import numpy as np
from keras.models import Model
from keras.layers import Flatten, Dense, Input, merge
from keras.layers import Convolution3D, MaxPooling3D, BatchNormalization, Activation, AveragePooling3D
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
subsample = (subsample_factor, subsample_factor, subsample_factor)
x = BatchNormalization(axis=4)(input_tensor)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=subsample, border_mode='same')(x)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(x)
if subsample_factor > 1:
shortcut = Convolution3D(nb_filters, 1, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
else:
shortcut = input_tensor
x = merge([x, shortcut], mode='sum')
return x
def define_model():
img_input = Input(shape=(32, 32, 64, 1))
x = Convolution3D(16, 5, 5, 5, subsample=(1, 1, 1), border_mode='same')(img_input)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=2)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=2)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=2)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = AveragePooling3D(pool_size=(4, 4, 8))(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid', name='predictions')(x)
model = Model(img_input, x)
model.compile(optimizer='adam', loss='binary_crossentropy')
return model
