LeNet.py

from keras.utils.np_utils import to_categorical # convert to one-hot-encoding
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau


def LeNet():
    model = Sequential()

    model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',activation ='relu', input_shape = (28,28,1)))
    #model.add(MaxPool2D(pool_size=(2,2)))
    model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))
    model.add(Dropout(0.25))
    model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',activation ='relu'))
    model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
    model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',activation ='relu'))
    #model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(256, activation = "relu"))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation = "softmax"))

    return model

def generator():
    datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
        zoom_range = 0.1, # Randomly zoom image
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=False,  # randomly flip images
        vertical_flip=False)  # randomly flip images

    return datagen

def train(X_train,Y_train,X_val,Y_val,model,datagen):
    optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
    model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])
    learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',patience=3,verbose=1,factor=0.5,min_lr=0.00001)
    history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=128),epochs = 50, validation_data = (X_val,Y_val),
                              verbose = 1, steps_per_epoch=X_train.shape[0]
                              , callbacks=[learning_rate_reduction])

    return history