Python 3.x 如何用图像测试CNN模型？ 介绍/设置

我是编程新手，我从一个教程中制作了我的第一个CNN模型。 我已经在C:\Users\labadmin中设置了jupyter/tensorflow/keras

我所理解的是，为了实现用于测试和培训的数据，我只需要输入来自labadmin的路径

因为我不确定是什么导致了错误，所以我粘贴了整个代码和错误，我认为这是因为系统没有获得数据

包含以下数据设置的文件夹：

labadmin有一个名为data的文件夹，其中包含两个文件夹 培训和测试

猫图像和狗图像在这两个文件夹中都被洗牌。每个文件夹中有10000张图片，因此应该有足够的：

这篇教程教你。 1.如何创建模型 2.定义您的标签 3.创建您的培训数据 4.创建和构建层 5.创建您的测试数据 6.（据我所知）我创建的代码的最后一部分是
验证我的模型

这是密码 第二个出现在：print（'model loaded！'）

---

如果os.path.exists（“{}1st:警告消息很清楚，跟随它，警告就会消失。但是不要担心，如果不这样做，您仍然可以正常运行代码

第二：是的。如果未打印出
模型加载！
，则表示模型未加载，请检查模型文件的路径

第三：要在培训后保存模型，请使用
model.save（“PATH-To-save”）
。然后您可以通过
model.load（“PATH-To-model”）
加载它

对于预测，请使用
model.predict（{'input'：X}）
。请参见此处

第二项问题
要保存和加载模型，请使用
请记住，您应该具有模型文件的扩展名，即
.tflearn

要进行预测，需要加载图像，就像加载图像进行训练一样
谢谢你的快速回复！你能看看我发布的2.答案吗？我已经更新了答案。顺便说一下，你应该通过编辑主要问题将第二个问题从答案转移到主要问题。

    import cv2
    import numpy as np
    import os
    from random import shuffle
    from tqdm import tqdm

    TRAIN_DIR = "data\\training"
    TEST_DIR = "data\\test"
    IMG_SIZE = 50

    LR = 1e-3

    MODEL_NAME = 'dogvscats-{}-{}.model'.format(LR, '2cov-basic1')

    def label_img(img):
        word_label = img.split('.')[-3]
        if word_label == 'cat': return [1,0]
        elif word_label == 'dog': return [0,1]

    def creat_train_data():
        training_data = []
        for img in tqdm(os.listdir(TRAIN_DIR)):
            label = label_img(img)
            path = os.path.join(TRAIN_DIR,img)
            img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE), (IMG_SIZE,IMG_SIZE))
            training_data.append([np.array(img), np.array(label)])
        shuffle(training_data)
        np.save('training.npy', training_data) #save file
        return training_data

    import tflearn
    from tflearn.layers.conv import conv_2d, max_pool_2d
    from tflearn.layers.core import input_data, dropout, fully_connected
    from tflearn.layers.estimator import regression



    # Building convolutional convnet
    convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
    # http://tflearn.org/layers/conv/
    # http://tflearn.org/activations/
    convnet = conv_2d(convnet, 32, 2, activation='relu')
    convnet = max_pool_2d(convnet, 2)

    convnet = conv_2d(convnet, 64, 2, activation='relu')
    convnet = max_pool_2d(convnet, 2)

    convnet = fully_connected(convnet, 1024, activation='relu')
    convnet = dropout(convnet, 0.8)

    #OUTPUT layer
    convnet = fully_connected(convnet, 2, activation='softmax')
    convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')

    model = tflearn.DNN(convnet, tensorboard_dir='log')

    def process_test_data():
        testing_data = []
        for img in tqdm(os.listdir(TEST_DIR)):
            path = os.path.join(TEST_DIR,img)
            img_num = img.split ('.')[0]  #ID of pic=img_num
            img = cv2.resize(cv2-imread(path, cv2.IMREAD_GRAYSCALE),  (IMG_SIZE,IMG_SIZE))
            testing_data.append([np.array(img), img_num])

        np.save('test_data.npy', testing_data)
        return testing_data

    train_data = creat_train_data()
    #if you already have train data:
    #train_data = np.load('train_data.npy')
    100%|███████████████████████████████████████████████████████████████████████████| 21756/21756 [02:39<00:00, 136.07it/s]

    if os.path.exists('{}<.meta'.format(MODEL_NAME)):
        model.load(MODEL_NAME)
        print('model loaded!')

    train = train_data[:-500]
    test = train_data[:-500]

    X = np.array([i[0] for i in train]).reshape( -1, IMG_SIZE, IMG_SIZE, 1) #feature set
    Y= [i[1] for i in test] #label

    test_x = np.array([i[0] for i in train]).reshape( -1, IMG_SIZE, IMG_SIZE, 1) 
    test_y= [i[1] for i in test] 

    model.fit({'input': X}, {'targets': Y}, n_epoch=5, validation_set=({'input': test_x}, {'targets': test_y}), 
        snapshot_step=500, show_metric=True, run_id=MODEL_NAME)

    Training Step: 1664  | total loss: 9.55887 | time: 63.467s
    | Adam | epoch: 005 | loss: 9.55887 - acc: 0.5849 -- iter: 21248/21256
    Training Step: 1665  | total loss: 9.71830 | time: 74.722s
    | Adam | epoch: 005 | loss: 9.71830 - acc: 0.5779 | val_loss: 9.81653 - val_acc: 0.5737 -- iter: 21256/21256
    --



    curses is not supported on this machine (please install/reinstall curses for an optimal experience)
    WARNING:tensorflow:From C:\Users\labadmin\Miniconda3\envs\tensorflow\lib\site-packages\tflearn\initializations.py:119: UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) is deprecated and will be removed in a future version.
    Instructions for updating:
    Use tf.initializers.variance_scaling instead with distribution=uniform to get equivalent behavior.
    WARNING:tensorflow:From C:\Users\labadmin\Miniconda3\envs\tensorflow\lib\site-packages\tflearn\objectives.py:66: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
    Instructions for updating:
    keep_dims is deprecated, use keepdims instead


    if os.path.exists('{}<.meta'.format(MODEL_NAME)):
        model.load(MODEL_NAME)
        print('model loaded!')


# Save a model
model.save('path-to-folder-you-want-to-save/my_model.tflearn')
# Load a model
model.load('the-folder-where-your-model-located/my_model.tflearn')

test_image = cv2.resize(cv2.imread("path-of-the-image", cv2.IMREAD_GRAYSCALE),  (IMG_SIZE,IMG_SIZE))

test_image = np.array(test_image).reshape( -1, IMG_SIZE, IMG_SIZE, 1)

prediction = model.predict({'input': test_image })