Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
# Code for testing the variational Multi-Stage Generative Model. #
##################################################################
from __future__ import print_function, division
# basic python
import cPickle as pickle
from PIL import Image
import numpy as np
import numpy.random as npr
from collections import OrderedDict
import time
# theano business
import theano
import theano.tensor as T
# blocks stuff
from blocks.initialization import Constant, IsotropicGaussian, Orthogonal
from blocks.filter import VariableFilter
from blocks.graph import ComputationGraph
from blocks.roles import PARAMETER
from blocks.model import Model
from blocks.bricks import Tanh, Identity, Rectifier, MLP
from blocks.bricks.cost import BinaryCrossEntropy
from blocks.bricks.recurrent import SimpleRecurrent, LSTM
# phil's sweetness
import utils
from BlocksModels import *
from RAMBlocks import *
from SeqCondGenVariants import *
from DKCode import get_adam_updates, get_adadelta_updates
from load_data import load_udm, load_tfd, load_svhn_gray, load_binarized_mnist
from HelperFuncs import sample_data_masks, shift_and_scale_into_01, \
row_shuffle, to_fX, one_hot_np
from MotionRenderers import TrajectoryGenerator, ObjectPainter
RESULT_PATH = "RAM_TEST_RESULTS/"
###########################################
###########################################
## ##
## Test attention-based image "copying". ##
## ##
###########################################
###########################################
def test_seq_cond_gen_copy(step_type='add', res_tag="AAA"):
##############################
# File tag, for output stuff #
##############################
result_tag = "{}TEST_{}".format(RESULT_PATH, res_tag)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, as_shared=False, zero_mean=False)
Xtr = datasets[0][0]
Xva = datasets[1][0]
Xte = datasets[2][0]
# merge validation set and training set, and test on test set.
#Xtr = np.concatenate((Xtr, Xva), axis=0)
#Xva = Xte
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
# basic params
batch_size = 128
traj_len = 20
im_dim = 28
obs_dim = im_dim*im_dim
def sample_batch(np_ary, bs=100):
row_count = np_ary.shape[0]
samp_idx = npr.randint(low=0,high=row_count,size=(bs,))
xb = np_ary.take(samp_idx, axis=0)
return xb
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
total_steps = traj_len
init_steps = 5
exit_rate = 0.1
nll_weight = 0.0
x_dim = obs_dim
y_dim = obs_dim
z_dim = 128
att_spec_dim = 5
rnn_dim = 512
mlp_dim = 512
def visualize_attention(result, pre_tag="AAA", post_tag="AAA"):
seq_len = result[0].shape[0]
samp_count = result[0].shape[1]
# get generated predictions