Inception-V3-Keras.py

from keras.models import Model
from keras.layers.merge import concatenate
from keras.layers import Conv2D , MaxPool2D , Input , GlobalAveragePooling2D ,AveragePooling2D, Dense , Dropout ,Activation, Flatten , BatchNormalization


def conv_with_Batch_Normalisation(prev_layer , nbr_kernels , filter_Size , strides =(1,1) , padding = 'same'):
    x = Conv2D(filters=nbr_kernels, kernel_size = filter_Size, strides=strides , padding=padding)(prev_layer)
    x = BatchNormalization(axis=3)(x)
    x = Activation(activation='relu')(x)
    return x

def StemBlock(prev_layer):
    x = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 32, filter_Size=(3,3) , strides=(2,2))
    x = conv_with_Batch_Normalisation(x, nbr_kernels = 32, filter_Size=(3,3))
    x = conv_with_Batch_Normalisation(x, nbr_kernels = 64, filter_Size=(3,3))
    x = MaxPool2D(pool_size=(3,3) , strides=(2,2)) (x)
    x = conv_with_Batch_Normalisation(x, nbr_kernels = 80, filter_Size=(1,1))
    x = conv_with_Batch_Normalisation(x, nbr_kernels = 192, filter_Size=(3,3))
    x = MaxPool2D(pool_size=(3,3) , strides=(2,2)) (x)
    
    return x    
    

def InceptionBlock_A(prev_layer  , nbr_kernels):
    
    branch1 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 64, filter_Size = (1,1))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels=96, filter_Size=(3,3))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels=96, filter_Size=(3,3))
    
    branch2 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels=48, filter_Size=(1,1))
    branch2 = conv_with_Batch_Normalisation(branch2, nbr_kernels=64, filter_Size=(3,3)) # may be 3*3
    
    branch3 = AveragePooling2D(pool_size=(3,3) , strides=(1,1) , padding='same') (prev_layer)
    branch3 = conv_with_Batch_Normalisation(branch3, nbr_kernels = nbr_kernels, filter_Size = (1,1))
    
    branch4 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels=64, filter_Size=(1,1))
    
    output = concatenate([branch1 , branch2 , branch3 , branch4], axis=3)
    
    return output



def InceptionBlock_B(prev_layer , nbr_kernels):
    
    branch1 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = nbr_kernels, filter_Size = (1,1))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = nbr_kernels, filter_Size = (7,1))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = nbr_kernels, filter_Size = (1,7))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = nbr_kernels, filter_Size = (7,1))    
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 192, filter_Size = (1,7))
    
    branch2 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = nbr_kernels, filter_Size = (1,1))
    branch2 = conv_with_Batch_Normalisation(branch2, nbr_kernels = nbr_kernels, filter_Size = (1,7))
    branch2 = conv_with_Batch_Normalisation(branch2, nbr_kernels = 192, filter_Size = (7,1))
    
    branch3 = AveragePooling2D(pool_size=(3,3) , strides=(1,1) , padding ='same') (prev_layer)
    branch3 = conv_with_Batch_Normalisation(branch3, nbr_kernels = 192, filter_Size = (1,1))
    
    branch4 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 192, filter_Size = (1,1))
    
    output = concatenate([branch1 , branch2 , branch3 , branch4], axis = 3)
    
    return output    


    
def InceptionBlock_C(prev_layer):
    
    branch1 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 448, filter_Size = (1,1))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 384, filter_Size = (3,3))
    branch1_1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 384, filter_Size = (1,3))    
    branch1_2 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 384, filter_Size = (3,1))
    branch1 = concatenate([branch1_1 , branch1_2], axis = 3)
    
    branch2 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 384, filter_Size = (1,1))
    branch2_1 = conv_with_Batch_Normalisation(branch2, nbr_kernels = 384, filter_Size = (1,3))
    branch2_2 = conv_with_Batch_Normalisation(branch2, nbr_kernels = 384, filter_Size = (3,1))
    branch2 = concatenate([branch2_1 , branch2_2], axis = 3)
    
    branch3 = AveragePooling2D(pool_size=(3,3) , strides=(1,1) , padding='same')(prev_layer)
    branch3 = conv_with_Batch_Normalisation(branch3, nbr_kernels = 192, filter_Size = (1,1))
    
    branch4 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 320, filter_Size = (1,1))
    
    output = concatenate([branch1 , branch2 , branch3 , branch4], axis = 3)
    
    return output

def ReductionBlock_A(prev_layer):
    
    branch1 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 64, filter_Size = (1,1))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 96, filter_Size = (3,3))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 96, filter_Size = (3,3) , strides=(2,2) ) #, padding='valid'
    
    branch2 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 384, filter_Size=(3,3) , strides=(2,2) )
    
    branch3 = MaxPool2D(pool_size=(3,3) , strides=(2,2) , padding='same')(prev_layer)
    
    output = concatenate([branch1 , branch2 , branch3], axis = 3)
    
    return output

    

def ReductionBlock_B(prev_layer):
    
    branch1 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 192, filter_Size = (1,1))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 192, filter_Size = (1,7))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 192, filter_Size = (7,1))
    branch1 = conv_with_Batch_Normalisation(branch1, nbr_kernels = 192, filter_Size = (3,3) , strides=(2,2) , padding = 'valid')
    
    branch2 = conv_with_Batch_Normalisation(prev_layer, nbr_kernels = 192, filter_Size = (1,1) )
    branch2 = conv_with_Batch_Normalisation(branch2, nbr_kernels = 320, filter_Size = (3,3) , strides=(2,2) , padding='valid' )

    branch3 = MaxPool2D(pool_size=(3,3) , strides=(2,2) )(prev_layer)
    
    output = concatenate([branch1 , branch2 , branch3], axis = 3)
    
    return output

def auxiliary_classifier(prev_Layer):
    x = AveragePooling2D(pool_size=(5,5) , strides=(3,3)) (prev_Layer)
    x = conv_with_Batch_Normalisation(x, nbr_kernels = 128, filter_Size = (1,1))
    x = Flatten()(x)
    x = Dense(units = 768, activation='relu') (x)
    x = Dropout(rate = 0.2) (x)
    x = Dense(units = 1000, activation='softmax') (x)
    return x



def InceptionV3():
    
    input_layer = Input(shape=(299 , 299 , 3))
    
    x = StemBlock(input_layer)
    
    x = InceptionBlock_A(prev_layer = x ,nbr_kernels = 32)
    x = InceptionBlock_A(prev_layer = x ,nbr_kernels = 64)
    x = InceptionBlock_A(prev_layer = x ,nbr_kernels = 64)
    
    x = ReductionBlock_A(prev_layer = x )
    
    x = InceptionBlock_B(prev_layer = x  , nbr_kernels = 128)
    x = InceptionBlock_B(prev_layer = x , nbr_kernels = 160)
    x = InceptionBlock_B(prev_layer = x , nbr_kernels = 160)
    x = InceptionBlock_B(prev_layer = x , nbr_kernels = 192)
    
    Aux = auxiliary_classifier(prev_Layer = x)
    
    x = ReductionBlock_B(prev_layer = x)
    
    x = InceptionBlock_C(prev_layer = x)
    x = InceptionBlock_C(prev_layer = x)
    
    x = GlobalAveragePooling2D()(x)
    x = Dense(units=2048, activation='relu') (x)
    x = Dropout(rate = 0.2) (x)
    x = Dense(units=1000, activation='softmax') (x)
    
    model = Model(inputs = input_layer , outputs = [x , Aux] , name = 'Inception-V3')
    
    return model