def __init__(self, weight_decay):
super(InceptionResNetB, self).__init__()
self.b1_conv = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b2_conv1 = BasicConv2D(filters=128,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b2_conv2 = BasicConv2D(filters=160,
kernel_size=(1, 7),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b2_conv3 = BasicConv2D(filters=192,
kernel_size=(7, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.conv = Conv2DLinear(filters=1152,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
def __init__(self, weight_decay):
super(InceptionResNetC, self).__init__()
self.b1_conv = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b2_conv1 = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b2_conv2 = BasicConv2D(filters=224,
kernel_size=(1, 3),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b2_conv3 = BasicConv2D(filters=256,
kernel_size=(3, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.conv = Conv2DLinear(filters=2144,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
def __init__(self, weight_decay):
super(Stem, self).__init__()
self.conv1 = BasicConv2D(filters=32,
kernel_size=(3, 3),
strides=2,
padding="valid",
weight_decay=weight_decay)
self.conv2 = BasicConv2D(filters=32,
kernel_size=(3, 3),
strides=1,
padding="valid",
weight_decay=weight_decay)
self.conv3 = BasicConv2D(filters=64,
kernel_size=(3, 3),
strides=1,
padding="same",
weight_decay=weight_decay)
self.maxpool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="valid")
self.conv4 = BasicConv2D(filters=80,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.conv5 = BasicConv2D(filters=192,
kernel_size=(3, 3),
strides=1,
padding="valid",
weight_decay=weight_decay)
self.conv6 = BasicConv2D(filters=256,
kernel_size=(3, 3),
strides=2,
padding="valid",
weight_decay=weight_decay)
def __init__(self, weight_decay):
super(ReductionB, self).__init__()
self.b1_maxpool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="valid")
self.b2_conv1 = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b2_conv2 = BasicConv2D(filters=384,
kernel_size=(3, 3),
strides=2,
padding="valid",
weight_decay=weight_decay)
self.b3_conv1 = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b3_conv2 = BasicConv2D(filters=288,
kernel_size=(3, 3),
strides=2,
padding="valid",
weight_decay=weight_decay)
self.b4_conv1 = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b4_conv2 = BasicConv2D(filters=288,
kernel_size=(3, 3),
strides=1,
padding="same",
weight_decay=weight_decay)
self.b4_conv3 = BasicConv2D(filters=320,
kernel_size=(3, 3),
strides=2,
padding="valid",
weight_decay=weight_decay)