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deepcaps.py
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/
deepcaps.py
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from keras import backend as K
from keras import layers, models, optimizers
from keras.layers import Layer
from keras.layers import Input, Conv2D, Activation, Dense, Dropout, Lambda, Reshape, Concatenate
from keras.layers import BatchNormalization, MaxPooling2D, Flatten, Conv1D, Deconvolution2D, Conv2DTranspose
from keras.callbacks import Callback, ModelCheckpoint, TensorBoard
from keras.utils import plot_model
from keras.layers.convolutional import UpSampling2D
import numpy as np
import tensorflow as tf
import os
from PIL import Image
from capslayers import Conv2DCaps, ConvCapsuleLayer3D, CapsuleLayer, CapsToScalars, Mask_CID, Mask, ConvertToCaps, FlattenCaps
# To limit the GPU usage
# config = tf.ConfigProto()
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# K.set_session(sess)
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
def DeepCapsNet28(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=7 * 7 * 16))
decoder.add(Reshape((7, 7, 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(1, 3, 3, subsample=(1, 1), border_mode='same'))
decoder.add(Activation("relu"))
decoder.add(Reshape(target_shape=(28, 28, 1), 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 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