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I am new to deep learning and tensorflow and i'm trying to create an image classifier using tensorflow that will classify 5 classes of images.
My training dataset is 25000 images and testing dataset is 5000 images.
below is my code:

import os

from random import shuffle



import cv2

import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

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

from tqdm import tqdm

import keras

from keras.models import save_model

"""from keras.models import Sequential

from keras.layers import Dense"""



TRAIN_DIR = 'train'

TEST_DIR = 'test'

IMG_SIZE = 16

LR = 1e-3

MODEL_NAME = 'cifar 10 -convnet'



def create_label(image_name):

    """ Create an one-hot encoded vector from image name """

    word_label = image_name.split('.')[0:2]

    if word_label == 'cat':

        return np.array([1,0,0,0,0])

    elif word_label == 'Dog':

        return np.array([0,1,0,0,0])

    elif word_label == 'Automobile':

        return np.array([0,0,1,0,0])

    elif word_label == 'Airplane':

        return np.array([0,0,0,1,0])

    elif word_label == 'Ship':

        return np.array([0,0,0,0,1])





def create_train_data():

    training_data = 

    for img in tqdm(os.listdir(TRAIN_DIR)):

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

        img_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)

        img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))

        training_data.append([np.array(img_data), create_label(img)])

    shuffle(training_data)

    np.save('train_data.npy', training_data)

    return training_data





def create_test_data():

    testing_data = 

    for img in tqdm(os.listdir(TEST_DIR)):

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

        img_num = img.split('.')[0:2]

        img_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)

        img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))

        testing_data.append([np.array(img_data), img_num])



    shuffle(testing_data)

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

    return testing_data





# If dataset is not created:

train_data = create_train_data()

test_data = create_test_data()

# If you have already created the dataset:

# train_data = np.load('train_data.npy')

# test_data = np.load('test_data.npy')

train = train_data[:25000]

test = train_data[:5000]

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

y_train = [i[1] for i in train]

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

y_test = [i[1] for i in test]



# Building The Model



tf.reset_default_graph()

convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')

convnet = conv_2d(convnet, 32, 5, activation='relu')

convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')

convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 128, 5, activation='relu')

convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')

convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 32, 5, activation='relu')

convnet = max_pool_2d(convnet, 5)

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

convnet = dropout(convnet, 0.8)

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', tensorboard_verbose=0)

history = model.fit({'input': X_train}, {'targets': y_train}, n_epoch=25,

          validation_set=({'input': X_test}, {'targets': y_test}),

          snapshot_step=500, show_metric=True, run_id=MODEL_NAME)



#print(history.history.keys())

#plt.figure(1)



# summarize history for accuracy



"""plt.subplot(211)

plt.plot(history.history['acc'])

plt.plot(history.history['val_acc'])

plt.title('model accuracy')

plt.ylabel('accuracy')

plt.xlabel('epoch')

plt.legend(['train', 'test1'], loc='upper left')

plt.show()"""



fig = plt.figure(figsize=(25, 12))



for num, data in enumerate(test_data[:25]):



    img_num = data[1]

    img_data = data[0]



    y = fig.add_subplot(5, 5, num + 1)

    orig = img_data

    data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)

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



    if np.argmax(model_out) == 1:

        str_label = 'Dog'

    if np.argmax(model_out) == 2:

        str_label = 'Automobile'

    if np.argmax(model_out) == 3:

        str_label = 'Airplane'

    if np.argmax(model_out) == 4:

        str_label = 'Ship'

    else:

        str_label = 'Cat'



    y.imshow(orig, cmap='gray')

    plt.title(str_label)

    y.axes.get_xaxis().set_visible(False)

    y.axes.get_yaxis().set_visible(False)

plt.show()

i'm getting following error:
ValueError: Cannot feed value of shape (64,) for Tensor 'targets/Y:0', which has shape '(?, 2)'

Can someone help me with this?
thanks in advance.

The problem is with the shape of your labels, so check the shape for y_train and y_test . Both of them should have to have a shape similar to the out put of your model, which is shown at the error ...which has shape '(?, 2)' and defined at the line where you create the last fully connected layer convnet = fully_connected(convnet, 2, activation='softmax')
.