Python data_utils.test_set() Examples
The following are 3
code examples of data_utils.test_set().
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Example #1
| Source File: neural_gpu_trainer.py From Action_Recognition_Zoo with MIT License | 5 votes |
|
def evaluate():
"""Evaluate an existing model."""
batch_size = FLAGS.batch_size
tasks = FLAGS.task.split("-")
with tf.Session() as sess:
model, min_length, max_length, _, _, ensemble = initialize(sess)
bound = data.bins[-1] + 1
for t in tasks:
l = min_length
while l < max_length + EXTRA_EVAL and l < bound:
_, seq_err, _ = single_test(l, model, sess, t, FLAGS.nprint,
batch_size, ensemble=ensemble)
l += 1
while l < bound + 1 and not data.test_set[t][l]:
l += 1
# Animate.
if FLAGS.animate:
anim_size = 2
_, _, test_data = single_test(l, model, sess, t, 0, anim_size,
get_steps=True)
animate(l, test_data, anim_size)
# More tests.
_, seq_err = multi_test(data.forward_max, model, sess, t, FLAGS.nprint,
batch_size * 4, ensemble=ensemble)
if seq_err < 0.01: # Super-test if we're very good and in large-test mode.
if data.forward_max > 4000 and len(tasks) == 1:
multi_test(data.forward_max, model, sess, tasks[0], FLAGS.nprint,
batch_size * 64, 0, ensemble=ensemble)
Example #2
| Source File: neural_gpu_trainer.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 5 votes |
|
def evaluate():
"""Evaluate an existing model."""
batch_size = FLAGS.batch_size
tasks = FLAGS.task.split("-")
with tf.Session() as sess:
model, min_length, max_length, _, _, ensemble = initialize(sess)
bound = data.bins[-1] + 1
for t in tasks:
l = min_length
while l < max_length + EXTRA_EVAL and l < bound:
_, seq_err, _ = single_test(l, model, sess, t, FLAGS.nprint,
batch_size, ensemble=ensemble)
l += 1
while l < bound + 1 and not data.test_set[t][l]:
l += 1
# Animate.
if FLAGS.animate:
anim_size = 2
_, _, test_data = single_test(l, model, sess, t, 0, anim_size,
get_steps=True)
animate(l, test_data, anim_size)
# More tests.
_, seq_err = multi_test(data.forward_max, model, sess, t, FLAGS.nprint,
batch_size * 4, ensemble=ensemble)
if seq_err < 0.01: # Super-test if we're very good and in large-test mode.
if data.forward_max > 4000 and len(tasks) == 1:
multi_test(data.forward_max, model, sess, tasks[0], FLAGS.nprint,
batch_size * 64, 0, ensemble=ensemble)
Example #3
| Source File: neural_gpu_trainer.py From AI_Reader with Apache License 2.0 | 5 votes |
|
def evaluate():
"""Evaluate an existing model."""
batch_size = FLAGS.batch_size
tasks = FLAGS.task.split("-")
with tf.Session() as sess:
model, min_length, max_length, _, _, ensemble = initialize(sess)
bound = data.bins[-1] + 1
for t in tasks:
l = min_length
while l < max_length + EXTRA_EVAL and l < bound:
_, seq_err, _ = single_test(l, model, sess, t, FLAGS.nprint,
batch_size, ensemble=ensemble)
l += 1
while l < bound + 1 and not data.test_set[t][l]:
l += 1
# Animate.
if FLAGS.animate:
anim_size = 2
_, _, test_data = single_test(l, model, sess, t, 0, anim_size,
get_steps=True)
animate(l, test_data, anim_size)
# More tests.
_, seq_err = multi_test(data.forward_max, model, sess, t, FLAGS.nprint,
batch_size * 4, ensemble=ensemble)
if seq_err < 0.01: # Super-test if we're very good and in large-test mode.
if data.forward_max > 4000 and len(tasks) == 1:
multi_test(data.forward_max, model, sess, tasks[0], FLAGS.nprint,
batch_size * 64, 0, ensemble=ensemble)
