无法保存和加载用于二值图像分类的经过训练的CNN模型

Question

我用卷积神经网络建立了一个二值图像分类器TensorFlow。它运行良好，然而，每次从零开始训练都需要很长时间。所以，我想保存经过训练的模型，下次再加载它。我似乎不明白如何在程序中实现these指南，如TensorFlow文档所示。 以下是完整代码：

# Python program to create
# Image Classifier using CNN

# Importing the required libraries
import cv2
import os
import numpy as np
from random import shuffle
from tqdm import tqdm
from keras.models import Sequential
'''Setting up the env'''

TRAIN_DIR = 'D:\\Project\\Final_Project\\chest_xray\\train\\'
TEST_DIR = 'D:\\Project\\Final_Project\\chest_xray\\test0\\'
check_point = 'D:\\Project\\Final_Project\\chest_xray\\chkpt\\'
IMG_SIZE = 80
LR = 1e-4

'''Setting up the model which will help with tensorflow models'''
MODEL_NAME = 'NormalVsAbnormalXRays-{}-{}.model'.format(LR, '6conv-basic')

'''Labelling the dataset'''


def label_img(img):
    word_label = img.split('.')[-3]
    # DIY One hot encoder
    if word_label == 'Nor':
        return [1, 0]
    elif word_label == 'Pne':
        return [0, 1]
    else :
        return[0, 0]

'''Creating the training data'''


def create_train_data():
    # Creating an empty list where we should the store the training data
    # after a little preprocessing of the data
    training_data = []

    # tqdm is only used for interactive loading
    # loading the training data
    for img in tqdm(os.listdir(TRAIN_DIR)):
        # labeling the images
        label = label_img(img)

        path = os.path.join(TRAIN_DIR, img)

        # loading the image from the path and then converting them into
        # greyscale for easier covnet prob
        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)

        # resizing the image for processing them in the covnet
        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))

        # final step-forming the training data list with numpy array of the images
        training_data.append([np.array(img), np.array(label)])

        # shuffling of the training data to preserve the random state of our data
    shuffle(training_data)

    # saving our trained data for further uses if required
    np.save('train_data.npy', training_data)
    return training_data


'''Processing the given test data'''


# Almost same as processing the traning data but
# we dont have to label it.
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]
        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
        testing_data.append([np.array(img), img_num])

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


'''Running the training and the testing in the dataset for our model'''
#train_data = create_train_data()
#test_data = process_test_data()

train_data = np.load('train_data.npy')
test_data = np.load('test_data.npy')
'''Creating the neural network using tensorflow'''
# Importing the required libraries
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

import tensorflow as tf
model = Sequential()
tf.reset_default_graph()


saver = tf.train.import_meta_graph('D:\\Project\\Final_Project\\chest_xray\\check_point-78.meta')
convnet = input_data(shape=[None,IMG_SIZE, IMG_SIZE, 1], name='input')

convnet = conv_2d(convnet, 32, 4, activation='relu')
convnet = max_pool_2d(convnet, 2)


convnet = conv_2d(convnet, 64, 4, activation='relu')
convnet = max_pool_2d(convnet, 2)


convnet = conv_2d(convnet, 128, 4, activation='relu')
convnet = max_pool_2d(convnet, 2)

convnet = conv_2d(convnet, 64, 4, activation='relu')
convnet = max_pool_2d(convnet, 2)

convnet = conv_2d(convnet, 32, 4, activation='relu')
convnet = max_pool_2d(convnet, 2)

convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.3)

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', checkpoint_path='check_point',best_checkpoint_path= 'check_point',max_checkpoints= 5)

# Splitting the testing data and training data
train = train_data
test = train_data

'''Setting up the features and lables'''
# X-Features & Y-Labels

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

'''Fitting the data into our model'''
# epoch = 40 taken
model.fit({'input': X}, {'targets': Y}, n_epoch=1,
          validation_set=0.05,
          snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
model.save(MODEL_NAME)

'''Testing the data'''
import matplotlib.pyplot as plt

# if you need to create the data:
# test_data = process_test_data()
# if you already have some saved:
test_data = np.load('test_data.npy')

fig = plt.figure(figsize=(80,80))

for num, data in enumerate(test_data[:1]):


    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(1, 1, num + 1)
    orig = img_data
    data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)

    # model_out = model.predict([data])[0]
    model_out = model.predict([data])[0]

    if np.argmax(model_out) == 1:
        str_label = 'Abnormal'
    else:
        str_label = 'Normal'

    y.imshow(orig, cmap='gray')
    plt.title(str_label,fontsize=20)
    y.axes.get_xaxis().set_visible(False)
    y.axes.get_yaxis().set_visible(False)
 plt.show()

我尝试过使用saver=tf.train.import_meta_graph('D:\\Project\\Final_Project\\chest_xray\\check_point-78.meta')来导入图形，但是我得到了这个错误

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进程结束，退出代码为1