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wide_residual_network.py
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wide_residual_network.py
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from keras.models import Model
from keras.layers import Input, Add, Activation, Dropout, Flatten, Dense
from keras.layers.convolutional import Convolution2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras import backend as K
weight_decay = 0.0005
def initial_conv(input):
x = Convolution2D(16, (3, 3), padding='same', kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
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',
W_regularizer=l2(weight_decay),
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',
W_regularizer=l2(weight_decay),
use_bias=False)(x)
skip = Convolution2D(base * k, (1, 1), padding='same', strides=strides, kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
use_bias=False)(init)
m = Add()([x, skip])
return m
def conv1_block(input, 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(16 * k, (3, 3), padding='same', kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
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(16 * k, (3, 3), padding='same', kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
use_bias=False)(x)
m = Add()([init, x])
return m
def conv2_block(input, k=1, dropout=0.0):
init = input
channel_axis = 1 if K.image_dim_ordering() == "th" else -1
x = BatchNormalization(axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer='uniform')(input)
x = Activation('relu')(x)
x = Convolution2D(32 * k, (3, 3), padding='same', kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
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(32 * k, (3, 3), padding='same', kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
use_bias=False)(x)
m = Add()([init, x])
return m
def conv3_block(input, k=1, dropout=0.0):
init = input
channel_axis = 1 if K.image_dim_ordering() == "th" else -1
x = BatchNormalization(axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer='uniform')(input)
x = Activation('relu')(x)
x = Convolution2D(64 * k, (3, 3), padding='same', kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
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(64 * k, (3, 3), padding='same', kernel_initializer='he_normal',
W_regularizer=l2(weight_decay),
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, verbose=1):
"""
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 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
x = expand_conv(x, 16, k)
nb_conv += 2
for i in range(N - 1):
x = conv1_block(x, 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 = expand_conv(x, 32, k, strides=(2, 2))
nb_conv += 2
for i in range(N - 1):
x = conv2_block(x, 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 = expand_conv(x, 64, k, strides=(2, 2))
nb_conv += 2
for i in range(N - 1):
x = conv3_block(x, 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 = AveragePooling2D((8, 8))(x)
x = Flatten()(x)
x = Dense(nb_classes, W_regularizer=l2(weight_decay), activation='softmax')(x)
model = Model(ip, x)
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)