evaluate_fashion.py

import pickle

from multiprocessing import Pool


from keras.optimizers import RMSprop

from keras.models import Sequential,Input,Model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU


from dataset import load_data
from config import Config
from utils import error




def createNetwork(X_train):
    model = Sequential()

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),activation='linear',input_shape=(28,28,1),padding='same'))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D((2, 2),padding='same'))
    model.add(Dropout(0.25))
    model.add(Conv2D(64, (3, 3), activation='linear',padding='same'))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
    model.add(Dropout(0.25))
    model.add(Conv2D(128, (3, 3), activation='linear',padding='same'))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
    model.add(Dropout(0.4))
    model.add(Flatten())
    model.add(Dense(128, activation='linear'))
    model.add(LeakyReLU(alpha=0.1))
    model.add(Dropout(0.3))
    model.add(Dense(10, activation='softmax'))
    
    model.compile(loss='categorical_crossentropy',
                  optimizer=RMSprop())

    return model
    

def evaluate(x, X_train, y_train, X_test, y_test):

    from numpy.random import seed
    seed(x)

    network = createNetwork(X_train)
    
    E_train, E_test = 0, 0
    network.fit(X_train, y_train,
                batch_size=Config.batch_size, nb_epoch=20, verbose=0)

    yy_train = network.predict(X_train)
    E_train = error(yy_train, y_train)
    
    yy_test = network.predict(X_test)
    E_test = error(yy_test, y_test)

    return E_train, E_test 
    


if __name__ == "__main__":
    # load the data sets
    X_train, y_train = load_data("data/fashion_mnist.train")
    X_test, y_test = load_data("data/fashion_mnist.test")


    def myeval(x):
        return evaluate(x, X_train, y_train, X_test, y_test)

    N = 10
    p = Pool(N)
    errors = list(p.map(myeval, range(N)))

    print(errors)
        
    E_train = sum([x[0] for x in errors]) / N
    E_test = sum([x[1] for x in errors]) / N

    print("E_train: {}".format(E_train))
    print("E_test: {}".format(E_test))