def inner(x):
x = LayerNormalization()(x)
x = Activation("relu")(x)
x = Convolution2D(channels, 3, strides=strides, **params)(x)
x = Dropout(drop_rate)(x) if drop_rate > 0 else x
x = LayerNormalization()(x)
x = Activation("relu")(x)
x = Convolution2D(channels, 3, **params)(x)
return x
def wide_resnet_impl(input_shape, output_size):
def conv(channels,
strides,
params=dict(padding="same",
use_bias=False,
kernel_regularizer=l2(l2_reg))):
def inner(x):
x = LayerNormalization()(x)
x = Activation("relu")(x)
x = Convolution2D(channels, 3, strides=strides, **params)(x)
x = Dropout(drop_rate)(x) if drop_rate > 0 else x
x = LayerNormalization()(x)
x = Activation("relu")(x)
x = Convolution2D(channels, 3, **params)(x)
return x
return inner
def resize(x, shape):
if K.int_shape(x) == shape:
return x
channels = shape[3 if K.image_data_format() ==
"channels_last" else 1]
strides = K.int_shape(x)[2] // shape[2]
return Convolution2D(channels,
1,
padding="same",
use_bias=False,
strides=strides)(x)
def block(channels, k, n, strides):
def inner(x):
for i in range(n):
x2 = conv(channels * k, strides if i == 0 else 1)(x)
x = add([resize(x, K.int_shape(x2)), x2])
return x
return inner
# According to the paper L = 6*n+4
n = int((L - 4) / 6)
group0 = Convolution2D(16,
3,
padding="same",
use_bias=False,
kernel_regularizer=l2(l2_reg))
group1 = block(16, k, n, 1)
group2 = block(32, k, n, 2)
group3 = block(64, k, n, 2)
x_in = x = Input(shape=input_shape)
x = group0(x)
x = group1(x)
x = group2(x)
x = group3(x)
x = LayerNormalization()(x)
x = Activation("relu")(x)
x = GlobalAveragePooling2D()(x)
x = Dense(output_size, kernel_regularizer=l2(l2_reg))(x)
y = Activation("softmax")(x)
model = Model(inputs=x_in, outputs=y)
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
return model
