inc_res_v2_rest.py

#Inception-ResNet-A modules
x = incresA(x,0.15,name='incresA_1')
x = incresA(x,0.15,name='incresA_2')
x = incresA(x,0.15,name='incresA_3')
x = incresA(x,0.15,name='incresA_4')

#35 × 35 to 17 × 17 reduction module.
x_red_11 = MaxPooling2D(3,strides=2,padding='valid',name='red_maxpool_1')(x)

x_red_12 = conv2d(x,384,3,2,'valid',True,name='x_red1_c1')

x_red_13 = conv2d(x,256,1,1,'same',True,name='x_red1_c2_1')
x_red_13 = conv2d(x_red_13,256,3,1,'same',True,name='x_red1_c2_2')
x_red_13 = conv2d(x_red_13,384,3,2,'valid',True,name='x_red1_c2_3')

x = Concatenate(axis=3, name='red_concat_1')([x_red_11,x_red_12,x_red_13])

#Inception-ResNet-B modules
x = incresB(x,0.1,name='incresB_1')
x = incresB(x,0.1,name='incresB_2')
x = incresB(x,0.1,name='incresB_3')
x = incresB(x,0.1,name='incresB_4')
x = incresB(x,0.1,name='incresB_5')
x = incresB(x,0.1,name='incresB_6')
x = incresB(x,0.1,name='incresB_7')

#17 × 17 to 8 × 8 reduction module.
x_red_21 = MaxPooling2D(3,strides=2,padding='valid',name='red_maxpool_2')(x)

x_red_22 = conv2d(x,256,1,1,'same',True,name='x_red2_c11')
x_red_22 = conv2d(x_red_22,384,3,2,'valid',True,name='x_red2_c12')

x_red_23 = conv2d(x,256,1,1,'same',True,name='x_red2_c21')
x_red_23 = conv2d(x_red_23,256,3,2,'valid',True,name='x_red2_c22')

x_red_24 = conv2d(x,256,1,1,'same',True,name='x_red2_c31')
x_red_24 = conv2d(x_red_24,256,3,1,'same',True,name='x_red2_c32')
x_red_24 = conv2d(x_red_24,256,3,2,'valid',True,name='x_red2_c33')

x = Concatenate(axis=3, name='red_concat_2')([x_red_21,x_red_22,x_red_23,x_red_24])

#Inception-ResNet-C modules
x = incresC(x,0.2,name='incresC_1')
x = incresC(x,0.2,name='incresC_2')
x = incresC(x,0.2,name='incresC_3')

#TOP
x = GlobalAveragePooling2D(data_format='channels_last')(x)
x = Dropout(0.6)(x)
x = Dense(num_classes, activation='softmax')(x)

is there pretrained wieghts for this model