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Commit ae0a9409 authored by cclauss's avatar cclauss
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Fix Python 3 Syntax Errors (en masse)

parent eb7c6e43
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Showing with 187 additions and 167 deletions
research/cognitive_mapping_and_planning/scripts/script_env_vis.py
View file @ ae0a9409
......@@ -90,7 +90,7 @@ def walk_through(b):
root = tk.Tk()
image = b.render_nodes(b.task.nodes[[current_node],:])[0]
print image.shape
print(image.shape)
image = image.astype(np.uint8)
im = Image.fromarray(image)
im = ImageTk.PhotoImage(im)
......
research/cognitive_mapping_and_planning/scripts/script_plot_trajectory.py
View file @ ae0a9409
......@@ -265,7 +265,7 @@ def plot_trajectory_first_person(dt, orig_maps, out_dir):
tmp_file_name = 'tmp.mp4'
line_ani.save(tmp_file_name, writer=writer, savefig_kwargs={'facecolor':'black'})
out_file_name = os.path.join(out_dir, 'vis_{:04d}.mp4'.format(i))
print out_file_name
print(out_file_name)
if fu.exists(out_file_name):
gfile.Remove(out_file_name)
......@@ -318,7 +318,7 @@ def plot_trajectory(dt, hardness, orig_maps, out_dir):
ax.set_ylim([xy1[1], xy2[1]])
file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i))
print file_name
print(file_name)
with fu.fopen(file_name, 'w') as f:
plt.savefig(f)
plt.close(fig)
......
research/cognitive_mapping_and_planning/src/utils.py
View file @ ae0a9409
......@@ -17,6 +17,7 @@ r"""Generaly Utilities.
"""
import numpy as np, cPickle, os, time
from six.moves import xrange
import src.file_utils as fu
import logging
......@@ -93,12 +94,12 @@ def tic_toc_print(interval, string):
global tic_toc_print_time_old
if 'tic_toc_print_time_old' not in globals():
tic_toc_print_time_old = time.time()
print string
print(string)
else:
new_time = time.time()
if new_time - tic_toc_print_time_old > interval:
tic_toc_print_time_old = new_time;
print string
print(string)
def mkdir_if_missing(output_dir):
if not fu.exists(output_dir):
......@@ -165,4 +166,3 @@ def calc_pr(gt, out, wt=None):
ap = voc_ap(rec, prec)
return ap, rec, prec
research/compression/entropy_coder/core/entropy_coder_single.py
View file @ ae0a9409
......@@ -58,7 +58,7 @@ def main(_):
#iteration = FLAGS.iteration
if not tf.gfile.Exists(FLAGS.input_codes):
print '\nInput codes not found.\n'
print('\nInput codes not found.\n')
return
with tf.gfile.FastGFile(FLAGS.input_codes, 'rb') as code_file:
......
research/compression/entropy_coder/core/entropy_coder_train.py
View file @ ae0a9409
......@@ -171,7 +171,7 @@ def train():
'code_length': model.average_code_length
}
np_tensors = sess.run(tf_tensors, feed_dict=feed_dict)
print np_tensors['code_length']
print(np_tensors['code_length'])
sv.Stop()
......
research/differential_privacy/multiple_teachers/analysis.py
View file @ ae0a9409
......@@ -41,6 +41,7 @@ python analysis.py
import os
import math
import numpy as np
from six.moves import xrange
import tensorflow as tf
from differential_privacy.multiple_teachers.input import maybe_download
......@@ -139,7 +140,7 @@ def logmgf_exact(q, priv_eps, l):
try:
log_t = math.log(t)
except ValueError:
print "Got ValueError in math.log for values :" + str((q, priv_eps, l, t))
print("Got ValueError in math.log for values :" + str((q, priv_eps, l, t)))
log_t = priv_eps * l
else:
log_t = priv_eps * l
......@@ -171,7 +172,7 @@ def sens_at_k(counts, noise_eps, l, k):
"""
counts_sorted = sorted(counts, reverse=True)
if 0.5 * noise_eps * l > 1:
print "l too large to compute sensitivity"
print("l too large to compute sensitivity")
return 0
# Now we can assume that at k, gap remains positive
# or we have reached the point where logmgf_exact is
......@@ -268,8 +269,8 @@ def main(unused_argv):
# Solving gives eps = (alpha - ln (delta))/l
eps_list_nm = (total_log_mgf_nm - math.log(delta)) / l_list
print "Epsilons (Noisy Max): " + str(eps_list_nm)
print "Smoothed sensitivities (Noisy Max): " + str(total_ss_nm / l_list)
print("Epsilons (Noisy Max): " + str(eps_list_nm))
print("Smoothed sensitivities (Noisy Max): " + str(total_ss_nm / l_list))
# If beta < eps / 2 ln (1/delta), then adding noise Lap(1) * 2 SS/eps
# is eps,delta DP
......@@ -280,12 +281,12 @@ def main(unused_argv):
# Print the first one's scale
ss_eps = 2.0 * beta * math.log(1/delta)
ss_scale = 2.0 / ss_eps
print "To get an " + str(ss_eps) + "-DP estimate of epsilon, "
print "..add noise ~ " + str(ss_scale)
print "... times " + str(total_ss_nm / l_list)
print "Epsilon = " + str(min(eps_list_nm)) + "."
print("To get an " + str(ss_eps) + "-DP estimate of epsilon, ")
print("..add noise ~ " + str(ss_scale))
print("... times " + str(total_ss_nm / l_list))
print("Epsilon = " + str(min(eps_list_nm)) + ".")
if min(eps_list_nm) == eps_list_nm[-1]:
print "Warning: May not have used enough values of l"
print("Warning: May not have used enough values of l")
# Data independent bound, as mechanism is
# 2*noise_eps DP.
......@@ -294,7 +295,7 @@ def main(unused_argv):
[logmgf_exact(1.0, 2.0 * noise_eps, l) for l in l_list])
data_ind_eps_list = (data_ind_log_mgf - math.log(delta)) / l_list
print "Data independent bound = " + str(min(data_ind_eps_list)) + "."
print("Data independent bound = " + str(min(data_ind_eps_list)) + ".")
return
......
research/differential_privacy/privacy_accountant/python/gaussian_moments.py
View file @ ae0a9409
......@@ -40,12 +40,15 @@ To verify that the I1 >= I2 (see comments in GaussianMomentsAccountant in
accountant.py for the context), run the same loop above with verify=True
passed to compute_log_moment.
"""
from __future__ import print_function
import math
import sys
import numpy as np
import scipy.integrate as integrate
import scipy.stats
from six.moves import xrange
from sympy.mpmath import mp
......@@ -108,10 +111,10 @@ def compute_a(sigma, q, lmbd, verbose=False):
a_lambda_exact = ((1.0 - q) * a_lambda_first_term_exact +
q * a_lambda_second_term_exact)
if verbose:
print "A: by binomial expansion {} = {} + {}".format(
print("A: by binomial expansion {} = {} + {}".format(
a_lambda_exact,
(1.0 - q) * a_lambda_first_term_exact,
q * a_lambda_second_term_exact)
q * a_lambda_second_term_exact))
return _to_np_float64(a_lambda_exact)
......@@ -125,8 +128,8 @@ def compute_b(sigma, q, lmbd, verbose=False):
b_fn = lambda z: (np.power(mu0(z) / mu(z), lmbd) -
np.power(mu(-z) / mu0(z), lmbd))
if verbose:
print "M =", m
print "f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m))
print("M =", m)
print("f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m)))
assert b_fn(-m) < 0 and b_fn(m) < 0
b_lambda_int1_fn = lambda z: (mu0(z) *
......@@ -140,9 +143,9 @@ def compute_b(sigma, q, lmbd, verbose=False):
b_bound = a_lambda_m1 + b_int1 - b_int2
if verbose:
print "B: by numerical integration", b_lambda
print "B must be no more than ", b_bound
print b_lambda, b_bound
print("B: by numerical integration", b_lambda)
print("B must be no more than ", b_bound)
print(b_lambda, b_bound)
return _to_np_float64(b_lambda)
......@@ -188,10 +191,10 @@ def compute_a_mp(sigma, q, lmbd, verbose=False):
a_lambda_second_term = integral_inf_mp(a_lambda_second_term_fn)
if verbose:
print "A: by numerical integration {} = {} + {}".format(
print("A: by numerical integration {} = {} + {}".format(
a_lambda,
(1 - q) * a_lambda_first_term,
q * a_lambda_second_term)
q * a_lambda_second_term))
return _to_np_float64(a_lambda)
......@@ -210,8 +213,8 @@ def compute_b_mp(sigma, q, lmbd, verbose=False):
b_fn = lambda z: ((mu0(z) / mu(z)) ** lmbd_int -
(mu(-z) / mu0(z)) ** lmbd_int)
if verbose:
print "M =", m
print "f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m))
print("M =", m)
print("f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m)))
assert b_fn(-m) < 0 and b_fn(m) < 0
b_lambda_int1_fn = lambda z: mu0(z) * (mu0(z) / mu(z)) ** lmbd_int
......@@ -223,8 +226,8 @@ def compute_b_mp(sigma, q, lmbd, verbose=False):
b_bound = a_lambda_m1 + b_int1 - b_int2
if verbose:
print "B by numerical integration", b_lambda
print "B must be no more than ", b_bound
print("B by numerical integration", b_lambda)
print("B must be no more than ", b_bound)
assert b_lambda < b_bound + 1e-5
return _to_np_float64(b_lambda)
......
research/neural_gpu/neural_gpu.py
View file @ ae0a9409
......@@ -17,6 +17,7 @@
import time
import numpy as np
from six.moves import xrange
import tensorflow as tf
from tensorflow.python.framework import function
......@@ -500,8 +501,10 @@ class NeuralGPU(object):
return tf.reduce_sum(encoder_outputs * tf.expand_dims(mask, 2), 1)
with tf.variable_scope("decoder"):
def decoder_loop_fn((state, prev_cell_out, _), (cell_inp, cur_tgt)):
def decoder_loop_fn(state__prev_cell_out__unused, cell_inp__cur_tgt):
"""Decoder loop function."""
state, prev_cell_out, _ = state__prev_cell_out__unused
cell_inp, cur_tgt = cell_inp__cur_tgt
attn_q = tf.layers.dense(prev_cell_out, height * nmaps,
name="attn_query")
attn_res = attention_query(attn_q, tf.get_variable(
......
research/neural_gpu/neural_gpu_trainer.py
View file @ ae0a9409
......@@ -22,6 +22,7 @@ import threading
import time
import numpy as np
from six.moves import xrange
import tensorflow as tf
import program_utils
......@@ -144,7 +145,7 @@ def read_data(source_path, target_path, buckets, max_size=None, print_out=True):
while source and target and (not max_size or counter < max_size):
counter += 1
if counter % 100000 == 0 and print_out:
print " reading data line %d" % counter
print(" reading data line %d" % counter)
sys.stdout.flush()
source_ids = [int(x) for x in source.split()]
target_ids = [int(x) for x in target.split()]
......@@ -188,7 +189,7 @@ def read_data_into_global(source_path, target_path, buckets,
global_train_set["wmt"].append(data_set)
train_total_size = calculate_buckets_scale(data_set, buckets, "wmt")
if print_out:
print " Finished global data reading (%d)." % train_total_size
print(" Finished global data reading (%d)." % train_total_size)
def initialize(sess=None):
......@@ -552,7 +553,7 @@ def score_beams_prog(beams, target, inp, history, print_out=False,
for h in history]
tgt_set = set(target)
if print_out:
print "target: ", tgt_prog
print("target: ", tgt_prog)
inps, tgt_outs = [], []
for i in xrange(3):
ilist = [inp[i + 1, l] for l in xrange(inp.shape[1])]
......@@ -566,11 +567,11 @@ def score_beams_prog(beams, target, inp, history, print_out=False,
if len(olist) == 1:
tgt_outs.append(olist[0])
else:
print [program_utils.prog_vocab[x] for x in ilist if x > 0]
print olist
print tgt_prog
print program_utils.evaluate(tgt_prog, {"a": inps[-1]})
print "AAAAA"
print([program_utils.prog_vocab[x] for x in ilist if x > 0])
print(olist)
print(tgt_prog)
print(program_utils.evaluate(tgt_prog, {"a": inps[-1]}))
print("AAAAA")
tgt_outs.append(olist[0])
if not test_mode:
for _ in xrange(7):
......@@ -602,7 +603,7 @@ def score_beams_prog(beams, target, inp, history, print_out=False,
best_prog = b_prog
best_score = score
if print_out:
print "best score: ", best_score, " best prog: ", best_prog
print("best score: ", best_score, " best prog: ", best_prog)
return best, best_score
......@@ -719,7 +720,7 @@ def train():
inp = new_inp
# If all results are great, stop (todo: not to wait for all?).
if FLAGS.nprint > 1:
print scores
print(scores)
if sum(scores) / float(len(scores)) >= 10.0:
break
# The final step with the true target.
......@@ -735,7 +736,7 @@ def train():
errors, total, seq_err = data.accuracy(
inp, res, target, batch_size, 0, new_target, scores)
if FLAGS.nprint > 1:
print "seq_err: ", seq_err
print("seq_err: ", seq_err)
acc_total += total
acc_errors += errors
acc_seq_err += seq_err
......@@ -944,8 +945,8 @@ def interactive():
for v in tf.trainable_variables():
shape = v.get_shape().as_list()
total += mul(shape)
print (v.name, shape, mul(shape))
print total
print(v.name, shape, mul(shape))
print(total)
# Start interactive loop.
sys.stdout.write("Input to Neural GPU Translation Model.\n")
sys.stdout.write("> ")
......@@ -960,7 +961,7 @@ def interactive():
normalize_digits=FLAGS.normalize_digits)
else:
token_ids = wmt.sentence_to_token_ids(inpt, en_vocab)
print [rev_en_vocab[t] for t in token_ids]
print([rev_en_vocab[t] for t in token_ids])
# Which bucket does it belong to?
buckets = [b for b in xrange(len(data.bins))
if data.bins[b] >= max(len(token_ids), len(cures))]
......@@ -986,12 +987,12 @@ def interactive():
loss = loss[0] - (data.bins[bucket_id] * FLAGS.length_norm)
outputs = [int(np.argmax(logit, axis=1))
for logit in output_logits]
print [rev_fr_vocab[t] for t in outputs]
print loss, data.bins[bucket_id]
print linearize(outputs, rev_fr_vocab)
print([rev_fr_vocab[t] for t in outputs])
print(loss, data.bins[bucket_id])
print(linearize(outputs, rev_fr_vocab))
cures.append(outputs[gen_idx])
print cures
print linearize(cures, rev_fr_vocab)
print(cures)
print(linearize(cures, rev_fr_vocab))
if FLAGS.simple_tokenizer:
cur_out = outputs
if wmt.EOS_ID in cur_out:
......@@ -1002,11 +1003,11 @@ def interactive():
if loss < result_cost:
result = outputs
result_cost = loss
print ("FINAL", result_cost)
print [rev_fr_vocab[t] for t in result]
print linearize(result, rev_fr_vocab)
print("FINAL", result_cost)
print([rev_fr_vocab[t] for t in result])
print(linearize(result, rev_fr_vocab))
else:
print "TOOO_LONG"
print("TOOO_LONG")
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline(), ""
......
research/neural_gpu/program_utils.py
View file @ ae0a9409
......@@ -177,7 +177,7 @@ def evaluate(program_str, input_names_to_vals, default="ERROR"):
with stdoutIO() as s:
# pylint: disable=bare-except
try:
exec exec_str + " print(out)"
exec(exec_str + " print(out)")
return s.getvalue()[:-1]
except:
return default
......@@ -290,7 +290,7 @@ class Program(object):
with stdoutIO() as s:
# pylint: disable=exec-used
exec inp_str + self.body + "; print(out)"
exec(inp_str + self.body + "; print(out)")
# pylint: enable=exec-used
return s.getvalue()[:-1]
......@@ -412,11 +412,11 @@ def gen(max_len, how_many):
else:
outcomes_to_programs[outcome_str] = t.flat_str()
if counter % 5000 == 0:
print "== proggen: tried: " + str(counter)
print "== proggen: kept: " + str(len(outcomes_to_programs))
print("== proggen: tried: " + str(counter))
print("== proggen: kept: " + str(len(outcomes_to_programs)))
if counter % 250000 == 0 and save_prefix is not None:
print "saving..."
print("saving...")
save_counter = 0
progfilename = os.path.join(save_prefix, "prog_" + str(counter) + ".txt")
iofilename = os.path.join(save_prefix, "io_" + str(counter) + ".txt")
......@@ -431,7 +431,7 @@ def gen(max_len, how_many):
for (o, p) in outcomes_to_programs.iteritems():
save_counter += 1
if save_counter % 500 == 0:
print "saving %d of %d" % (save_counter, len(outcomes_to_programs))
print("saving %d of %d" % (save_counter, len(outcomes_to_programs)))
fp.write(p+"\n")
fi.write(o+"\n")
ftp.write(str(tokenize(p, tokens))+"\n")
......
research/neural_gpu/wmt_utils.py
View file @ ae0a9409
......@@ -14,6 +14,8 @@
# ==============================================================================
"""Utilities for downloading data from WMT, tokenizing, vocabularies."""
from __future__ import print_function
import gzip
import os
import re
......@@ -53,20 +55,20 @@ _WMT_ENFR_DEV_URL = "http://www.statmt.org/wmt15/dev-v2.tgz"
def maybe_download(directory, filename, url):
"""Download filename from url unless it's already in directory."""
if not tf.gfile.Exists(directory):
print "Creating directory %s" % directory
print("Creating directory %s" % directory)
os.mkdir(directory)
filepath = os.path.join(directory, filename)
if not tf.gfile.Exists(filepath):
print "Downloading %s to %s" % (url, filepath)
print("Downloading %s to %s" % (url, filepath))
filepath, _ = urllib.request.urlretrieve(url, filepath)
statinfo = os.stat(filepath)
print "Successfully downloaded", filename, statinfo.st_size, "bytes"
print("Successfully downloaded", filename, statinfo.st_size, "bytes")
return filepath
def gunzip_file(gz_path, new_path):
"""Unzips from gz_path into new_path."""
print "Unpacking %s to %s" % (gz_path, new_path)
print("Unpacking %s to %s" % (gz_path, new_path))
with gzip.open(gz_path, "rb") as gz_file:
with open(new_path, "wb") as new_file:
for line in gz_file:
......@@ -80,7 +82,7 @@ def get_wmt_enfr_train_set(directory):
tf.gfile.Exists(train_path +".en")):
corpus_file = maybe_download(directory, "training-giga-fren.tar",
_WMT_ENFR_TRAIN_URL)
print "Extracting tar file %s" % corpus_file
print("Extracting tar file %s" % corpus_file)
with tarfile.open(corpus_file, "r") as corpus_tar:
corpus_tar.extractall(directory)
gunzip_file(train_path + ".fr.gz", train_path + ".fr")
......@@ -95,7 +97,7 @@ def get_wmt_enfr_dev_set(directory):
if not (tf.gfile.Exists(dev_path + ".fr") and
tf.gfile.Exists(dev_path + ".en")):
dev_file = maybe_download(directory, "dev-v2.tgz", _WMT_ENFR_DEV_URL)
print "Extracting tgz file %s" % dev_file
print("Extracting tgz file %s" % dev_file)
with tarfile.open(dev_file, "r:gz") as dev_tar:
fr_dev_file = dev_tar.getmember("dev/" + dev_name + ".fr")
en_dev_file = dev_tar.getmember("dev/" + dev_name + ".en")
......@@ -206,7 +208,7 @@ def create_vocabulary(vocabulary_path, data_path, max_vocabulary_size,
normalize_digits: Boolean; if true, all digits are replaced by 0s.
"""
if not tf.gfile.Exists(vocabulary_path):
print "Creating vocabulary %s from data %s" % (vocabulary_path, data_path)
print("Creating vocabulary %s from data %s" % (vocabulary_path, data_path))
vocab, chars = {}, {}
for c in _PUNCTUATION:
chars[c] = 1
......@@ -218,7 +220,7 @@ def create_vocabulary(vocabulary_path, data_path, max_vocabulary_size,
line = " ".join(line_in.split())
counter += 1
if counter % 100000 == 0:
print " processing fr line %d" % counter
print(" processing fr line %d" % counter)
for c in line:
if c in chars:
chars[c] += 1
......@@ -240,7 +242,7 @@ def create_vocabulary(vocabulary_path, data_path, max_vocabulary_size,
line = " ".join(line_in.split())
counter += 1
if counter % 100000 == 0:
print " processing en line %d" % counter
print(" processing en line %d" % counter)
for c in line:
if c in chars:
chars[c] += 1
......@@ -371,7 +373,7 @@ def data_to_token_ids(data_path, target_path, vocabulary_path,
normalize_digits: Boolean; if true, all digits are replaced by 0s.
"""
if not tf.gfile.Exists(target_path):
print "Tokenizing data in %s" % data_path
print("Tokenizing data in %s" % data_path)
vocab, _ = initialize_vocabulary(vocabulary_path)
with tf.gfile.GFile(data_path, mode="rb") as data_file:
with tf.gfile.GFile(target_path, mode="w") as tokens_file:
......@@ -379,7 +381,7 @@ def data_to_token_ids(data_path, target_path, vocabulary_path,
for line in data_file:
counter += 1
if counter % 100000 == 0:
print " tokenizing line %d" % counter
print(" tokenizing line %d" % counter)
token_ids = sentence_to_token_ids(line, vocab, tokenizer,
normalize_digits)
tokens_file.write(" ".join([str(tok) for tok in token_ids]) + "\n")
......
research/neural_programmer/data_utils.py
View file @ ae0a9409
......@@ -15,6 +15,8 @@
"""Functions for constructing vocabulary, converting the examples to integer format and building the required masks for batch computation Author: aneelakantan (Arvind Neelakantan)
"""
from __future__ import print_function
import copy
import numbers
import numpy as np
......@@ -536,15 +538,15 @@ def add_special_words(utility):
utility.reverse_word_ids[utility.word_ids[
utility.entry_match_token]] = utility.entry_match_token
utility.entry_match_token_id = utility.word_ids[utility.entry_match_token]
print "entry match token: ", utility.word_ids[
utility.entry_match_token], utility.entry_match_token_id
print("entry match token: ", utility.word_ids[
utility.entry_match_token], utility.entry_match_token_id)
utility.words.append(utility.column_match_token)
utility.word_ids[utility.column_match_token] = len(utility.word_ids)
utility.reverse_word_ids[utility.word_ids[
utility.column_match_token]] = utility.column_match_token
utility.column_match_token_id = utility.word_ids[utility.column_match_token]
print "entry match token: ", utility.word_ids[
utility.column_match_token], utility.column_match_token_id
print("entry match token: ", utility.word_ids[
utility.column_match_token], utility.column_match_token_id)
utility.words.append(utility.dummy_token)
utility.word_ids[utility.dummy_token] = len(utility.word_ids)
utility.reverse_word_ids[utility.word_ids[
......
research/neural_programmer/model.py
View file @ ae0a9409
......@@ -15,6 +15,8 @@
"""Author: aneelakantan (Arvind Neelakantan)
"""
from __future__ import print_function
import numpy as np
import tensorflow as tf
import nn_utils
......@@ -545,7 +547,7 @@ class Graph():
self.batch_log_prob = tf.zeros([self.batch_size], dtype=self.data_type)
#Perform max_passes and at each pass select operation and column
for curr_pass in range(max_passes):
print "step: ", curr_pass
print("step: ", curr_pass)
output, select, softmax, soft_softmax, column_softmax, soft_column_softmax = self.one_pass(
select, question_embedding, hidden_vectors, hprev, prev_select_1,
curr_pass)
......@@ -636,7 +638,7 @@ class Graph():
self.total_cost = self.compute_error()
optimize_params = self.params.values()
optimize_names = self.params.keys()
print "optimize params ", optimize_names
print("optimize params ", optimize_names)
if (self.utility.FLAGS.l2_regularizer > 0.0):
reg_cost = 0.0
for ind_param in self.params.keys():
......@@ -645,7 +647,7 @@ class Graph():
grads = tf.gradients(self.total_cost, optimize_params, name="gradients")
grad_norm = 0.0
for p, name in zip(grads, optimize_names):
print "grads: ", p, name
print("grads: ", p, name)
if isinstance(p, tf.IndexedSlices):
grad_norm += tf.reduce_sum(p.values * p.values)
elif not (p == None):
......@@ -675,4 +677,3 @@ class Graph():
self.step = adam.apply_gradients(zip(grads, optimize_params),
global_step=self.global_step)
self.init_op = tf.global_variables_initializer()
research/neural_programmer/neural_programmer.py
View file @ ae0a9409
......@@ -113,9 +113,9 @@ def evaluate(sess, data, batch_size, graph, i):
graph))
gc += ct * batch_size
num_examples += batch_size
print "dev set accuracy after ", i, " : ", gc / num_examples
print num_examples, len(data)
print "--------"
print("dev set accuracy after ", i, " : ", gc / num_examples)
print(num_examples, len(data))
print("--------")
def Train(graph, utility, batch_size, train_data, sess, model_dir,
......@@ -142,9 +142,9 @@ def Train(graph, utility, batch_size, train_data, sess, model_dir,
if (i > 0 and i % FLAGS.eval_cycle == 0):
end = time.time()
time_taken = end - start
print "step ", i, " ", time_taken, " seconds "
print("step ", i, " ", time_taken, " seconds ")
start = end
print " printing train set loss: ", train_set_loss / utility.FLAGS.eval_cycle
print(" printing train set loss: ", train_set_loss / utility.FLAGS.eval_cycle)
train_set_loss = 0.0
......@@ -183,23 +183,23 @@ def master(train_data, dev_data, utility):
file_list = sorted(selected_models.items(), key=lambda x: x[0])
if (len(file_list) > 0):
file_list = file_list[0:len(file_list) - 1]
print "list of models: ", file_list
print("list of models: ", file_list)
for model_file in file_list:
model_file = model_file[1]
print "restoring: ", model_file
print("restoring: ", model_file)
saver.restore(sess, model_dir + "/" + model_file)
model_step = int(
model_file.split("_")[len(model_file.split("_")) - 1])
print "evaluating on dev ", model_file, model_step
print("evaluating on dev ", model_file, model_step)
evaluate(sess, dev_data, batch_size, graph, model_step)
else:
ckpt = tf.train.get_checkpoint_state(model_dir)
print "model dir: ", model_dir
print("model dir: ", model_dir)
if (not (tf.gfile.IsDirectory(utility.FLAGS.output_dir))):
print "create dir: ", utility.FLAGS.output_dir
print("create dir: ", utility.FLAGS.output_dir)
tf.gfile.MkDir(utility.FLAGS.output_dir)
if (not (tf.gfile.IsDirectory(model_dir))):
print "create dir: ", model_dir
print("create dir: ", model_dir)
tf.gfile.MkDir(model_dir)
Train(graph, utility, batch_size, train_data, sess, model_dir,
saver)
......@@ -225,10 +225,10 @@ def main(args):
train_data = data_utils.complete_wiki_processing(train_data, utility, True)
dev_data = data_utils.complete_wiki_processing(dev_data, utility, False)
test_data = data_utils.complete_wiki_processing(test_data, utility, False)
print "# train examples ", len(train_data)
print "# dev examples ", len(dev_data)
print "# test examples ", len(test_data)
print "running open source"
print("# train examples ", len(train_data))
print("# dev examples ", len(dev_data))
print("# test examples ", len(test_data))
print("running open source")
#construct TF graph and train or evaluate
master(train_data, dev_data, utility)
......
research/neural_programmer/parameters.py
View file @ ae0a9409
......@@ -59,7 +59,7 @@ class Parameters:
#Biases for the gates and cell
for bias in ["i", "f", "c", "o"]:
if (bias == "f"):
print "forget gate bias"
print("forget gate bias")
params[key + "_" + bias] = tf.Variable(
tf.random_uniform([embedding_dims], 1.0, 1.1, self.utility.
tf_data_type[self.utility.FLAGS.data_type]))
......
research/neural_programmer/wiki_data.py
View file @ ae0a9409
......@@ -22,6 +22,8 @@ columns.
lookup answer (or matrix) is also split into number and word lookup matrix
Author: aneelakantan (Arvind Neelakantan)
"""
from __future__ import print_function
import math
import os
import re
......@@ -56,7 +58,7 @@ def correct_unicode(string):
#string = re.sub("[“”«»]", "\"", string)
#string = re.sub("[•†‡]", "", string)
#string = re.sub("[‐‑–—]", "-", string)
string = re.sub(ur'[\u2E00-\uFFFF]', "", string)
string = re.sub(r'[\u2E00-\uFFFF]', "", string)
string = re.sub("\\s+", " ", string).strip()
return string
......@@ -78,7 +80,7 @@ def full_normalize(string):
# Remove trailing info in brackets
string = re.sub("\[[^\]]*\]", "", string)
# Remove most unicode characters in other languages
string = re.sub(ur'[\u007F-\uFFFF]', "", string.strip())
string = re.sub(r'[\u007F-\uFFFF]', "", string.strip())
# Remove trailing info in parenthesis
string = re.sub("\([^)]*\)$", "", string.strip())
string = final_normalize(string)
......@@ -298,7 +300,7 @@ class WikiQuestionGenerator(object):
question_id, question, target_canon, context)
self.annotated_tables[context] = []
counter += 1
print "Annotated examples loaded ", len(self.annotated_examples)
print("Annotated examples loaded ", len(self.annotated_examples))
f.close()
def is_number_column(self, a):
......
research/object_detection/utils/visualization_utils_test.py
View file @ ae0a9409
......@@ -145,7 +145,7 @@ class VisualizationUtilsTest(tf.test.TestCase):
for i in range(images_with_boxes_np.shape[0]):
img_name = 'image_' + str(i) + '.png'
output_file = os.path.join(self.get_temp_dir(), img_name)
print 'Writing output image %d to %s' % (i, output_file)
print('Writing output image %d to %s' % (i, output_file))
image_pil = Image.fromarray(images_with_boxes_np[i, ...])
image_pil.save(output_file)
......
research/real_nvp/celeba_formatting.py
View file @ ae0a9409
......@@ -30,6 +30,8 @@ python celeba_formatting.py \
"""
from __future__ import print_function
import os
import os.path
......@@ -70,7 +72,7 @@ def main():
writer = tf.python_io.TFRecordWriter(file_out)
for example_idx, img_fn in enumerate(img_fn_list):
if example_idx % 1000 == 0:
print example_idx, "/", num_examples
print(example_idx, "/", num_examples)
image_raw = scipy.ndimage.imread(os.path.join(fn_root, img_fn))
rows = image_raw.shape[0]
cols = image_raw.shape[1]
......
research/real_nvp/imnet_formatting.py
View file @ ae0a9409
......@@ -34,6 +34,8 @@ done
"""
from __future__ import print_function
import os
import os.path
......@@ -73,10 +75,10 @@ def main():
file_out = "%s_%05d.tfrecords"
file_out = file_out % (FLAGS.file_out,
example_idx // n_examples_per_file)
print "Writing on:", file_out
print("Writing on:", file_out)
writer = tf.python_io.TFRecordWriter(file_out)
if example_idx % 1000 == 0:
print example_idx, "/", num_examples
print(example_idx, "/", num_examples)
image_raw = scipy.ndimage.imread(os.path.join(fn_root, img_fn))
rows = image_raw.shape[0]
cols = image_raw.shape[1]
......
research/real_nvp/lsun_formatting.py
View file @ ae0a9409
......@@ -29,6 +29,7 @@ python lsun_formatting.py \
--fn_root [LSUN_FOLDER]
"""
from __future__ import print_function
import os
import os.path
......@@ -68,10 +69,10 @@ def main():
file_out = "%s_%05d.tfrecords"
file_out = file_out % (FLAGS.file_out,
example_idx // n_examples_per_file)
print "Writing on:", file_out
print("Writing on:", file_out)
writer = tf.python_io.TFRecordWriter(file_out)
if example_idx % 1000 == 0:
print example_idx, "/", num_examples
print(example_idx, "/", num_examples)
image_raw = numpy.array(Image.open(os.path.join(fn_root, img_fn)))
rows = image_raw.shape[0]
cols = image_raw.shape[1]
......
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