import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import tensorflow as tf
LEARNING_RATE = 1e-4
TRAINING_ITERATIONS = 2500        
DROPOUT = 0.5
BATCH_SIZE = 50
VALIDATION_SIZE = 2000
IMAGE_TO_DISPLAY = 10
data = pd.read_csv('../input/train.csv')
print('data({0[0]},{0[1]})'.format(data.shape))
print (data.head())
images = data.iloc[:,1:].values
images = images.astype(np.float)
images = np.multiply(images, 1.0 / 255.0)
print('images({0[0]},{0[1]})'.format(images.shape))

image_size = images.shape[1]
print ('image_size => {0}'.format(image_size))

# in this case all images are square
image_width = image_height = np.ceil(np.sqrt(image_size)).astype(np.uint8)

print ('image_width => {0}\nimage_height => {1}'.format(image_width,image_height))

# display image
def display(img):
    
    # (784) => (28,28)
    one_image = img.reshape(image_width,image_height)
    
    plt.axis('off')
    plt.imshow(one_image, cmap=cm.binary)

# output image     
display(images[IMAGE_TO_DISPLAY])