lesson22-24 MNIST資料集，模組化搭建神經網路八股，手寫數字識別準確率輸出

阿新 • • 發佈：2018-12-14

import tensorflow as tf


#MNIST資料集輸出識別準確率
#MNIST資料集：
#提供6w張28*28畫素點的0-9手寫數字圖片和標籤，用於訓練
#提供1w張28*28畫素點的0-9手寫數字圖片和標籤，用於測試


#每張圖片的784個畫素點（28*28=784）組成長度為784的一維陣列。作為輸入特徵
#圖片的標籤以一維陣列形式給出，每個元素表示對應分類出現的 概率。


#tf.get_clooection("") #從集合中取全部變數，生成一個列表
#tf.add_n([]) #列表內對應元素相加
#tf.cast(x,dtype) #把x轉為dtype型別
#tf.argmax(x,axis) #返回最大值所在索引號 如：tf.argmax([1,0,0],1)返回0
#os.path,join("home","name")
#字串.split()#按指定拆分符對字串切片，返回分割後的列表
#如：‘./model/mnist_model-10001’.split('/')[-1].split('-')[-1] 返回1001

#with tf.Graph().as_default() as g:#其內定義的節點在計算圖g中

#mnist_app.py

def pre_pic(picName):
    img = Image.open

    
#前向傳播：mnist_forward.py
INPUT_MODE = 784
OUTPUT_MODE = 10
LAYER1_MODE = 500

def get_weight(shape, regularizer):
    w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
    if regularizer != None: 
        tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

def get_bias(shape):
    b = tf.Variable(tf.zeros(shape))
    return b

def forward(x, regularizer):
    w1 = get_weight([INPUT_MODE, LAYER1_MODE], regularizer)
    b1 = get_bias([LAYER1_MODE])
    y1 = tf.nn.relu(tf.matmul(x, w1) + b1)
    
    w2 = get_weight([LAYER1_MODE,OUTPUT_MODE], regularizer)
    b2 = get_bias([OUTPUT_MODE])
    y = tf.matmul(y1, w2) + b2
    return y

    



#反向傳播： mnist_backward.py
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os

BATCH_SIZE = 200
LEARNING_RATE_BASE = 0.1
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "./model/"
MODEL_NAME="mnist_model"
STEPS = 50000

def backward(mnist):
    
    x = tf.placeholder(tf.float32, [None, INPUT_MODE])
    y_ = tf.placeholder(tf.float32, [None,OUTPUT_MODE])
    y = forward(x, REGULARIZER)
    global_step = tf.Variable(0,trainable=False)
    #損失函式loss含正則化regularization
    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_,1))
    cem = tf.reduce_mean(ce)
    loss = cem + tf.add_n(tf.get_collection('losses'))
    #學習率
    learning_rate = tf.train.exponential_decay(
    LEARNING_RATE_BASE,
    global_step,
    mnist.train.num_examples / BATCH_SIZE,
    LEARNING_RATE_DECAY,
    staircase=True)
    
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
    #滑動平均
    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    ema_op = ema.apply(tf.trainable_variables())
    #with tf.control_dependencies(train_step, ema_op):
    with tf.control_dependencies([train_step, ema_op]):
        train_op = tf.no_op(name='train')
    
    #例項化saver物件   儲存模型
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()
        sess.run(init_op)
        
        for i in range(STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            _,loss_value,step= sess.run([train_op, loss, global_step], feed_dict={x:xs,y_:ys})
            if i % 1000 == 0:
                print(step,loss_value,saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step))

#測試輸出準確率 ： mnist_test.py

#def main():
#    mnist = input_data.read_data_sets("./date", one_hot=True)
#    backward(mnist)
    
#if __name__ == '__main__':
#    main()




#mnist_test.py
import time
TEST_INTERVAL_SECS=5


def test(mnist):
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32,[None,INPUT_MODE])
        y_=tf.placeholder(tf.float32,[None,OUTPUT_MODE])
        y = forward(x, None)
        
        #實列化可還原滑動平均值的saver
        ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
        ema_restore = ema.variables_to_restore()
        saver = tf.train.Saver(ema_restore)
        
        #準確率計算方法
        correct_prediction = tf.equal(tf.argmax(y,1), tf.arg_max(y_,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        
        while True:
            with tf.Session() as sess:
                #價值ckpt模型
                ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
                #如果已有ckpt模型則恢復
                if ckpt and ckpt.model_checkpoint_path:
                    #恢復會話
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    #恢復輪數
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    #計算準確率
                    accuracy_score = sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels})
                    #給出提示
                    print(global_step,accuracy_score)
                else:
                    print("No checkpoint file found")
                    return
            
            time.sleep(TEST_INTERVAL_SECS)
            
def main():
    mnist = input_data.read_data_sets("./data/",one_hot=True)
    test(mnist)
    
if __name__=='__main__':
    main()

Extracting ./date/train-images-idx3-ubyte.gz Extracting ./date/train-labels-idx1-ubyte.gz Extracting ./date/t10k-images-idx3-ubyte.gz Extracting ./date/t10k-labels-idx1-ubyte.gz (1, 4.678938, './model/mnist_model-1') (1001, 1.8297149, './model/mnist_model-1001') (2001, 1.7304844, './model/mnist_model-2001') (3001, 1.709178, './model/mnist_model-3001') (4001, 1.6456411, './model/mnist_model-4001') (5001, 1.6040242, './model/mnist_model-5001') (6001, 1.5808734, './model/mnist_model-6001') (7001, 1.5344688, './model/mnist_model-7001') (8001, 1.5165496, './model/mnist_model-8001') (9001, 1.5073484, './model/mnist_model-9001') (10001, 1.4617984, './model/mnist_model-10001') (11001, 1.4598992, './model/mnist_model-11001') (12001, 1.4594458, './model/mnist_model-12001') (13001, 1.4198472, './model/mnist_model-13001') (14001, 1.4244304, './model/mnist_model-14001') (15001, 1.4247683, './model/mnist_model-15001') (16001, 1.3590205, './model/mnist_model-16001') (17001, 1.354147, './model/mnist_model-17001') (18001, 1.3466628, './model/mnist_model-18001') (19001, 1.3093699, './model/mnist_model-19001') (20001, 1.2971283, './model/mnist_model-20001') (21001, 1.289797, './model/mnist_model-21001') (22001, 1.272865, './model/mnist_model-22001') (23001, 1.2688172, './model/mnist_model-23001') (24001, 1.2655104, './model/mnist_model-24001') (25001, 1.2440013, './model/mnist_model-25001') (26001, 1.2412161, './model/mnist_model-26001') (27001, 1.2221898, './model/mnist_model-27001') (28001, 1.2146492, './model/mnist_model-28001') (29001, 1.2083429, './model/mnist_model-29001') (30001, 1.2041415, './model/mnist_model-30001') (31001, 1.1921252, './model/mnist_model-31001') (32001, 1.1892465, './model/mnist_model-32001') (33001, 1.1844958, './model/mnist_model-33001') (34001, 1.172609, './model/mnist_model-34001') (35001, 1.1687734, './model/mnist_model-35001') (36001, 1.1579167, './model/mnist_model-36001') (37001, 1.1569072, './model/mnist_model-37001') (38001, 1.1456205, './model/mnist_model-38001') (39001, 1.1516279, './model/mnist_model-39001') (40001, 1.1405009, './model/mnist_model-40001') (41001, 1.1383176, './model/mnist_model-41001') (42001, 1.128678, './model/mnist_model-42001') (43001, 1.1366055, './model/mnist_model-43001') (44001, 1.1176779, './model/mnist_model-44001') (45001, 1.1101508, './model/mnist_model-45001') (46001, 1.109341, './model/mnist_model-46001') (47001, 1.1146913, './model/mnist_model-47001') (48001, 1.103515, './model/mnist_model-48001') (49001, 1.0985389, './model/mnist_model-49001')

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lesson22-24 MNIST資料集，模組化搭建神經網路八股，手寫數字識別準確率輸出

lesson22-24 MNIST資料集，模組化搭建神經網路八股，手寫數字識別準確率輸出

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TensorFlow筆記（3）——利用TensorFlow和MNIST資料集訓練一個最簡單的手寫數字識別模型...

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lesson22-24 MNIST資料集，模組化搭建神經網路八股，手寫數字識別準確率輸出

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