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fixed variational autoencoder visualization for Gaussian latent space (#4423)

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Dontloo 2016-11-24 06:08:19 +08:00 committed by François Chollet
parent 773d4ce8cb
commit 88f3b3f75e
2 changed files with 12 additions and 8 deletions
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10
examples/variational_autoencoder.py
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@ -4,6 +4,7 @@ Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
'''
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda
from keras.models import Model
@ -82,9 +83,10 @@ generator = Model(decoder_input, _x_decoded_mean)
n = 15 # figure with 15x15 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# we will sample n points within [-15, 15] standard deviations
grid_x = np.linspace(-15, 15, n)
grid_y = np.linspace(-15, 15, n)
# linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian
# to produce values of the latent variables z, since the prior of the latent space is Gaussian
grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
grid_y = norm.ppf(np.linspace(0.05, 0.95, n))
for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
@ -95,5 +97,5 @@ for i, yi in enumerate(grid_x):
j * digit_size: (j + 1) * digit_size] = digit
plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.imshow(figure, cmap='Greys_r')
plt.show()

10
examples/variational_autoencoder_deconv.py
View File

@ -5,6 +5,7 @@ Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
'''
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda, Flatten, Reshape
from keras.layers import Convolution2D, Deconvolution2D
@ -153,9 +154,10 @@ generator = Model(decoder_input, _x_decoded_mean_squash)
n = 15 # figure with 15x15 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# we will sample n points within [-15, 15] standard deviations
grid_x = np.linspace(-15, 15, n)
grid_y = np.linspace(-15, 15, n)
# linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian
# to produce values of the latent variables z, since the prior of the latent space is Gaussian
grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
grid_y = norm.ppf(np.linspace(0.05, 0.95, n))
for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
@ -167,5 +169,5 @@ for i, yi in enumerate(grid_x):
j * digit_size: (j + 1) * digit_size] = digit
plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.imshow(figure, cmap='Greys_r')
plt.show()