neg_softmax',
name='lvq_capsule'))
outputs = caps(inputs)
# pre-train the model over 10000 random digits
idx = np.random.randint(0, len(x_train) - 1, (min(10000, len(x_train)), ))
save_dir = save_dir_base + '/run_' + str(j)
if not os.path.exists(save_dir):
os.mkdir(save_dir)
# iterate over number of tangents
results = []
for i in range(max_number_tangents + 1):
number_tangents = i
# define define GTLVQ network as capsule
diss = TangentDistance(projected_atom_shape=number_tangents,
linear_factor=None,
squared_dissimilarity=True,
signal_output='signals')
caps = Capsule(prototype_distribution=(1, 10))
caps.add(
InputModule(signal_shape=(1, ) + x_train.shape[1:4],
trainable=False,
init_diss_initializer='zeros'))
caps.add(diss)
caps.add(SqueezeRouting())
caps.add(NearestCompetition(use_for_loop=False))
outputs = caps(inputs)[1]
# pre-train the model over 10000 random digits
idx = np.random.randint(0,
len(x_train) - 1, (min(10000, len(x_train)), ))
pre_train_model = Model(inputs=inputs, outputs=diss.input[0])
diss_input = pre_train_model.predict(x_train[idx, :],