from keras.models import Model
from keras.layers import Input, Add, Activation, Dropout, GlobalAveragePooling2D, Dense
from keras.layers.convolutional import Convolution2D
from keras.layers.normalization import BatchNormalization
from keras import backend as K
def initial_conv(input):
x = Convolution2D(16, (3, 3), padding='same', kernel_initializer='he_normal',
use_bias=False)(input)
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
x = BatchNormalization(axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer='uniform')(x)
x = Activation('relu')(x)
return x
def expand_conv(init, base, k, strides=(1, 1)):
x = Convolution2D(base * k, (3, 3), padding='same', strides=strides, kernel_initializer='he_normal',
use_bias=False)(init)
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
x = BatchNormalization(axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer='uniform')(x)
x = Activation('relu')(x)
x = Convolution2D(base * k, (3, 3), padding='same', kernel_initializer='he_normal',
use_bias=False)(x)
skip = Convolution2D(base * k, (1, 1), padding='same', strides=strides, kernel_initializer='he_normal',
use_bias=False)(init)
m = Add()([x, skip])
return m
def conv_block(input, base, k=1, dropout=0.0):
init = input
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
x = BatchNormalization(axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer='uniform')(input)
x = Activation('relu')(x)
x = Convolution2D(base * k, (3, 3), padding='same', kernel_initializer='he_normal',
use_bias=False)(x)
if dropout > 0.0: x = Dropout(dropout)(x)
x = BatchNormalization(axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer='uniform')(x)
x = Activation('relu')(x)
x = Convolution2D(base * k, (3, 3), padding='same', kernel_initializer='he_normal',
use_bias=False)(x)
m = Add()([init, x])
return m
def create_wide_residual_network(input_dim, nb_classes=100, N=2, k=1, dropout=0.0, final_activation='softmax', verbose=1, name=None):
"""
Creates a Wide Residual Network with specified parameters
:param input: Input Keras object
:param nb_classes: Number of output classes
:param N: Depth of the network. Compute N = (n - 4) / 6.
Example : For a depth of 16, n = 16, N = (16 - 4) / 6 = 2
Example2: For a depth of 28, n = 28, N = (28 - 4) / 6 = 4
Example3: For a depth of 40, n = 40, N = (40 - 4) / 6 = 6
:param k: Width of the network.
:param dropout: Adds dropout if value is greater than 0.0
:param final_activation: Activation function of last layer
:param verbose: Debug info to describe created WRN
:return:
"""
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
ip = Input(shape=input_dim)
x = initial_conv(ip)
nb_conv = 4
for block_index, base in enumerate([16, 32, 64]):
x = expand_conv(x, base, k, strides=(2, 2) if block_index > 0 else (1, 1))
for i in range(N - 1):
x = conv_block(x, base, k, dropout)
nb_conv += 2
x = BatchNormalization(axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer='uniform')(x)
x = Activation('relu')(x)
x = GlobalAveragePooling2D()(x)
x = Dense(nb_classes, activation=final_activation, name = 'prob' if final_activation == 'softmax' else 'embedding')(x)
model = Model(ip, x, name=name)
if verbose: print("Wide Residual Network-%d-%d created." % (nb_conv, k))
return model
if __name__ == "__main__":
from keras.utils import plot_model
from keras.layers import Input
from keras.models import Model
init = (32, 32, 3)
wrn_28_10 = create_wide_residual_network(init, nb_classes=10, N=2, k=2, dropout=0.0)
wrn_28_10.summary()
plot_model(wrn_28_10, "WRN-16-2.png", show_shapes=True, show_layer_names=True)
