fixed variational autoencoder visualization for Gaussian latent space (#4423)
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@ -4,6 +4,7 @@ Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
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'''
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import norm
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from keras.layers import Input, Dense, Lambda
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from keras.models import Model
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@ -82,9 +83,10 @@ generator = Model(decoder_input, _x_decoded_mean)
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n = 15 # figure with 15x15 digits
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digit_size = 28
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figure = np.zeros((digit_size * n, digit_size * n))
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# we will sample n points within [-15, 15] standard deviations
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grid_x = np.linspace(-15, 15, n)
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grid_y = np.linspace(-15, 15, n)
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# linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian
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# to produce values of the latent variables z, since the prior of the latent space is Gaussian
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grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
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grid_y = norm.ppf(np.linspace(0.05, 0.95, n))
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for i, yi in enumerate(grid_x):
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for j, xi in enumerate(grid_y):
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@ -95,5 +97,5 @@ for i, yi in enumerate(grid_x):
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j * digit_size: (j + 1) * digit_size] = digit
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plt.figure(figsize=(10, 10))
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plt.imshow(figure)
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plt.imshow(figure, cmap='Greys_r')
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plt.show()
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@ -5,6 +5,7 @@ Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
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'''
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import norm
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from keras.layers import Input, Dense, Lambda, Flatten, Reshape
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from keras.layers import Convolution2D, Deconvolution2D
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@ -153,9 +154,10 @@ generator = Model(decoder_input, _x_decoded_mean_squash)
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n = 15 # figure with 15x15 digits
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digit_size = 28
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figure = np.zeros((digit_size * n, digit_size * n))
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# we will sample n points within [-15, 15] standard deviations
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grid_x = np.linspace(-15, 15, n)
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grid_y = np.linspace(-15, 15, n)
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# linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian
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# to produce values of the latent variables z, since the prior of the latent space is Gaussian
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grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
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grid_y = norm.ppf(np.linspace(0.05, 0.95, n))
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for i, yi in enumerate(grid_x):
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for j, xi in enumerate(grid_y):
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@ -167,5 +169,5 @@ for i, yi in enumerate(grid_x):
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j * digit_size: (j + 1) * digit_size] = digit
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plt.figure(figsize=(10, 10))
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plt.imshow(figure)
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plt.imshow(figure, cmap='Greys_r')
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plt.show()
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