Coverage for hypergan/samplers/random_walk_sampler.py : 19%

from hypergan.samplers.base_sampler import BaseSampler 

import tensorflow as tf 

import numpy as np 

class RandomWalkSampler(BaseSampler): 

def __init__(self, gan, samples_per_row=8): 

BaseSampler.__init__(self, gan, samples_per_row) 

self.z = None 

self.y = None 

self.x = None 

self.step = 0 

self.steps = 30 

self.target = None 

def _sample(self): 

gan = self.gan 

z_t = gan.encoder.sample 

inputs_t = gan.inputs.x 

if self.z is None: 

self.z = gan.encoder.sample.eval() 

self.target = gan.encoder.sample.eval() 

self.input = gan.session.run(gan.inputs.x) 

if self.step > self.steps: 

self.z = self.target 

self.target = gan.encoder.sample.eval() 

self.step = 0 

percent = float(self.step)/self.steps 

z_interp = self.z*(1.0-percent) + self.target*percent 

self.step+=1 

g=tf.get_default_graph() 

with g.as_default(): 

tf.set_random_seed(1) 

return { 

'generator': gan.session.run(gan.generator.sample, feed_dict={z_t: z_interp, inputs_t: self.input}) 

}