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I have built a Binary Image classifier using Convolutional Neural Networks using TensorFlow.It is running fine, however, each time it takes too long to train from scratch. So, I want to save the trained model and load it next time. I can't seem to understand how to implement these guides in my program as shown in the TensorFlow documentation.
Here's the full code:

# 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()

I have tried to use saver = tf.train.import_meta_graph('D:\Project\Final_Project\chest_xray\check_point-78.meta') to import the graph but I get this error

Traceback (most recent call last):

  File "D:/Project/Final_Project/chest_xray/Final_CNN.py", line 104, in <module>

    saver = tf.train.import_meta_graph('D:\Project\Final_Project\chest_xray\check_point-78.meta')

  File "C:UserswaqarAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythontrainingsaver.py", line 1674, in import_meta_graph

    meta_graph_or_file, clear_devices, import_scope, **kwargs)[0]

  File "C:UserswaqarAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythontrainingsaver.py", line 1696, in _import_meta_graph_with_return_elements

    **kwargs))

  File "C:UserswaqarAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonframeworkmeta_graph.py", line 852, in import_scoped_meta_graph_with_return_elements

    ops.prepend_name_scope(value, scope_to_prepend_to_names))

  File "C:UserswaqarAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonframeworkops.py", line 3490, in as_graph_element

    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)

  File "C:UserswaqarAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonframeworkops.py", line 3550, in _as_graph_element_locked

    "graph." % repr(name))

KeyError: "The name 'Adam' refers to an Operation not in the graph."

Process finished with exit code 1