TFlearn——（2）SVHN

1，数据集简介

　　SVHN（Street View House Number）Dateset 来源于谷歌街景门牌号码，原生的数据集1也就是官网的 Format 1 是一些原始的未经处理的彩色图片，如下图所示（不含有蓝色的边框），下载的数据集含有 PNG 的图像和 digitStruct.mat 的文件，其中包含了边框的位置信息，这个数据集每张图片上有好几个数字，适用于 OCR 相关方向。

　　这里采用 Format2, Format2 将这些数字裁剪成32x32的大小，如图所示，并且数据是 .mat 文件。

2，数据处理

　　数据集含有两个变量 X 代表图像，训练集 X 的 shape 是（32，32，3，73257）也就是(width, height, channels, samples), tensorflow 的张量需要（samples, width, height, channels），所以需要转换一下，由于直接调用 cifar 10 的网络模型，数据只需要先做个归一化，所有像素除于255就 OK，另外原始数据 0 的标签是 10，这里要转化成 0，并提供 one_hot 编码。

#!/usr/bin/env python2

# -*- coding: utf-8 -*-

"""

Created on Thu Jan 19 09:55:36 2017

@author: cheers

"""

import scipy.io as sio

import matplotlib.pyplot as plt

import numpy as np

image_size = 32

num_labels = 10

def display_data():

    print 'loading Matlab data...'

    train = sio.loadmat('train_32x32.mat')

    data=train['X']

    label=train['y']

    for i in range(10):

        plt.subplot(2,5,i+1)

        plt.title(label[i][0])

        plt.imshow(data[...,i])

        plt.axis('off')

    plt.show()

def load_data(one_hot = False):

    train = sio.loadmat('train_32x32.mat')

    test = sio.loadmat('test_32x32.mat')

    train_data=train['X']

    train_label=train['y']

    test_data=test['X']

    test_label=test['y']

    train_data = np.swapaxes(train_data, 0, 3)

    train_data = np.swapaxes(train_data, 2, 3)

    train_data = np.swapaxes(train_data, 1, 2)

    test_data = np.swapaxes(test_data, 0, 3)

    test_data = np.swapaxes(test_data, 2, 3)

    test_data = np.swapaxes(test_data, 1, 2)

    test_data = test_data / 255.

    train_data =train_data / 255.

    for i in range(train_label.shape[0]):

         if train_label[i][0] == 10:

             train_label[i][0] = 0

    for i in range(test_label.shape[0]):

         if test_label[i][0] == 10:

             test_label[i][0] = 0

    if one_hot:

        train_label = (np.arange(num_labels) == train_label[:,]).astype(np.float32)

        test_label = (np.arange(num_labels) == test_label[:,]).astype(np.float32)

    return train_data,train_label, test_data,test_label

if __name__ == '__main__':

    load_data(one_hot = True)

    display_data()

3，TFearn 训练

注意 ImagePreprocessing 对数据做了 0 均值化。网络结构也比较简单，直接调用 TFlearn 的 cifar10 例子。

from __future__ import division, print_function, absolute_import

import tflearn

from tflearn.data_utils import shuffle, to_categorical

from tflearn.layers.core import input_data, dropout, fully_connected

from tflearn.layers.conv import conv_2d, max_pool_2d

from tflearn.layers.estimator import regression

from tflearn.data_preprocessing import ImagePreprocessing

from tflearn.data_augmentation import ImageAugmentation

# Data loading and preprocessing

import svhn_data as SVHN

X, Y, X_test, Y_test = SVHN.load_data(one_hot = True)

X, Y = shuffle(X, Y)

# Real-time data preprocessing

img_prep = ImagePreprocessing()

img_prep.add_featurewise_zero_center()

img_prep.add_featurewise_stdnorm()

# Convolutional network building

network = input_data(shape=[None, 32, 32, 3],

                     data_preprocessing=img_prep)

network = conv_2d(network, 32, 3, activation='relu')

network = max_pool_2d(network, 2)

network = conv_2d(network, 64, 3, activation='relu')

network = conv_2d(network, 64, 3, activation='relu')

network = max_pool_2d(network, 2)

network = fully_connected(network, 512, activation='relu')

network = dropout(network, 0.5)

network = fully_connected(network, 10, activation='softmax')

network = regression(network, optimizer='adam',

                     loss='categorical_crossentropy',

                     learning_rate=0.001)

# Train using classifier

model = tflearn.DNN(network, tensorboard_verbose=0)

model.fit(X, Y, n_epoch=15, shuffle=True, validation_set=(X_test, Y_test),

          show_metric=True, batch_size=96, run_id='svhn_cnn')

训练结果：

Training Step: 11452  | total loss: 0.68217 | time: 7.973s

| Adam | epoch: 015 | loss: 0.68217 - acc: 0.9329 -- iter: 72576/73257

Training Step: 11453  | total loss: 0.62980 | time: 7.983s

| Adam | epoch: 015 | loss: 0.62980 - acc: 0.9354 -- iter: 72672/73257

Training Step: 11454  | total loss: 0.58649 | time: 7.994s

| Adam | epoch: 015 | loss: 0.58649 - acc: 0.9356 -- iter: 72768/73257

Training Step: 11455  | total loss: 0.53254 | time: 8.005s

| Adam | epoch: 015 | loss: 0.53254 - acc: 0.9421 -- iter: 72864/73257

Training Step: 11456  | total loss: 0.49179 | time: 8.016s

| Adam | epoch: 015 | loss: 0.49179 - acc: 0.9416 -- iter: 72960/73257

Training Step: 11457  | total loss: 0.45679 | time: 8.027s

| Adam | epoch: 015 | loss: 0.45679 - acc: 0.9433 -- iter: 73056/73257

Training Step: 11458  | total loss: 0.42026 | time: 8.038s

| Adam | epoch: 015 | loss: 0.42026 - acc: 0.9469 -- iter: 73152/73257

Training Step: 11459  | total loss: 0.38929 | time: 8.049s

| Adam | epoch: 015 | loss: 0.38929 - acc: 0.9491 -- iter: 73248/73257

Training Step: 11460  | total loss: 0.35542 | time: 9.928s

| Adam | epoch: 015 | loss: 0.35542 - acc: 0.9542 | val_loss: 0.40315 - val_acc: 0.9085 -- iter: 73257/73257

巴特西

TFlearn——（2）SVHN

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