mnist.train.images is essentially a numpy array of shape [55000, 784]. Where, 55000 is the number of images and 784 is the number of pixels in each image (each image is 28x28)
You need to create a similar numpy array from your data in case you want to run this exact code. So, you'll need to iterate over all your images, read image as a numpy array, flatten it and create a matrix of size [num_examples, image_size]
The following code snippet should do it:
import os
import cv2
import numpy as np
def load_data(img_dir):
return np.array([cv2.imread(os.path.join(img_dir, img)).flatten() for img in os.listdir(img_dir) if img.endswith(".jpg")])
A more comprehensive code to enable debugging:
import os
list_of_imgs = []
img_dir = "../input/train/"
for img in os.listdir("."):
img = os.path.join(img_dir, img)
if not img.endswith(".jpg"):
continue
a = cv2.imread(img)
if a is None:
print "Unable to read image", img
continue
list_of_imgs.append(a.flatten())
train_data = np.array(list_of_imgs)
Note:
If your images are not 28x28x1 (B/W images), you will need to change the neural network architecture (defined in cnn_model_fn). The architecture in the tutorial is a toy architecture which only works for simple images like MNIST. Alexnet may be a good place to start for RGB images.