Convolution Neural Network (using FASHION-MNIST data)

[Code]

import numpy as np
import mnist_reader
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Activation
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from matplotlib import pyplot as plt
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import time
 
 
seed = 7
np.random.seed(seed)
#시간측정
start_time = time.time()
 
 
#data load
x_train, y_train = mnist_reader.load_mnist('data/', kind='train')
x_test, y_test = mnist_reader.load_mnist('data/', kind='t10k')
# flatten 28*28 images to a 784 vector for each image
num_pixels = 784
X_train = x_train.reshape(x_train.shape[0], num_pixels).astype('float32')
x_test = x_test.reshape(x_test.shape[0], num_pixels).astype('float32')
 
# normalize inputs from 0-255 to 0-1
x_train = x_train / 255
x_test = x_test / 255
 
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# define a simple DNN model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(1000, input_dim=num_pixels, init='normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.25))
    model.add(Activation('relu'))
    model.add(Dense(1500, input_dim=num_pixels, init='normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.25))
    model.add(Activation('relu'))
    model.add(Dense(2000, input_dim=num_pixels, init='normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.25))
    model.add(Activation('relu'))
    model.add(Dense(2500, input_dim=num_pixels, init='normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.25))
    model.add(Activation('relu'))
    model.add(Dense(3000, input_dim=num_pixels, init='normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.25))
    model.add(Activation('relu'))
    model.add(Dense(3000, input_dim=num_pixels, init='normal'))
    model.add(BatchNormalization())
    model.add(Dropout(0.25))
    model.add(Activation('relu'))
    model.add(Dense(num_classes, init='normal', activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model
# build the model
model = baseline_model()
# Fit the model
hist = model.fit(x_train, y_train, validation_data=(x_test, y_test), nb_epoch=12, batch_size=128, verbose=2)
# Final evaluation of the model
scores = model.evaluate(x_test, y_test, verbose=0)
print("DNN Error: %.2f%%" % (100-scores[1]*100))
 
 
#모델 시각
fig, loss_ax = plt.subplots()
 
acc_ax = loss_ax.twinx()
 
loss_ax.plot(hist.history['loss'], 'y', label='train loss')
loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')
 
acc_ax.plot(hist.history['acc'], 'b', label='train acc')
acc_ax.plot(hist.history['val_acc'], 'g', label='val acc')
 
loss_ax.set_xlabel('epoch')
loss_ax.set_ylabel('loss')
acc_ax. set_ylabel('accuracy')
 
loss_ax.legend(loc='upper left')
acc_ax.legend(loc='lower left')
 
plt.show()
 
#걸린시간
print("--- %s seconds ---" %(time.time() - start_time))

reference 

https://github.com/zalandoresearch/fashion-mnist

keras.io