Myujth

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')
.