Keras Lenet on MNIST

Keras Lenet on MNIST

from keras.models import Sequential

from keras.layers.convolutional import Convolution2D

from keras.layers.convolutional import MaxPooling2D

from keras.layers.core import Activation

from keras.layers.core import Flatten

from keras.layers.core import Dense



class LeNet:

    @staticmethod

    def build(width, height, depth, classes, weightsPath=None):

        # initialize the model

        model = Sequential()



        # first set of CONV => RELU => POOL

        model.add(Convolution2D(20, 5, 5, border_mode="same",

            input_shape=(depth, height, width)))

        model.add(Activation("relu"))

        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))



        # second set of CONV => RELU => POOL

        model.add(Convolution2D(50, 5, 5, border_mode="same"))

        model.add(Activation("relu"))

        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))



        # set of FC => RELU layers

        model.add(Flatten())

        model.add(Dense(500))

        model.add(Activation("relu"))



        # softmax classifier

        model.add(Dense(classes))

        model.add(Activation("softmax"))



        # if weightsPath is specified load the weights

        if weightsPath is not None:

            model.load_weights(weightsPath)



        return model