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utils.py
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utils.py
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from scipy import misc
import tensorflow as tf
import numpy as np
def sample_prob(probs, rand):
""" Takes a tensor of probabilities (as from a sigmoidal activation)
and samples from all the distributions
:param probs: tensor of probabilities
:param rand: tensor (of the same shape as probs) of random values
:return : binary sample of probabilities
"""
return tf.nn.relu(tf.sign(probs - rand))
def gen_batches(data, batch_size):
""" Divide input data into batches.
:param data: input data
:param batch_size: size of each batch
:return: data divided into batches
"""
data = np.array(data)
for i in range(0, data.shape[0], batch_size):
yield data[i:i+batch_size]
def gen_image(img, width, height, outfile, img_type='grey'):
assert len(img) == width * height or len(img) == width * height * 3
if img_type == 'grey':
misc.imsave(outfile, img.reshape(width, height))
elif img_type == 'color':
misc.imsave(outfile, img.reshape(3, width, height))