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mnist_utils.py
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mnist_utils.py
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import matplotlib
matplotlib.use("Agg")
import math
import matplotlib.pyplot as plt
import numpy as np
from mnist import MNIST
# TODO: clean up
img_dim = 28
n_chan = 1
max_pixel = 1.
data_mean = 0
data_std = 1.
def show(img, normalize, i, title="", num_rows=-3, path=None):
img = img.copy().reshape(img_dim, img_dim)
if num_rows < 0:
plt.subplot(2, -num_rows, i)
else:
plt.subplot(num_rows, 2, i)
plt.imshow(img, vmin=0, vmax=1, cmap="gray")
plt.title(title)
plt.axis("off")
def reconstruct(vae, batch, path=None, normalize=True):
plt.figure(figsize=(8, 30))
num_imgs = batch.shape[0]
batch_rec = vae.batch_reconstruct(batch)
for i in range(num_imgs):
show(batch[i], normalize, 2 * i + 1, 'original', num_imgs)
show(batch_rec[i], normalize, 2 * i + 2, 'reconstr', num_imgs)
if path is None:
plt.show()
else:
plt.savefig(path)
plt.close()
def load_data(normalize=True):
# LOAD DATA
mndata = MNIST("./data/mnist")
mndata.gz = True
train_x, train_y = mndata.load_training()
test_x, test_y = mndata.load_testing()
train_x = np.array(train_x, dtype=np.float32).reshape(
len(train_x), 28, 28, 1) / 255.0
train_y = np.array(train_y)
test_x = np.array(test_x, dtype=np.float32).reshape(
len(test_x), 28, 28, 1) / 255.0
test_y = np.array(test_y)
idx = np.random.permutation(train_x.shape[0])
train_x = train_x[idx]
train_y = train_y[idx]
val_idx = math.ceil(train_x.shape[0] * 0.1)
val_x = train_x[:val_idx]
val_y = train_y[:val_idx]
train_x = train_x[val_idx:]
train_y = train_y[val_idx:]
return train_x, train_y, val_x, val_y, test_x, test_y
def input(img, batch_size):
return np.tile(img, (batch_size, 1, 1, 1)).reshape(batch_size,
img_dim, img_dim, 1)
def dist(imgs, img, batch_size):
if imgs.shape == (batch_size, img_dim, img_dim, 1):
imgs = imgs.reshape(batch_size, 784)
assert imgs.shape == (batch_size, 784)
img = img.reshape((784,))
imgs_pixels = imgs * 255.0
img_pixels = img * 255.0
diff = np.linalg.norm(imgs_pixels - img_pixels, axis=1)
return np.mean(diff), np.std(diff)