tensorflow训练自己的数据集实现CNN图像分类2（保存模型&测试单张图片）

　　神经网络训练的时候，我们需要将模型保存下来，方便后面继续训练或者用训练好的模型进行测试。因此，我们需要创建一个saver保存模型。

1 def run_training():
2     data_dir = 'C:/Users/wk/Desktop/bky/dataSet/'
3     log_dir = 'C:/Users/wk/Desktop/bky/log/'
4     image,label = inputData.get_files(data_dir)
5     image_batches,label_batches = inputData.get_batches(image,label,32,32,16,20)
6     print(image_batches.shape)
7     p = model.mmodel(image_batches,16)
8     cost = model.loss(p,label_batches)
9     train_op = model.training(cost,0.001)
10     acc = model.get_accuracy(p,label_batches)
11
12     sess = tf.Session()
13     init = tf.global_variables_initializer()
14     sess.run(init)
15     saver = tf.train.Saver()
16     coord = tf.train.Coordinator()
17     threads = tf.train.start_queue_runners(sess = sess,coord = coord)
18
19     try:
20        for step in np.arange(1000):
21            print(step)
22            if coord.should_stop():
23                break
24            _,train_acc,train_loss = sess.run([train_op,acc,cost])
25            print("loss:{} accuracy:{}".format(train_loss,train_acc))
26            if step % 100 == 0:
27                check = os.path.join(log_dir,"model.ckpt")
28                saver.save(sess,check,global_step = step)
29     except tf.errors.OutOfRangeError:
30         print("Done!!!")
31     finally:
32         coord.request_stop()
33     coord.join(threads)
34     sess.close()

　　训练好的模型信息会记录在checkpoint文件中，大致如下：

model_checkpoint_path: "C:/Users/wk/Desktop/bky/log/model.ckpt-100"
all_model_checkpoint_paths: "C:/Users/wk/Desktop/bky/log/model.ckpt-0"
all_model_checkpoint_paths: "C:/Users/wk/Desktop/bky/log/model.ckpt-100"

　　其余还会生成一些文件，分别记录了模型参数等信息，后边测试的时候程序会读取checkpoint文件去加载这些真正的数据文件



　　构建好神经网络进行训练完成后，如果用之前的代码直接进行测试，会报shape不符合的错误，大致是卷积层的输入与图像的shape不一致，这是因为上篇的代码，将weights和biases定义在了模型的外面，调用模型的时候，出现valueError的错误。



　　因此，我们需要将参数定义在模型里面，加载训练好的模型参数时，训练好的参数才能够真正初始化模型。重写模型函数如下

1 def mmodel(images,batch_size):
2     with tf.variable_scope('conv1') as scope:
3         weights = tf.get_variable('weights',
4                                   shape = [3,3,3, 16],
5                                   dtype = tf.float32,
6                                   initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
7         biases = tf.get_variable('biases',
8                                  shape=[16],
9                                  dtype=tf.float32,
10                                  initializer=tf.constant_initializer(0.1))
11         conv = tf.nn.conv2d(images, weights, strides=[1,1,1,1], padding='SAME')
12         pre_activation = tf.nn.bias_add(conv, biases)
13         conv1 = tf.nn.relu(pre_activation, name= scope.name)
14     with tf.variable_scope('pooling1_lrn') as scope:
15         pool1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1],strides=[1,2,2,1],
16                                padding='SAME', name='pooling1')
17         norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0,
18                           beta=0.75,name='norm1')
19     with tf.variable_scope('conv2') as scope:
20         weights = tf.get_variable('weights',
21                                   shape=[3,3,16,128],
22                                   dtype=tf.float32,
23                                   initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
24         biases = tf.get_variable('biases',
25                                  shape=[128],
26                                  dtype=tf.float32,
27                                  initializer=tf.constant_initializer(0.1))
28         conv = tf.nn.conv2d(norm1, weights, strides=[1,1,1,1],padding='SAME')
29         pre_activation = tf.nn.bias_add(conv, biases)
30         conv2 = tf.nn.relu(pre_activation, name='conv2')
31     with tf.variable_scope('pooling2_lrn') as scope:
32         norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001/9.0,
33                           beta=0.75,name='norm2')
34         pool2 = tf.nn.max_pool(norm2, ksize=[1,2,2,1], strides=[1,1,1,1],
35                                padding='SAME',name='pooling2')
36     with tf.variable_scope('local3') as scope:
37         reshape = tf.reshape(pool2, shape=[batch_size, -1])
38         dim = reshape.get_shape()[1].value
39         weights = tf.get_variable('weights',
40                                   shape=[dim,4096],
41                                   dtype=tf.float32,
42                                   initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
43         biases = tf.get_variable('biases',
44                                  shape=[4096],
45                                  dtype=tf.float32,
46                                  initializer=tf.constant_initializer(0.1))
47         local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
48     with tf.variable_scope('softmax_linear') as scope:
49         weights = tf.get_variable('softmax_linear',
50                                   shape=[4096, 2],
51                                   dtype=tf.float32,
52                                   initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
53         biases = tf.get_variable('biases',
54                                  shape=[2],
55                                  dtype=tf.float32,
56                                  initializer=tf.constant_initializer(0.1))
57         softmax_linear = tf.add(tf.matmul(local3, weights), biases, name='softmax_linear')
58     return softmax_linear

测试训练好的模型

首先获取一张测试图像

1 def get_one_image(img_dir):
2     image = Image.open(img_dir)
3     plt.imshow(image)
4     image = image.resize([32, 32])
5     image_arr = np.array(image)
6     return image_arr

加载模型，计算测试结果

1 def test(test_file):
2     log_dir = 'C:/Users/wk/Desktop/bky/log/'
3     image_arr = get_one_image(test_file)
4
5     with tf.Graph().as_default():
6         image = tf.cast(image_arr, tf.float32)
7         image = tf.image.per_image_standardization(image)
8         image = tf.reshape(image, [1,32, 32, 3])
9         print(image.shape)
10         p = model.mmodel(image,1)
11         logits = tf.nn.softmax(p)
12         x = tf.placeholder(tf.float32,shape = [32,32,3])
13         saver = tf.train.Saver()
14         with tf.Session() as sess:
15             ckpt = tf.train.get_checkpoint_state(log_dir)
16             if ckpt and ckpt.model_checkpoint_path:
17                 global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
18                 saver.restore(sess, ckpt.model_checkpoint_path)
19                 print('Loading success)
20             else:
21                 print('No checkpoint')
22             prediction = sess.run(logits, feed_dict={x: image_arr})
23             max_index = np.argmax(prediction)
24             print(max_index)

前面主要是将测试图片标准化为网络的输入图像，15-19是加载模型文件，然后将图像输入到模型里即可