forked from Philip-Bachman/ICML-2015
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TempTests.py
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/
TempTests.py
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import time
import numpy as np
import numpy.random as npr
import theano
import theano.tensor as T
import utils as utils
from load_data import load_udm, load_udm_ss, load_mnist, load_svhn, load_tfd
from InfNet import InfNet, load_infnet_from_file
from OneStageModel import OneStageModel
from NetLayers import relu_actfun, softplus_actfun, \
safe_softmax, tanh_actfun, row_shuffle
from VideoUtils import VideoSink
LOGVAR_BOUND = 6.0
####################
# HELPER FUNCTIONS #
####################
def nan_debug_print(x, str='damn the nan'):
"""
Work around theano's debugging deficiencies.
"""
if np.any(np.isnan(x)) or np.any(np.isinf(x)):
print(str)
return x
def binarize_data(X):
"""
Make a sample of bernoulli variables with probabilities given by X.
"""
X_shape = X.shape
probs = npr.rand(*X_shape)
X_binary = 1.0 * (probs < X)
return X_binary.astype(theano.config.floatX)
def mnist_prob_embed(X, Y):
"""
Embed the predicted class probabilities in Y in the digits images in X.
"""
obs_count = X.shape[0]
class_count = Y.shape[1]
Xy = np.zeros(X.shape)
for i in range(obs_count):
x_sq = X[i,:].reshape((28,28))
for j in range(class_count):
x_sq[((2*j)+1):((2*j+2)+1),0:3] = Y[i,j]
x_sq[((2*j)+1):((2*j+2)+1),3] = 0.2
x_sq[((2*j)+1):((2*j+2)+1),0] = 0.2
x_sq[0,0:4] = 0.2
x_sq[((2*class_count)+1),0:4] = 0.2
Xy[i,:] = x_sq.flatten()
return Xy
def rand_sample(param_list):
"""
Sample a value uniformly at random from the given (python) list.
"""
new_list = [val for val in param_list]
npr.shuffle(new_list)
rand_val = new_list[0]
return rand_val
def one_hot_np(Yc, cat_dim=None):
"""
Given a numpy integer column vector Yc, generate a matrix Yoh in which
Yoh[i,:] is a one-hot vector -- Yoh[i,Yc[i]] = 1.0 and other Yoh[i,j] = 0
"""
if cat_dim is None:
cat_dim = np.max(Yc) + 1
Yoh = np.zeros((Yc.size, cat_dim))
Yoh[np.arange(Yc.size),Yc.flatten()] = 1.0
return Yoh
def zmuv(X, axis=1):
X = X - np.mean(X, axis=axis, keepdims=True)
X = X / np.std(X, axis=axis, keepdims=True)
return X
def to_video(X, shape, v_file, frame_rate=30):
"""
Convert grayscale image sequence to video.
"""
# check that this is a floaty grayscale image array
assert((np.min(X) >= 0.0) and (np.max(X) <= 1.0))
# convert 0...1 float grayscale to 0...255 uint8 grayscale
X = 255.0 * X
X = X.astype(np.uint8)
# open a video encoding stream to receive the images
vsnk = VideoSink(v_file, size=shape, rate=frame_rate, colorspace='y8')
for i in range(X.shape[0]):
# reshape this frame, and push it to the video encoding stream
frame = X[i].reshape(shape)
vsnk(frame)
vsnk.close()
return
def group_chains(chain_list):
chain_len = len(chain_list)
chain_count = chain_list[0].shape[0]
obs_dim = chain_list[0].shape[1]
Xs = np.zeros((chain_len*chain_count, obs_dim))
idx = 0
for i in range(chain_count):
for j in range(chain_len):
Xs[idx] = chain_list[j][i]
idx = idx + 1
return Xs
def block_video(seq_matrix, im_dim, block_dim):
b_rows = block_dim[0]
b_cols = block_dim[1]
i_rows = im_dim[0]
i_cols = im_dim[1]
seq_len = seq_matrix[0][0].shape[0]
gap_px = 4
block_im_dim = (b_rows*(i_rows+gap_px), b_cols*(i_cols+gap_px))
# make the square-form multi-block video
full_vid_sq = np.zeros((seq_len, block_im_dim[0], block_im_dim[1]))
for i in range(seq_len):
for br in range(b_rows):
for bc in range(b_cols):
r_start = br * (i_rows + gap_px)
r_end = r_start + i_rows
c_start = bc * (i_cols + gap_px)
c_end = c_start + i_cols
full_vid_sq[i, r_start:r_end, c_start:c_end] = \
seq_matrix[br][bc][i,:].reshape((i_rows, i_cols))
full_vid_flat = np.zeros((seq_len, block_im_dim[0]*block_im_dim[1]))
for i in range(seq_len):
full_vid_flat[i,:] = full_vid_sq[i,:,:].ravel()
return [full_vid_flat, block_im_dim]
#############################################
# TESTING FOR PARZEN LOG_DENSITY ESTIMATION #
#############################################
def test_gip_sigma_scale_mnist():
from LogPDFs import cross_validate_sigma
# Simple test code, to check that everything is basically functional.
print("TESTING...")
# Initialize a source of randomness
rng = np.random.RandomState(12345)
# Load some data to train/validate/test with
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, zero_mean=False)
Xtr = datasets[0][0]
Xtr = Xtr.get_value(borrow=False)
Xva = datasets[2][0]
Xva = Xva.get_value(borrow=False)
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape),str(Xva.shape)))
# get and set some basic dataset information
tr_samples = Xtr.shape[0]
batch_size = 100
Xtr_mean = np.mean(Xtr, axis=0, keepdims=True)
Xtr_mean = (0.0 * Xtr_mean) + np.mean(Xtr)
Xc_mean = np.repeat(Xtr_mean, batch_size, axis=0).astype(theano.config.floatX)
# Symbolic inputs
Xd = T.matrix(name='Xd')
Xc = T.matrix(name='Xc')
Xm = T.matrix(name='Xm')
Xt = T.matrix(name='Xt')
# Load inferencer and generator from saved parameters
gn_fname = "MNIST_WALKOUT_TEST_MED_KLD/pt_osm_params_b80000_GN.pkl"
in_fname = "MNIST_WALKOUT_TEST_MED_KLD/pt_osm_params_b80000_IN.pkl"
IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd)
GN = load_infnet_from_file(f_name=gn_fname, rng=rng, Xd=Xd)
x_dim = IN.shared_layers[0].in_dim
z_dim = IN.mu_layers[-1].out_dim
# construct a GIPair with the loaded InfNet and GenNet
osm_params = {}
osm_params['x_type'] = 'gaussian'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=x_dim, z_dim=z_dim, params=osm_params)
# compute variational likelihood bound and its sub-components
Xva = row_shuffle(Xva)
Xb = Xva[0:5000]
file_name = "AX_MNIST_MAX_KLD_POST_KLDS.png"
post_klds = OSM.compute_post_klds(Xb)
post_dim_klds = np.mean(post_klds, axis=0)
utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
file_name)
# compute information about free-energy on validation set
file_name = "AX_MNIST_MAX_KLD_FREE_ENERGY.png"
fe_terms = OSM.compute_fe_terms(Xb, 20)
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# bound_results = OSM.compute_ll_bound(Xva)
# ll_bounds = bound_results[0]
# post_klds = bound_results[1]
# log_likelihoods = bound_results[2]
# max_lls = bound_results[3]
# print("mean ll bound: {0:.4f}".format(np.mean(ll_bounds)))
# print("mean posterior KLd: {0:.4f}".format(np.mean(post_klds)))
# print("mean log-likelihood: {0:.4f}".format(np.mean(log_likelihoods)))
# print("mean max log-likelihood: {0:.4f}".format(np.mean(max_lls)))
# print("min ll bound: {0:.4f}".format(np.min(ll_bounds)))
# print("max posterior KLd: {0:.4f}".format(np.max(post_klds)))
# print("min log-likelihood: {0:.4f}".format(np.min(log_likelihoods)))
# print("min max log-likelihood: {0:.4f}".format(np.min(max_lls)))
# # compute some information about the approximate posteriors
# post_stats = OSM.compute_post_stats(Xva, 0.0*Xva, 0.0*Xva)
# all_post_klds = np.sort(post_stats[0].ravel()) # post KLds for each obs and dim
# obs_post_klds = np.sort(post_stats[1]) # summed post KLds for each obs
# post_dim_klds = post_stats[2] # average post KLds for each post dim
# post_dim_vars = post_stats[3] # average squared mean for each post dim
# utils.plot_line(np.arange(all_post_klds.shape[0]), all_post_klds, "AAA_ALL_POST_KLDS.png")
# utils.plot_line(np.arange(obs_post_klds.shape[0]), obs_post_klds, "AAA_OBS_POST_KLDS.png")
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, "AAA_POST_DIM_KLDS.png")
# utils.plot_stem(np.arange(post_dim_vars.shape[0]), post_dim_vars, "AAA_POST_DIM_VARS.png")
# draw many samples from the GIP
for i in range(5):
tr_idx = npr.randint(low=0,high=tr_samples,size=(100,))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xs = []
for row in range(3):
Xs.append([])
for col in range(3):
sample_lists = OSM.sample_from_chain(Xd_batch[0:10,:], loop_iters=100, \
sigma_scale=1.0)
Xs[row].append(group_chains(sample_lists['data samples']))
Xs, block_im_dim = block_video(Xs, (28,28), (3,3))
to_video(Xs, block_im_dim, "AX_MNIST_MAX_KLD_CHAIN_VIDEO_{0:d}.avi".format(i), frame_rate=10)
file_name = "AX_MNIST_MAX_KLD_PRIOR_SAMPLE.png"
Xs = OSM.sample_from_prior(20*20)
utils.visualize_samples(Xs, file_name, num_rows=20)
# # test Parzen density estimator built from prior samples
# Xs = OSM.sample_from_prior(10000)
# [best_sigma, best_ll, best_lls] = \
# cross_validate_sigma(Xs, Xva, [0.12, 0.14, 0.15, 0.16, 0.18], 20)
# sort_idx = np.argsort(best_lls)
# sort_idx = sort_idx[0:400]
# utils.plot_line(np.arange(sort_idx.shape[0]), best_lls[sort_idx], "A_MNIST_MAX_KLD_BEST_LLS_1.png")
# utils.visualize_samples(Xva[sort_idx], "A_MNIST_MAX_KLD_BAD_DIGITS_1.png", num_rows=20)
return
def test_gip_sigma_scale_tfd():
from LogPDFs import cross_validate_sigma
# Simple test code, to check that everything is basically functional.
print("TESTING...")
# Initialize a source of randomness
rng = np.random.RandomState(12345)
# Load some data to train/validate/test with
data_file = 'data/tfd_data_48x48.pkl'
dataset = load_tfd(tfd_pkl_name=data_file, which_set='unlabeled', fold='all')
Xtr_unlabeled = dataset[0]
dataset = load_tfd(tfd_pkl_name=data_file, which_set='train', fold='all')
Xtr_train = dataset[0]
Xtr = np.vstack([Xtr_unlabeled, Xtr_train])
dataset = load_tfd(tfd_pkl_name=data_file, which_set='test', fold='all')
Xva = dataset[0]
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape),str(Xva.shape)))
# get and set some basic dataset information
tr_samples = Xtr.shape[0]
data_dim = Xtr.shape[1]
batch_size = 100
# Symbolic inputs
Xd = T.matrix(name='Xd')
Xc = T.matrix(name='Xc')
Xm = T.matrix(name='Xm')
Xt = T.matrix(name='Xt')
# Load inferencer and generator from saved parameters
gn_fname = "TFD_WALKOUT_TEST_KLD/pt_walk_params_b25000_GN.pkl"
in_fname = "TFD_WALKOUT_TEST_KLD/pt_walk_params_b25000_IN.pkl"
IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd)
GN = load_infnet_from_file(f_name=gn_fname, rng=rng, Xd=Xd)
x_dim = IN.shared_layers[0].in_dim
z_dim = IN.mu_layers[-1].out_dim
# construct a GIPair with the loaded InfNet and GenNet
osm_params = {}
osm_params['x_type'] = 'gaussian'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=x_dim, z_dim=z_dim, params=osm_params)
# # compute variational likelihood bound and its sub-components
Xva = row_shuffle(Xva)
Xb = Xva[0:5000]
# file_name = "A_TFD_POST_KLDS.png"
# post_klds = OSM.compute_post_klds(Xb)
# post_dim_klds = np.mean(post_klds, axis=0)
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
# file_name)
# compute information about free-energy on validation set
file_name = "A_TFD_KLD_FREE_ENERGY.png"
fe_terms = OSM.compute_fe_terms(Xb, 20)
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# bound_results = OSM.compute_ll_bound(Xva)
# ll_bounds = bound_results[0]
# post_klds = bound_results[1]
# log_likelihoods = bound_results[2]
# max_lls = bound_results[3]
# print("mean ll bound: {0:.4f}".format(np.mean(ll_bounds)))
# print("mean posterior KLd: {0:.4f}".format(np.mean(post_klds)))
# print("mean log-likelihood: {0:.4f}".format(np.mean(log_likelihoods)))
# print("mean max log-likelihood: {0:.4f}".format(np.mean(max_lls)))
# print("min ll bound: {0:.4f}".format(np.min(ll_bounds)))
# print("max posterior KLd: {0:.4f}".format(np.max(post_klds)))
# print("min log-likelihood: {0:.4f}".format(np.min(log_likelihoods)))
# print("min max log-likelihood: {0:.4f}".format(np.min(max_lls)))
# # compute some information about the approximate posteriors
# post_stats = OSM.compute_post_stats(Xva, 0.0*Xva, 0.0*Xva)
# all_post_klds = np.sort(post_stats[0].ravel()) # post KLds for each obs and dim
# obs_post_klds = np.sort(post_stats[1]) # summed post KLds for each obs
# post_dim_klds = post_stats[2] # average post KLds for each post dim
# post_dim_vars = post_stats[3] # average squared mean for each post dim
# utils.plot_line(np.arange(all_post_klds.shape[0]), all_post_klds, "AAA_ALL_POST_KLDS.png")
# utils.plot_line(np.arange(obs_post_klds.shape[0]), obs_post_klds, "AAA_OBS_POST_KLDS.png")
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, "AAA_POST_DIM_KLDS.png")
# utils.plot_stem(np.arange(post_dim_vars.shape[0]), post_dim_vars, "AAA_POST_DIM_VARS.png")
# draw many samples from the GIP
for i in range(5):
tr_idx = npr.randint(low=0,high=tr_samples,size=(100,))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xs = []
for row in range(3):
Xs.append([])
for col in range(3):
sample_lists = OSM.sample_from_chain(Xd_batch[0:10,:], loop_iters=100, \
sigma_scale=1.0)
Xs[row].append(group_chains(sample_lists['data samples']))
Xs, block_im_dim = block_video(Xs, (48,48), (3,3))
to_video(Xs, block_im_dim, "A_TFD_KLD_CHAIN_VIDEO_{0:d}.avi".format(i), frame_rate=10)
#sample_lists = GIP.sample_from_chain(Xd_batch[0,:].reshape((1,data_dim)), loop_iters=300, \
# sigma_scale=1.0)
#Xs = np.vstack(sample_lists["data samples"])
#file_name = "TFD_TEST_{0:d}.png".format(i)
#utils.visualize_samples(Xs, file_name, num_rows=15)
file_name = "A_TFD_KLD_PRIOR_SAMPLE.png"
Xs = OSM.sample_from_prior(20*20)
utils.visualize_samples(Xs, file_name, num_rows=20)
# test Parzen density estimator built from prior samples
# Xs = OSM.sample_from_prior(10000)
# [best_sigma, best_ll, best_lls] = \
# cross_validate_sigma(Xs, Xva, [0.09, 0.095, 0.1, 0.105, 0.11], 10)
# sort_idx = np.argsort(best_lls)
# sort_idx = sort_idx[0:400]
# utils.plot_line(np.arange(sort_idx.shape[0]), best_lls[sort_idx], "A_TFD_BEST_LLS_1.png")
# utils.visualize_samples(Xva[sort_idx], "A_TFD_BAD_FACES_1.png", num_rows=20)
return
###################
# TEST DISPATCHER #
###################
if __name__=="__main__":
test_gip_sigma_scale_mnist()
#test_gip_sigma_scale_tfd()