一、首先，簡單來說CNN卷積神經網路與BP神經網路主要區別在於：

1、網路的層數的多少（我這裡的CNN是比較簡單的，層數較少，真正應用的話，層數是很多的）。

2、CNN名稱來說，具有卷積運算的特點，對於大型的圖片或者數量多的圖片，卷積運算可以大量提高計算效能，而BP神經網路大都為全連線層，計算量本身就大，輸入大量的圖片，效能就不好了。

二、新建CNN檔案

import tensorflow as tf


def inference(images, batch_size, n_classes):
    # 一個簡單的卷積神經網路，卷積+池化層x2，全連線層x2，最後一個softmax層做分類。
    # 卷積層1
    # 64個3x3的卷積核（3通道），padding=’SAME’，表示padding後卷積的圖與原圖尺寸一致，啟用函式relu()
    with tf.variable_scope('conv1') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], stddev=1.0, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[64]),
                             name='biases', dtype=tf.float32)

        conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name=scope.name)

    # 池化層1
    # 3x3最大池化，步長strides為2，池化後執行lrn()操作，區域性響應歸一化，對訓練有利。
    with tf.variable_scope('pooling1_lrn') as scope:
        pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')
        norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')

    # 卷積層2
    # 16個3x3的卷積核（16通道），padding=’SAME’，表示padding後卷積的圖與原圖尺寸一致，啟用函式relu()
    with tf.variable_scope('conv2') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 16], stddev=0.1, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[16]),
                             name='biases', dtype=tf.float32)

        conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name='conv2')

    # 池化層2
    # 3x3最大池化，步長strides為2，池化後執行lrn()操作，
    # pool2 and norm2
    with tf.variable_scope('pooling2_lrn') as scope:
        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
        pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')

    # 全連線層3
    # 128個神經元，將之前pool層的輸出reshape成一行，啟用函式relu()
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.Variable(tf.truncated_normal(shape=[dim, 128], stddev=0.005, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),
                             name='biases', dtype=tf.float32)

        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)

    # 全連線層4
    # 128個神經元，啟用函式relu()
    with tf.variable_scope('local4') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[128, 128], stddev=0.005, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),
                             name='biases', dtype=tf.float32)

        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')

    # dropout層
    #    with tf.variable_scope('dropout') as scope:
    #        drop_out = tf.nn.dropout(local4, 0.8)

    # Softmax迴歸層
    # 將前面的FC層輸出，做一個線性迴歸，計算出每一類的得分，在這裡是2類，所以這個層輸出的是兩個得分。
    with tf.variable_scope('softmax_linear') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[128, n_classes], stddev=0.005, dtype=tf.float32),
                              name='softmax_linear', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[n_classes]),
                             name='biases', dtype=tf.float32)

        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')

    return softmax_linear


#loss計算
    #傳入引數：logits，網路計算輸出值。labels，真實值，在這裡是0或者1
    #返回引數：loss，損失值
def losses(logits, labels):
    with tf.variable_scope('loss') as scope:
        cross_entropy =tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.summary.scalar(scope.name+'/loss', loss)
    return loss


# loss損失值優化
# 輸入引數：loss。learning_rate，學習速率。
# 返回引數：train_op，訓練op，這個引數要輸入sess.run中讓模型去訓練。
def trainning(loss, learning_rate):
    with tf.name_scope('optimizer'):
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        global_step = tf.Variable(0, name='global_step', trainable=False)
        train_op = optimizer.minimize(loss, global_step=global_step)
    return train_op


# 評價/準確率計算
# 輸入引數：logits，網路計算值。labels，標籤，也就是真實值，在這裡是0或者1。
# 返回引數：accuracy，當前step的平均準確率，也就是在這些batch中多少張圖片被正確分類了。
def evaluation(logits, labels):
    with tf.variable_scope('accuracy') as scope:
        correct = tf.nn.in_top_k(logits, labels, 1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.summary.scalar(scope.name + '/accuracy', accuracy)
    return accuracy

 

這裡的網路為2個卷積層，2個池化層，2個全連線層，最後通過softmax層輸出。

三、新建TestCnn檔案

import os
import numpy as np
import tensorflow as tf
import CNN
import GetCnnData

#變數宣告
N_CLASSES = 0  #類別
IMG_W = 64   # resize影象，太大的話訓練時間久
IMG_H = 64
BATCH_SIZE =20
CAPACITY = 200
MAX_STEP = 2000 # 一般大於10K
learning_rate = 0.0001 # 一般小於0.0001

train_dir = r'E:\PycharmPython\NewCnn\train\train_data'  #訓練樣本的讀入
logs_train_dir = r'E:\PycharmPython\NewCnn\logs'              #logs儲存路徑

#計算分類類別
for str in os.listdir(train_dir):
    N_CLASSES = N_CLASSES+1

train,trian_label,val,val_label = GetCnnData.get_files(train_dir,0.3)
#訓練資料以及標籤
train_batch,train_label_batch = GetCnnData.get_batch(train,trian_label,IMG_W,IMG_H,BATCH_SIZE,CAPACITY)
#測試資料以及標籤
val_batch,val_label_batch = GetCnnData.get_batch(val,val_label,IMG_W,IMG_H,BATCH_SIZE,CAPACITY)

#訓練操作定義
train_logits = CNN.inference(train_batch,BATCH_SIZE,N_CLASSES)
train_loss = CNN.losses(train_logits, train_label_batch)
train_op = CNN.trainning(train_loss, learning_rate)
train_acc = CNN.evaluation(train_logits, train_label_batch)

#測試操作定義
test_logits = CNN.inference(val_batch, BATCH_SIZE, N_CLASSES)
test_loss = CNN.losses(test_logits, val_label_batch)
test_acc = CNN.evaluation(test_logits, val_label_batch)

#LOGS
summary_op = tf.summary.merge_all()

#定義一個會話
sess = tf.Session()
#寫logs檔案
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
#產生一個saver來儲存訓練好的模型
saver = tf.train.Saver()
#所有節點初始化
sess.run(tf.global_variables_initializer())
#佇列監控
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)


# 進行batch的訓練
try:
    # 執行MAX_STEP步的訓練，一步一個batch
    for step in np.arange(MAX_STEP):
        if coord.should_stop():
            break
        # 啟動以下操作節點，有個疑問，為什麼train_logits在這裡沒有開啟？
        _, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])

        # 每隔50步列印一次當前的loss以及acc，同時記錄log，寫入writer
        if step % 10 == 0:
            print('Step %d, train loss = %.2f, train accuracy = %.2f%%' % (step, tra_loss, tra_acc * 100.0))
            summary_str = sess.run(summary_op)
            train_writer.add_summary(summary_str, step)
        # 每隔100步，儲存一次訓練好的模型
        if (step + 1) == MAX_STEP:
            checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
            saver.save(sess, checkpoint_path, global_step=step)

except tf.errors.OutOfRangeError:
    print('Done training -- epoch limit reached')

finally:
    coord.request_stop()
 

這裡是對模型的訓練和模型的儲存。

連載：https://blog.csdn.net/qq_28821995/article/details/83587032             https://blog.csdn.net/qq_28821995/article/details/83587802

參考：https://blog.csdn.net/ywx1832990/article/details/78610711