吴裕雄--天生自然深度学习TensorBoard可视化：projector_data_prepare

import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data

%matplotlib inline

LOG_DIR = ‘F:\\temp\\log\\‘
SPRITE_FILE = ‘mnist_sprite.jpg‘
META_FIEL = "mnist_meta.tsv"

def create_sprite_image(images):
    """Returns a sprite image consisting of images passed as argument. Images should be count x width x height"""
    if isinstance(images, list):
        images = np.array(images)
    img_h = images.shape[1]
    img_w = images.shape[2]
    n_plots = int(np.ceil(np.sqrt(images.shape[0])))
    
    spriteimage = np.ones((img_h * n_plots ,img_w * n_plots ))
    
    for i in range(n_plots):
        for j in range(n_plots):
            this_filter = i * n_plots + j
            if(this_filter < images.shape[0]):
                this_img = images[this_filter]
                spriteimage[i * img_h:(i + 1) * img_h,j * img_w:(j + 1) * img_w] = this_img
    return spriteimage
    
mnist = input_data.read_data_sets("F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data", one_hot=False)

to_visualise = 1 - np.reshape(mnist.test.images,(-1,28,28))
sprite_image = create_sprite_image(to_visualise)

path_for_mnist_sprites = os.path.join(LOG_DIR, SPRITE_FILE)
plt.imsave(path_for_mnist_sprites,sprite_image,cmap=‘gray‘)
plt.imshow(sprite_image,cmap=‘gray‘)