Python data_utils.get_batch() Examples
The following are 17
code examples of data_utils.get_batch().
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Example #1
| Source File: neural_gpu_trainer.py From Action_Recognition_Zoo with MIT License | 6 votes |
|
def interactive():
"""Interactively probe an existing model."""
with tf.Session() as sess:
model, _, _, _, _, _ = initialize(sess)
sys.stdout.write("Input to Neural GPU, e.g., 0 1. Use -1 for PAD.\n")
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
while inpt:
ids = [data.to_id(s) for s in inpt.strip().split()]
inpt, target = data.get_batch(len(ids), 1, False, "",
preset=(ids, [0 for _ in ids]))
_, res, _, _ = model.step(sess, inpt, target, False)
res = [np.argmax(o, axis=1) for o in res]
res = [o for o in res[:len(ids)] if o > 0]
print " " + " ".join([data.to_symbol(output[0]) for output in res])
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
Example #2
| Source File: neural_gpu_trainer.py From AI_Reader with Apache License 2.0 | 6 votes |
|
def interactive():
"""Interactively probe an existing model."""
with tf.Session() as sess:
model, _, _, _, _, _ = initialize(sess)
sys.stdout.write("Input to Neural GPU, e.g., 0 1. Use -1 for PAD.\n")
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
while inpt:
ids = [data.to_id(s) for s in inpt.strip().split()]
inpt, target = data.get_batch(len(ids), 1, False, "",
preset=(ids, [0 for _ in ids]))
_, res, _, _ = model.step(sess, inpt, target, False)
res = [np.argmax(o, axis=1) for o in res]
res = [o for o in res[:len(ids)] if o > 0]
print " " + " ".join([data.to_symbol(output[0]) for output in res])
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
Example #3
| Source File: neural_gpu_trainer.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 6 votes |
|
def interactive():
"""Interactively probe an existing model."""
with tf.Session() as sess:
model, _, _, _, _, _ = initialize(sess)
sys.stdout.write("Input to Neural GPU, e.g., 0 1. Use -1 for PAD.\n")
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
while inpt:
ids = [data.to_id(s) for s in inpt.strip().split()]
inpt, target = data.get_batch(len(ids), 1, False, "",
preset=(ids, [0 for _ in ids]))
_, res, _, _ = model.step(sess, inpt, target, False)
res = [np.argmax(o, axis=1) for o in res]
res = [o for o in res[:len(ids)] if o > 0]
print " " + " ".join([data.to_symbol(output[0]) for output in res])
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
Example #4
| Source File: neural_gpu_trainer.py From hands-detection with MIT License | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #5
| Source File: neural_gpu_trainer.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #6
| Source File: neural_gpu_trainer.py From models with Apache License 2.0 | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #7
| Source File: neural_gpu_trainer.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #8
| Source File: neural_gpu_trainer.py From HumanRecognition with MIT License | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #9
| Source File: neural_gpu_trainer.py From AI_Reader with Apache License 2.0 | 5 votes |
|
def single_test(l, model, sess, task, nprint, batch_size, print_out=True,
offset=None, ensemble=None, get_steps=False):
"""Test model on test data of length l using the given session."""
inpt, target = data.get_batch(l, batch_size, False, task, offset)
_, res, _, steps = model.step(sess, inpt, target, False, get_steps=get_steps)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size, nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" %s len %d errors %.2f sequence-errors %.2f"
% (task, l, 100*errors, 100*seq_err))
# Ensemble eval.
if ensemble:
results = []
for m in ensemble:
model.saver.restore(sess, m)
_, result, _, _ = model.step(sess, inpt, target, False)
m_errors, m_total, m_seq_err = data.accuracy(inpt, result, target,
batch_size, nprint)
m_seq_err = float(m_seq_err) / batch_size
if total > 0:
m_errors = float(m_errors) / m_total
data.print_out(" %s len %d m-errors %.2f m-sequence-errors %.2f"
% (task, l, 100*m_errors, 100*m_seq_err))
results.append(result)
ens = [sum(o) for o in zip(*results)]
errors, total, seq_err = data.accuracy(inpt, ens, target,
batch_size, nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" %s len %d ens-errors %.2f ens-sequence-errors %.2f"
% (task, l, 100*errors, 100*seq_err))
return errors, seq_err, (steps, inpt, [np.argmax(o, axis=1) for o in res])
Example #10
| Source File: neural_gpu_trainer.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #11
| Source File: neural_gpu_trainer.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #12
| Source File: neural_gpu_trainer.py From DOTA_models with Apache License 2.0 | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #13
| Source File: neural_gpu_trainer.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 5 votes |
|
def single_test(l, model, sess, task, nprint, batch_size, print_out=True,
offset=None, ensemble=None, get_steps=False):
"""Test model on test data of length l using the given session."""
inpt, target = data.get_batch(l, batch_size, False, task, offset)
_, res, _, steps = model.step(sess, inpt, target, False, get_steps=get_steps)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size, nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" %s len %d errors %.2f sequence-errors %.2f"
% (task, l, 100*errors, 100*seq_err))
# Ensemble eval.
if ensemble:
results = []
for m in ensemble:
model.saver.restore(sess, m)
_, result, _, _ = model.step(sess, inpt, target, False)
m_errors, m_total, m_seq_err = data.accuracy(inpt, result, target,
batch_size, nprint)
m_seq_err = float(m_seq_err) / batch_size
if total > 0:
m_errors = float(m_errors) / m_total
data.print_out(" %s len %d m-errors %.2f m-sequence-errors %.2f"
% (task, l, 100*m_errors, 100*m_seq_err))
results.append(result)
ens = [sum(o) for o in zip(*results)]
errors, total, seq_err = data.accuracy(inpt, ens, target,
batch_size, nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" %s len %d ens-errors %.2f ens-sequence-errors %.2f"
% (task, l, 100*errors, 100*seq_err))
return errors, seq_err, (steps, inpt, [np.argmax(o, axis=1) for o in res])
Example #14
| Source File: neural_gpu_trainer.py From Action_Recognition_Zoo with MIT License | 5 votes |
|
def single_test(l, model, sess, task, nprint, batch_size, print_out=True,
offset=None, ensemble=None, get_steps=False):
"""Test model on test data of length l using the given session."""
inpt, target = data.get_batch(l, batch_size, False, task, offset)
_, res, _, steps = model.step(sess, inpt, target, False, get_steps=get_steps)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size, nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" %s len %d errors %.2f sequence-errors %.2f"
% (task, l, 100*errors, 100*seq_err))
# Ensemble eval.
if ensemble:
results = []
for m in ensemble:
model.saver.restore(sess, m)
_, result, _, _ = model.step(sess, inpt, target, False)
m_errors, m_total, m_seq_err = data.accuracy(inpt, result, target,
batch_size, nprint)
m_seq_err = float(m_seq_err) / batch_size
if total > 0:
m_errors = float(m_errors) / m_total
data.print_out(" %s len %d m-errors %.2f m-sequence-errors %.2f"
% (task, l, 100*m_errors, 100*m_seq_err))
results.append(result)
ens = [sum(o) for o in zip(*results)]
errors, total, seq_err = data.accuracy(inpt, ens, target,
batch_size, nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" %s len %d ens-errors %.2f ens-sequence-errors %.2f"
% (task, l, 100*errors, 100*seq_err))
return errors, seq_err, (steps, inpt, [np.argmax(o, axis=1) for o in res])
Example #15
| Source File: neural_gpu_trainer.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #16
| Source File: neural_gpu_trainer.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def single_test(bin_id, model, sess, nprint, batch_size, dev, p, print_out=True,
offset=None, beam_model=None):
"""Test model on test data of length l using the given session."""
if not dev[p][bin_id]:
data.print_out(" bin %d (%d)\t%s\tppl NA errors NA seq-errors NA"
% (bin_id, data.bins[bin_id], p))
return 1.0, 1.0, 0.0
inpt, target = data.get_batch(
bin_id, batch_size, dev[p], FLAGS.height, offset)
if FLAGS.beam_size > 1 and beam_model:
loss, res, new_tgt, scores = m_step(
model, beam_model, sess, batch_size, inpt, target, bin_id,
FLAGS.eval_beam_steps, p)
score_avgs = [sum(s) / float(len(s)) for s in scores]
score_maxs = [max(s) for s in scores]
score_str = ["(%.2f, %.2f)" % (score_avgs[i], score_maxs[i])
for i in xrange(FLAGS.eval_beam_steps)]
data.print_out(" == scores (avg, max): %s" % "; ".join(score_str))
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint, new_tgt, scores[-1])
else:
loss, res, _, _ = model.step(sess, inpt, target, False)
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size,
nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" bin %d (%d)\t%s\tppl %.2f errors %.2f seq-errors %.2f"
% (bin_id, data.bins[bin_id], p, data.safe_exp(loss),
100 * errors, 100 * seq_err))
return (errors, seq_err, loss)
Example #17
| Source File: model.py From RGAN with MIT License | 4 votes |
|
def train_epoch(epoch, samples, labels, sess, Z, X, CG, CD, CS, D_loss, G_loss, D_solver, G_solver,
batch_size, use_time, D_rounds, G_rounds, seq_length,
latent_dim, num_generated_features, cond_dim, max_val, WGAN_clip, one_hot):
"""
Train generator and discriminator for one epoch.
"""
for batch_idx in range(0, int(len(samples) / batch_size) - (D_rounds + (cond_dim > 0)*G_rounds), D_rounds + (cond_dim > 0)*G_rounds):
# update the discriminator
for d in range(D_rounds):
X_mb, Y_mb = data_utils.get_batch(samples, batch_size, batch_idx + d, labels)
Z_mb = sample_Z(batch_size, seq_length, latent_dim, use_time)
if cond_dim > 0:
# CGAN
Y_mb = Y_mb.reshape(-1, cond_dim)
if one_hot:
# change all of the labels to a different one
offsets = np.random.choice(cond_dim-1, batch_size) + 1
new_labels = (np.argmax(Y_mb, axis=1) + offsets) % cond_dim
Y_wrong = np.zeros_like(Y_mb)
Y_wrong[np.arange(batch_size), new_labels] = 1
else:
# flip all of the bits (assuming binary...)
Y_wrong = 1 - Y_mb
_ = sess.run(D_solver, feed_dict={X: X_mb, Z: Z_mb, CD: Y_mb, CS: Y_wrong, CG: Y_mb})
else:
_ = sess.run(D_solver, feed_dict={X: X_mb, Z: Z_mb})
if WGAN_clip:
# clip the weights
_ = sess.run([clip_disc_weights])
# update the generator
for g in range(G_rounds):
if cond_dim > 0:
# note we are essentially throwing these X_mb away...
X_mb, Y_mb = data_utils.get_batch(samples, batch_size, batch_idx + D_rounds + g, labels)
_ = sess.run(G_solver,
feed_dict={Z: sample_Z(batch_size, seq_length, latent_dim, use_time=use_time), CG: Y_mb})
else:
_ = sess.run(G_solver,
feed_dict={Z: sample_Z(batch_size, seq_length, latent_dim, use_time=use_time)})
# at the end, get the loss
if cond_dim > 0:
D_loss_curr, G_loss_curr = sess.run([D_loss, G_loss], feed_dict={X: X_mb, Z: sample_Z(batch_size, seq_length, latent_dim, use_time=use_time), CG: Y_mb, CD: Y_mb})
D_loss_curr = np.mean(D_loss_curr)
G_loss_curr = np.mean(G_loss_curr)
else:
D_loss_curr, G_loss_curr = sess.run([D_loss, G_loss], feed_dict={X: X_mb, Z: sample_Z(batch_size, seq_length, latent_dim, use_time=use_time)})
D_loss_curr = np.mean(D_loss_curr)
G_loss_curr = np.mean(G_loss_curr)
return D_loss_curr, G_loss_curr
