/
architecture.py
525 lines (492 loc) · 47.1 KB
/
architecture.py
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import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Activation, Input, Add, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, Concatenate, Lambda, add, GlobalAveragePooling2D, Convolution2D, LocallyConnected2D, ZeroPadding2D, concatenate, AveragePooling2D
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras import backend as K
def scaling(x, scale):
return x * scale
def InceptionResNetV2():
inputs = Input(shape=(160, 160, 3))
x = Conv2D(32, 3, strides=2, padding='valid', use_bias=False, name= 'Conv2d_1a_3x3') (inputs)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_1a_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_1a_3x3_Activation')(x)
x = Conv2D(32, 3, strides=1, padding='valid', use_bias=False, name= 'Conv2d_2a_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_2a_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_2a_3x3_Activation')(x)
x = Conv2D(64, 3, strides=1, padding='same', use_bias=False, name= 'Conv2d_2b_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_2b_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_2b_3x3_Activation')(x)
x = MaxPooling2D(3, strides=2, name='MaxPool_3a_3x3')(x)
x = Conv2D(80, 1, strides=1, padding='valid', use_bias=False, name= 'Conv2d_3b_1x1') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_3b_1x1_BatchNorm')(x)
x = Activation('relu', name='Conv2d_3b_1x1_Activation')(x)
x = Conv2D(192, 3, strides=1, padding='valid', use_bias=False, name= 'Conv2d_4a_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_4a_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_4a_3x3_Activation')(x)
x = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Conv2d_4b_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_4b_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_4b_3x3_Activation')(x)
# 5x Block35 (Inception-ResNet-A block):
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_1_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_1_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_1_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_1_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_1_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_1_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_1_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_1_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_1_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_2_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_2_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_2_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_2_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_2_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_2_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_2_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_2_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_3_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_3_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_3_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_3_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_3_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_3_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_3_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_3_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_4_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_4_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_4_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_4_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_4_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_4_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_4_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_4_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_5_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_5_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_5_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_5_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_5_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_5_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_5_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_5_Activation')(x)
# Mixed 6a (Reduction-A block):
branch_0 = Conv2D(384, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_6a_Branch_0_Conv2d_1a_3x3') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Mixed_6a_Branch_0_Conv2d_1a_3x3_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_6a_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_6a_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, 3, strides=1, padding='same', use_bias=False, name= 'Mixed_6a_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_6a_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_1 = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_6a_Branch_1_Conv2d_1a_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_6a_Branch_1_Conv2d_1a_3x3_Activation')(branch_1)
branch_pool = MaxPooling2D(3, strides=2, padding='valid', name='Mixed_6a_Branch_2_MaxPool_1a_3x3')(x)
branches = [branch_0, branch_1, branch_pool]
x = Concatenate(axis=3, name='Mixed_6a')(branches)
# 10x Block17 (Inception-ResNet-B block):
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_1_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_1_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_1_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_1_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_1_Branch_1_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_1_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_1_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_1_Branch_1_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_1_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_1_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_1_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_2_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_2_Branch_2_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_2_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_2_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_2_Branch_2_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_2_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_2_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_2_Branch_2_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_2_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_2_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_2_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_3_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_3_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_3_Branch_3_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_3_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_3_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_3_Branch_3_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_3_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_3_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_3_Branch_3_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_3_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_3_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_3_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_4_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_4_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_4_Branch_4_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_4_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_4_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_4_Branch_4_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_4_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_4_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_4_Branch_4_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_4_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_4_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_4_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_5_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_5_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_5_Branch_5_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_5_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_5_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_5_Branch_5_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_5_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_5_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_5_Branch_5_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_5_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_5_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_5_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_6_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_6_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_6_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_6_Branch_6_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_6_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_6_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_6_Branch_6_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_6_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_6_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_6_Branch_6_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_6_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_6_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_6_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_7_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_7_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_7_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_7_Branch_7_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_7_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_7_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_7_Branch_7_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_7_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_7_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_7_Branch_7_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_7_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_7_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_7_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_8_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_8_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_8_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_8_Branch_8_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_8_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_8_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_8_Branch_8_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_8_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_8_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_8_Branch_8_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_8_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_8_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_8_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_9_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_9_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_9_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_9_Branch_9_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_9_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_9_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_9_Branch_9_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_9_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_9_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_9_Branch_9_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_9_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_9_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_9_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_10_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_10_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_10_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_10_Branch_10_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_10_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_10_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_10_Branch_10_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_10_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_10_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_10_Branch_10_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_10_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_10_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_10_Activation')(x)
# Mixed 7a (Reduction-B block): 8 x 8 x 2080
branch_0 = Conv2D(256, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_0_Conv2d_0a_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Mixed_7a_Branch_0_Conv2d_0a_1x1_Activation')(branch_0)
branch_0 = Conv2D(384, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_7a_Branch_0_Conv2d_1a_3x3') (branch_0)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Mixed_7a_Branch_0_Conv2d_1a_3x3_Activation')(branch_0)
branch_1 = Conv2D(256, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_7a_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_7a_Branch_1_Conv2d_1a_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_7a_Branch_1_Conv2d_1a_3x3_Activation')(branch_1)
branch_2 = Conv2D(256, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Mixed_7a_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(256, 3, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Mixed_7a_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_7a_Branch_2_Conv2d_1a_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Mixed_7a_Branch_2_Conv2d_1a_3x3_Activation')(branch_2)
branch_pool = MaxPooling2D(3, strides=2, padding='valid', name='Mixed_7a_Branch_3_MaxPool_1a_3x3')(x)
branches = [branch_0, branch_1, branch_2, branch_pool]
x = Concatenate(axis=3, name='Mixed_7a')(branches)
# 5x Block8 (Inception-ResNet-C block):
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_1_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_1_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_1_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_1_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_1_Branch_1_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_1_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_1_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_1_Branch_1_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_1_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_1_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_1_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_2_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_2_Branch_2_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_2_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_2_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_2_Branch_2_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_2_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_2_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_2_Branch_2_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_2_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_2_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_2_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_3_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_3_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_3_Branch_3_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_3_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_3_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_3_Branch_3_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_3_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_3_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_3_Branch_3_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_3_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_3_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_3_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_4_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_4_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_4_Branch_4_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_4_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_4_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_4_Branch_4_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_4_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_4_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_4_Branch_4_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_4_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_4_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_4_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_5_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_5_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_5_Branch_5_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_5_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_5_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_5_Branch_5_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_5_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_5_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_5_Branch_5_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_5_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_5_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_5_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_6_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_6_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_1_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_1_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_6_Branch_1_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_1_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_1_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_6_Branch_1_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_6_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_6_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 1})(up)
x = add([x, up])
# Classification block
x = GlobalAveragePooling2D(name='AvgPool')(x)
x = Dropout(1.0 - 0.8, name='Dropout')(x)
# Bottleneck
x = Dense(128, use_bias=False, name='Bottleneck')(x)
x = BatchNormalization(momentum=0.995, epsilon=0.001, scale=False, name='Bottleneck_BatchNorm')(x)
# Create model
model = Model(inputs, x, name='inception_resnet_v1')
return model
# Create the FaceNet model
# face_encoder = InceptionResNetV2()
# # Load the weights of the model
# path = "facenet_keras_weights.h5"
# face_encoder.load_weights(path)