深度學習花朵的識別（花朵+CNN+TensorFlow+多張識別）代碼

之前說過手寫數字的識別，那麼接下來交流一下花朵圖片的識別

親測可用，不能忽悠你！

本篇直接摟代碼，可能相關的深度學習的基本概念需要大家有一定的基礎，目前網上關於深度學習的理論也有系統和詳細的講解，我就不班門弄斧了

還是在代碼之前，先說一下花朵的數據集，網上花朵的數據集很多，如果沒有，推薦一個數據集下載地址：
http://download.tensorflow.org/example_images/flower_photos.tgz

ok，數據集包括5種花朵（daisy雛菊，dandelion蒲公英，rose玫瑰，sunflower向日葵，tulips鬱金香）

整個識別程序分成建模和分類兩大部分

（1）建立模型=導入庫+獲取數據集+輸入變換+搭建模型+小批量處理+訓練模型+保存模型

庫的選取

from skimage import io,transform   
import glob                        #查找目錄和文件模塊
import os                          #操作文件夾模塊
import tensorflow as tf            #tens框架
import numpy as np                 #數組函數包 
import time                        #時間模塊

獲取數據集

#數據集地址
path='E:/花朵分類/flower_photos/'
#模型保存地址
model_path='E:/花朵分類/ckpt_dir/model'
 

這時，哲學三問（我是誰，從哪來，到哪去）得到了完美回答。
程序知道了它要啥，輸入從哪來，輸出到哪去。

接下來就要完成文件夾中的圖片轉換成深度神經網絡理想的輸入形式
輸入變換

#將所有的圖片resize成100*100
w=100       #寬度
h=100       #高度
c=3         #深度
 
#讀取圖片
def read_img(path):
    cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
    imgs=[]
    labels=[]
    for idx,folder in enumerate(cate):
        for im in glob.glob(folder+'/*.jpg'):
            print('reading the images:%s'%(im))
            img=io.imread(im)
            img=transform.resize(img,(w,h))
            imgs.append(img)
            labels.append(idx)
    return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)
data,label=read_img(path)
  
#打亂順序
num_example=data.shape[0]
arr=np.arange(num_example)
np.random.shuffle(arr)
data=data[arr]
label=label[arr]
 
#將所有數據分爲訓練集和驗證集
ratio=0.8
s=np.int(num_example*ratio)
x_train=data[:s]
y_train=label[:s]
x_val=data[s:]
y_val=label[s:]

每塊的主要作用已經註釋在程序塊前，那麼我們想更好的理解，還需要稍微的深入研究一下。
enumerate( )：讀取當前路徑下文件夾，統計名稱和對應索引

glob.glob( )：批量抓取某種格式、或者以某個字符打頭的文件名

transform.resize( )：裁剪圖片，以float64的格式存儲，數值的取值範圍是（0~1）

np.asarray( )：保存對象的指針

np.arange( )：生成一個從start（包含）到stop（不包含），以step爲步長的序列。返回一個list對象。

在這段程序中，我們將花朵圖片處理成適合神經網絡輸入的形式，並對處理後的圖片進行標籤處理，隨機拆分成訓練集和測試集。

之後就到了我們的深度學習模型搭建的過程了
在此，搭建了一個卷積神經網絡用於特徵提取，話不多說，直接上代碼

def inference(input_tensor, train, regularizer):
    with tf.variable_scope('layer1-conv1'):
        conv1_weights = tf.get_variable("weight",[5,5,3,32],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))
        conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
 
    with tf.name_scope("layer2-pool1"):
        pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="VALID")
 
    with tf.variable_scope("layer3-conv2"):
        conv2_weights = tf.get_variable("weight",[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))
        conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
 
    with tf.name_scope("layer4-pool2"):
        pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
 
    with tf.variable_scope("layer5-conv3"):
        conv3_weights = tf.get_variable("weight",[3,3,64,128],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
        conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))
 
    with tf.name_scope("layer6-pool3"):
        pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
 
    with tf.variable_scope("layer7-conv4"):
        conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
        conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))
 
    with tf.name_scope("layer8-pool4"):
        pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
        nodes = 6*6*128
        reshaped = tf.reshape(pool4,[-1,nodes])
 
    with tf.variable_scope('layer9-fc1'):
        fc1_weights = tf.get_variable("weight", [nodes, 1024],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
        fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))
 
        fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
        if train: fc1 = tf.nn.dropout(fc1, 0.5)
 
    with tf.variable_scope('layer10-fc2'):
        fc2_weights = tf.get_variable("weight", [1024, 512],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
        fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))
 
        fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)
        if train: fc2 = tf.nn.dropout(fc2, 0.5)
 
    with tf.variable_scope('layer11-fc3'):
        fc3_weights = tf.get_variable("weight", [512, 5],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))
        fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))
        logit = tf.matmul(fc2, fc3_weights) + fc3_biases
 
    return logit

在此階段，之所以沒有對模型做過多的註釋，是因爲在此部分，可以隨便的增添或刪減自己的神經網絡，這部分不做過多的講解，目前網上優秀的模型非常多。

接下來幹嘛呢
爲了能更快的處理圖片，我們增加了一個小批量處理環節

#(小處理)將logits乘以1賦值給logits_eval，定義name，方便在後續調用模型時通過tensor名字調用輸出tensor
b = tf.constant(value=1,dtype=tf.float32)
logits_eval = tf.multiply(logits,b,name='logits_eval') 
 
loss=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)
train_op=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits,1),tf.int32), y_)    
acc= tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
#定義一個函數，按批次取數據
def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
    assert len(inputs) == len(targets)
    if shuffle:
        indices = np.arange(len(inputs))
        np.random.shuffle(indices)
    for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
        if shuffle:
            excerpt = indices[start_idx:start_idx + batch_size]
        else:
            excerpt = slice(start_idx, start_idx + batch_size)
        yield inputs[excerpt], targets[excerpt]

模型的最後肯定就是要訓練啦
那麼就開始吧！

n_epoch=10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           
batch_size=64
saver=tf.train.Saver()
sess=tf.Session()  
sess.run(tf.global_variables_initializer())
for epoch in range(n_epoch):
    start_time = time.time()
 
    #training
    train_loss, train_acc, n_batch = 0, 0, 0
    for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
        _,err,ac=sess.run([train_op,loss,acc], feed_dict={x: x_train_a, y_: y_train_a})
        train_loss += err; train_acc += ac; n_batch += 1
    print("   train loss: %f" % (np.sum(train_loss)/ n_batch))
    print("   train acc: %f" % (np.sum(train_acc)/ n_batch))
 
    #validation
    val_loss, val_acc, n_batch = 0, 0, 0
    for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
        err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a})
        val_loss += err; val_acc += ac; n_batch += 1
    print("   validation loss: %f" % (np.sum(val_loss)/ n_batch))
    print("   validation acc: %f" % (np.sum(val_acc)/ n_batch))
saver.save(sess,model_path)
sess.close()

訓練後不要忘記保存模型喲！

（2）分類程序
模型在手，不試試能行嗎？真金還需火煉！
Let‘s Go

分類程序=多張圖片導入+讀取圖片+導入模型+分類結果

先開始導入圖片

path1 = "E:/flower_photos/dandelion/822355d2f_n.jpg"
path2 = "E:/flower_photos/dandelion/7355d3078_m.jpg"
path3 = "E:/flower_photos/roses/39492cf8d_n.jpg"
path4 = "E:/flower_photos/sunflowers/69536bf4ea3.jpg"
path5 = "E:/flower_photos/tulips/107912168491604.jpg"
 
flower_dict = {0:'dasiy',1:'dandelion',2:'roses',3:'sunflowers',4:'tulips'}
 

簡單不，ok不，歐不，路徑一改，天下你有

讀取

w=100
h=100
c=3
 
def read_one_image(path):
    img = io.imread(path)
    img = transform.resize(img,(w,h))
    return np.asarray(img)
 

沒的問題是吧，和之前的一樣

接下來幹嘛，導入模型訓練那

with tf.Session() as sess:
    data = []
    data1 = read_one_image(path1)
    data2 = read_one_image(path2)
    data3 = read_one_image(path3)
    data4 = read_one_image(path4)
    data5 = read_one_image(path5)
    data.append(data1)
    data.append(data2)
    data.append(data3)
    data.append(data4)
    data.append(data5)
 
    saver = tf.train.import_meta_graph('E:/花朵分類/ckpt_dir/.meta')
    saver.restore(sess,tf.train.latest_checkpoint('E:/花朵分類/ckpt_dir/'))
 
    graph = tf.get_default_graph()
    x = graph.get_tensor_by_name("x:0")
    feed_dict = {x:data}
 
    logits = graph.get_tensor_by_name("logits_eval:0")
    classification_result = sess.run(logits,feed_dict)

這塊也不復雜

快出結果吧，等不及了都

    #打印出預測矩陣
    print(classification_result)
    #打印出預測矩陣每一行最大值的索引
    print(tf.argmax(classification_result,1).eval())
    #根據索引通過字典對應花的分類
    output = []
    output = tf.argmax(classification_result,1).eval()
    for i in range(len(output)):
        print("第",i+1,"朵花預測:"+flower_dict[output[

然後就可以看到非常非常理想的結果啦！

peace of the world