/
wrn.py
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wrn.py
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from ..imports import *
weight_decay = 0.0005
def initial_conv(input):
x = Convolution2D(16, (3, 3), padding='same', kernel_initializer='he_normal',
kernel_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',
kernel_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',
kernel_regularizer=l2(weight_decay),
use_bias=False)(x)
skip = Convolution2D(base * k, (1, 1), padding='same', strides=strides, kernel_initializer='he_normal',
kernel_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',
kernel_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',
kernel_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
channel_axis = -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',
kernel_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',
kernel_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
channel_axis = -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',
kernel_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',
kernel_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,
activation='softmax', 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, kernel_regularizer=l2(weight_decay), activation=activation)(x)
model = Model(ip, x)
if verbose: print("Wide Residual Network-%d-%d created." % (nb_conv, k))
return model
if __name__ == "__main__":
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)