LeNet.py

import keras 
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

def Le_Net(input_shape,channels):
    model = Sequential()
    #Layer 1
    #Conv Layer 1
    model.add(Conv2D(filters = 6, 
                    kernel_size = 5, 
                    strides = 1, 
                    activation = 'relu', 
                    input_shape = input_shape))
    #Pooling layer 1
    model.add(MaxPooling2D(pool_size = 2, strides = 2))
    #Layer 2
    #Conv Layer 2
    model.add(Conv2D(filters = 16, 
                    kernel_size = 5,
                    strides = 1,
                    activation = 'relu',
                    input_shape = (14,14,6)))
    #Pooling Layer 2
    model.add(MaxPooling2D(pool_size = 2, strides = 2))
    #Flatten
    model.add(Flatten())
    #Layer 3
    #Fully connected layer 1
    model.add(Dense(units = 120, activation = 'relu'))
    #Layer 4
    #Fully connected layer 2
    model.add(Dense(units = 84, activation = 'relu'))
    #Layer 5
    #Output Layer
    model.add(Dense(units = channels, activation = 'softmax'))
    model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])

    return model