def eval_mos(_):
# We want to see all the logging messages for this tutorial.
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new TensorFlow session.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession()
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
audio_processor = input_data.AudioProcessor(
FLAGS.data_url, FLAGS.data_dir,
FLAGS.silence_percentage, FLAGS.unknown_percentage,
FLAGS.wanted_words.split(','), FLAGS.training_percentage,
FLAGS.validation_percentage, FLAGS.testing_percentage, model_settings)
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
# uid_count = audio_processor.num_uids
uid_count = audio_processor.num_uids
print("Label count: %d uid count: %d" % (label_count, uid_count))
#sys.exit()
time_shift_samples = int((FLAGS.time_shift_ms * FLAGS.sample_rate) / 1000)
fingerprint_input = tf.placeholder(tf.float32, [None, fingerprint_size],
name='fingerprint_input')
net = lambda scope: lambda inp: models.create_model(inp,
model_settings,
FLAGS.
model_architecture,
is_training=True,
scope=scope)
# Define loss and optimizer
ground_truth_label = tf.placeholder(tf.float32, [None, label_count],
name='groundtruth_input')
ground_truth_style = tf.placeholder(tf.float32, [None, uid_count],
name='groundtruth_style')
with tf.variable_scope('crossgrad'):
label_net = net('label')
label_embedding = label_net(fingerprint_input)
with tf.variable_scope(''):
cross_entropy, label_logits = losses.mos(label_embedding,
ground_truth_style,
ground_truth_label,
label_count, uid_count,
fingerprint_input, FLAGS)
tune_var = tf.get_variable("tune_var", [FLAGS.num_uids])
c_wts = tf.sigmoid(tune_var)
c_wts /= tf.norm(c_wts)
# c_wts /= tf.reduce_sum(c_wts)
# tune_var = tf.get_variable("tune_var", [uid_count])
with tf.variable_scope('', reuse=True):
emb_mat = tf.get_variable("emb_mat", [uid_count, FLAGS.num_uids])
# _cwts = tf.nn.softmax(tune_var)
# c_wts = tf.einsum("j,ju->u", _cwts, emb_mat)
logits_for_tune = losses.mos_tune(label_embedding, c_wts,
ground_truth_label, label_count,
fingerprint_input, FLAGS)
# look away, ugly code!!
logits_dir = losses.mos_tune(label_embedding, c_wts,
ground_truth_label, label_count,
fingerprint_input, FLAGS)
# logits_for_tune = tf.nn.relu(logits_for_tune)
# logits_dir = tf.nn.relu(logits_dir)
probs_for_tune = tf.nn.softmax(logits_for_tune, axis=1)
probs_dir = tf.nn.softmax(logits_dir, axis=1)
agg_prob = tf.reduce_sum(probs_for_tune, axis=0)
agg_prob2 = tf.reduce_sum(probs_dir, axis=0)
# the first label is silence which is not present for many ids
np_u = np.ones([label_count], dtype=np.float32) / (label_count - 1)
np_u[0] = 0
U = tf.constant(np_u)
_l = logits_for_tune - tf.expand_dims(
tf.reduce_min(logits_for_tune, axis=1), axis=1)
# _l /= tf.expand_dims(tf.reduce_max(_l, 1), 1)
loss1 = tf.reduce_sum(
tf.abs(agg_prob -
(U * tf.cast(tf.shape(probs_for_tune)[0], tf.float32))))
loss2 = -tf.reduce_mean(
tf.reduce_sum(probs_for_tune * tf.one_hot(
tf.argmax(probs_for_tune, axis=1), depth=label_count),
axis=1))
loss4 = -tf.reduce_mean(tf.reduce_sum(probs_for_tune * (_l), axis=1))
loss_for_tune = loss4
_l = logits_dir - tf.expand_dims(tf.reduce_min(logits_dir, axis=1), axis=1)
_l /= tf.expand_dims(tf.reduce_max(_l, 1), 1)
loss1 = tf.reduce_sum(
tf.abs(agg_prob2 -
(U * tf.cast(tf.shape(probs_for_tune)[0], tf.float32))))
loss2 = -tf.reduce_mean(
tf.reduce_sum(probs_dir * tf.one_hot(tf.argmax(probs_dir, axis=1),
depth=label_count),
axis=1))
loss4 = -tf.reduce_mean(tf.reduce_sum(probs_dir * (_l), axis=1))
loss_dir = loss4
predicted_tuned_indices = tf.argmax(logits_for_tune, axis=1)
tune_acc = tf.reduce_mean(
tf.cast(
tf.equal(predicted_tuned_indices, tf.argmax(ground_truth_label,
1)), tf.float32))
control_dependencies = []
if FLAGS.check_nans:
checks = tf.add_check_numerics_ops()
control_dependencies = [checks]
predicted_indices = tf.argmax(label_logits, 1)
expected_indices = tf.argmax(ground_truth_label, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
global_step = tf.contrib.framework.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
lvars = [
var for var in tf.global_variables() if var.name.find("tune_var") < 0
]
saver = tf.train.Saver(lvars)
opt = tf.train.MomentumOptimizer(0.1, 0.1, use_nesterov=True)
# opt = tf.train.AdamOptimizer(0.01)
# opt = tf.train.GradientDescentOptimizer(0.2)
# Create the back propagation and training evaluation machinery in the graph.
with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
tune_op = opt.minimize(loss_for_tune, var_list=[tune_var])
tf.global_variables_initializer().run()
print(tf.trainable_variables())
sys.stdout.flush()
if FLAGS.model_dir:
saver.restore(sess, FLAGS.model_dir)
start_step = global_step.eval(session=sess)
ph_uidx = tf.placeholder(tf.int32, [])
tf_emb = tf.nn.embedding_lookup(emb_mat, [ph_uidx])
tf_emb = tf.sigmoid(tf_emb)
tf_emb /= tf.expand_dims(tf.norm(tf_emb, axis=1), axis=1)
# sess.graph.finalize()
set_size = audio_processor.set_size('training')
tf.logging.info('set_size=%d', set_size)
# train accuracy is almost 100.
# total_accuracy = 0
# total_conf_matrix = None
# for i in xrange(0, set_size, FLAGS.batch_size):
# test_fingerprints, test_ground_truth, test_uids = audio_processor.get_data(
# FLAGS.batch_size, i, model_settings, 0.0, 0.0, 0, 'training', sess)
# test_accuracy, conf_matrix = sess.run(
# [evaluation_step, confusion_matrix],
# feed_dict={
# fingerprint_input: test_fingerprints,
# ground_truth_label: test_ground_truth,
# ground_truth_style: test_uids,
# })
# batch_size = min(FLAGS.batch_size, set_size - i)
# total_accuracy += (test_accuracy * batch_size) / set_size
# if total_conf_matrix is None:
# total_conf_matrix = conf_matrix
# else:
# total_conf_matrix += conf_matrix
# tf.logging.info('Confusion Matrix:\n %s' % (total_conf_matrix))
# tf.logging.info('Final train accuracy = %.1f%% (N=%d)' % (total_accuracy * 100, set_size))
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
# for i in xrange(0, set_size, FLAGS.batch_size):
_t1, _t2, _t3 = 0, 0, 0
_i1, _i2, _i3 = 0, 0, 0
agg_acc = np.zeros([uid_count])
for test_fingerprints, test_ground_truth in audio_processor.get_data_per_domain(
model_settings, 0.0, 0.0, 0, 'validation', sess):
sess.run(tune_var.initializer)
label_dist = np.sum(test_ground_truth, axis=0)
if label_dist[0] > 0 or _i1 >= 20:
continue
print("Num samples: %d label dist: %s" %
(len(test_fingerprints), label_dist))
_ln = len(test_ground_truth)
max_test = -1
ruid = np.random.randint(0, uid_count)
all_acc = []
for uidx in tqdm.tqdm(range(uid_count)):
uids = np.zeros([_ln, uid_count])
uids[np.arange(_ln), uidx] = 1
test_accuracy, conf_matrix = sess.run(
[evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth,
ground_truth_style: uids,
})
all_acc.append(test_accuracy)
if test_accuracy > max_test:
max_test = test_accuracy
best_wt = sess.run(tf_emb, feed_dict={ph_uidx: uidx})[0]
#print ("Test acc: %0.4f" % test_accuracy)
if uidx == ruid:
base = test_accuracy
_t3 += base
_i3 += 1
_t1 += max_test
_i1 += 1.
agg_acc += np.array(sorted(all_acc))
print("Base Test acc: %0.4f" % (base))
print("Best Test acc: %0.4f -- wt: %s" % (max_test, best_wt))
fd = {
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth,
}
# sess.run(tf.assign(tune_var, best_wt))
# np_loss_for_best = sess.run(loss_dir, feed_dict=fd)
# print ("Loss for best wt: %f" % np_loss_for_best)
sess.run(tune_var.initializer)
for _it in range(100):
_, np_l, np_wts, np_wts2 = sess.run(
[tune_op, loss_for_tune, c_wts, tune_var], feed_dict=fd)
if _it % 100 == 0:
print("Loss: %f wts: %s %s" % (np_l, np_wts, np_wts2))
np_tuned_acc, np_preds = sess.run(
[tune_acc,
tf.one_hot(predicted_tuned_indices, label_count)],
feed_dict=fd)
_t2 += np_tuned_acc
_i2 += 1
print("Tuned acc: %f dist: %s" %
(100 * np_tuned_acc, np.sum(np_preds, axis=0)))
#print (conf_matrix)
print("Defau Avg test accuracy: %f over %d domains" % ((_t3 / _i3), _i3))
print("Brute Avg test accuracy: %f over %d domains" % ((_t1 / _i1), _i1))
print("Tuned Avg test accuracy: %f over %d domains" % ((_t2 / _i2), _i2))
agg_acc /= _i1
for pi in range(0, 110, 10):
print(pi, np.percentile(agg_acc, pi))
def get_initialization(_):
# Start a new TensorFlow session.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession()
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
audio_processor = input_data.AudioProcessor(
FLAGS.data_url, FLAGS.data_dir,
FLAGS.silence_percentage, FLAGS.unknown_percentage,
FLAGS.wanted_words.split(','), FLAGS.training_percentage,
FLAGS.validation_percentage, FLAGS.testing_percentage, model_settings)
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
uid_count = audio_processor.num_uids
time_shift_samples = int((FLAGS.time_shift_ms * FLAGS.sample_rate) / 1000)
fingerprint_input = tf.placeholder(tf.float32, [None, fingerprint_size],
name='fingerprint_input')
net = lambda scope: lambda inp: models.create_model(inp,
model_settings,
FLAGS.
model_architecture,
is_training=True,
scope=scope)
# Define loss and optimizer
ground_truth_label = tf.placeholder(tf.float32, [None, label_count],
name='groundtruth_input')
ground_truth_style = tf.placeholder(tf.float32, [None, uid_count],
name='groundtruth_style')
with tf.variable_scope('crossgrad'):
label_net = net('label')
label_embedding = label_net(fingerprint_input)
with tf.variable_scope(''):
cross_entropy, label_logits = losses.mos(label_embedding,
ground_truth_style,
ground_truth_label,
label_count, uid_count,
fingerprint_input, FLAGS)
tune_var = tf.get_variable("tune_var", [FLAGS.num_uids])
c_wts = tf.sigmoid(tune_var)
c_wts /= tf.norm(c_wts)
with tf.variable_scope('', reuse=True):
emb_mat = tf.get_variable("emb_mat", [uid_count, FLAGS.num_uids])
tf_reprs = losses.mos_tune_project(label_embedding)
logits_for_tune, tf_reprs = losses.mos_tune(tf_reprs, c_wts,
ground_truth_label,
label_count, FLAGS)
sm_w = tf.get_variable("sm_w",
shape=[FLAGS.num_uids, 128, label_count])
sm_bias = tf.get_variable("sm_bias",
shape=[FLAGS.num_uids, label_count])
probs_for_tune = tf.nn.softmax(logits_for_tune, axis=1)
agg_prob = tf.reduce_sum(probs_for_tune, axis=0)
# the first label is silence which is not present for many ids
loss_for_tune = tf.losses.softmax_cross_entropy(
onehot_labels=ground_truth_label, logits=logits_for_tune)
predicted_tuned_indices = tf.argmax(logits_for_tune, axis=1)
tune_acc = tf.reduce_mean(
tf.cast(
tf.equal(predicted_tuned_indices, tf.argmax(ground_truth_label,
1)), tf.float32))
control_dependencies = []
if FLAGS.check_nans:
checks = tf.add_check_numerics_ops()
control_dependencies = [checks]
predicted_indices = tf.argmax(logits_for_tune, 1)
expected_indices = tf.argmax(ground_truth_label, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
global_step = tf.contrib.framework.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
lvars = [
var for var in tf.global_variables() if var.name.find("tune_var") < 0
]
saver = tf.train.Saver(lvars)
opt = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.mom, use_nesterov=True)
# Create the back propagation and training evaluation machinery in the graph.
with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
tune_op = opt.minimize(loss_for_tune, var_list=[tune_var])
tf.global_variables_initializer().run()
print(tf.trainable_variables())
sys.stdout.flush()
if FLAGS.model_dir:
saver.restore(sess, FLAGS.model_dir)
start_step = global_step.eval(session=sess)
ph_uidx = tf.placeholder(tf.int32, [])
tf_emb = tf.nn.embedding_lookup(emb_mat, [ph_uidx])
tf_emb = tf.sigmoid(tf_emb)
tf_emb /= tf.expand_dims(tf.norm(tf_emb, axis=1), axis=1)
set_size = audio_processor.set_size('training')
tf.logging.info('set_size=%d', set_size)
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
nsteps = 1000
batch_size = 128
best_val = -1
best = None
for _iter in range(nsteps):
train_fingerprints, train_ground_truth, train_uids = audio_processor.get_data(
batch_size, 0, model_settings, 0, 0, 0, 'training', sess)
fd = {
fingerprint_input: train_fingerprints,
ground_truth_label: train_ground_truth,
}
_, np_l, np_wts, np_wts2 = sess.run(
[tune_op, loss_for_tune, c_wts, tune_var], feed_dict=fd)
if _iter % 50 == 0:
print("Loss: %f wts: %s %s" % (np_l, np_wts, np_wts2))
val, test = -1, -1
for si, split in enumerate(['validation', 'testing']):
inps, labels, _ = test_fingerprints, test_ground_truth, test_uids = audio_processor.get_data(
-1, 0, model_settings, 0.0, 0.0, 0, split, sess)
fd = {fingerprint_input: inps, ground_truth_label: labels}
np_acc = sess.run(evaluation_step, feed_dict=fd)
print("Split: %s -- acc: %f" % (split, np_acc))
if si == 0:
val = np_acc
else:
test = np_acc
if val > best_val:
best_val = val
best = (val, test, np_wts, np_wts2)
print("Best validation accuracy %f -- %s" % (best_val, best))
def eval_vanilla(_):
"""
Use all the label data to fine-tune the combination weights
"""
# Start a new TensorFlow session.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession()
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
audio_processor = input_data.AudioProcessor(
FLAGS.data_url, FLAGS.data_dir,
FLAGS.silence_percentage, FLAGS.unknown_percentage,
FLAGS.wanted_words.split(','), FLAGS.training_percentage,
FLAGS.validation_percentage, FLAGS.testing_percentage, model_settings)
# val_fname, test_fname = 'extra-data/all_paths.txt', 'extra-data/all_paths.txt'
# audio_processor = input_data.make_processor(val_fname, test_fname, FLAGS.wanted_words.split(','), FLAGS.data_dir, model_settings)
# audio_processor.num_uids = FLAGS.training_percentage + 2
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
uid_count = audio_processor.num_uids
print("Label count: %d uid count: %d" % (label_count, uid_count))
#sys.exit()
time_shift_samples = int((FLAGS.time_shift_ms * FLAGS.sample_rate) / 1000)
fingerprint_input = tf.placeholder(tf.float32, [None, fingerprint_size],
name='fingerprint_input')
net = lambda scope: lambda inp: models.create_model(inp,
model_settings,
FLAGS.
model_architecture,
is_training=True,
scope=scope)
# Define loss and optimizer
ground_truth_label = tf.placeholder(tf.float32, [None, label_count],
name='groundtruth_input')
ground_truth_style = tf.placeholder(tf.float32, [None, uid_count],
name='groundtruth_style')
with tf.variable_scope('crossgrad'):
label_net = net('label')
label_embedding = label_net(fingerprint_input)
with tf.variable_scope(''):
cross_entropy, label_logits = losses.mos(label_embedding,
ground_truth_style,
ground_truth_label,
label_count, uid_count,
fingerprint_input, FLAGS)
tune_var = tf.get_variable("tune_var", [FLAGS.num_uids])
c_wts = tf.sigmoid(tune_var)
c_wts /= tf.norm(c_wts)
with tf.variable_scope('', reuse=True):
emb_mat = tf.get_variable("emb_mat", [uid_count, FLAGS.num_uids])
tf_reprs = losses.mos_tune_project(label_embedding)
logits_for_tune, tf_reprs = losses.mos_tune(tf_reprs, c_wts,
ground_truth_label,
label_count, FLAGS)
sm_w = tf.get_variable("sm_w",
shape=[FLAGS.num_uids, 128, label_count])
sm_bias = tf.get_variable("sm_bias",
shape=[FLAGS.num_uids, label_count])
probs_for_tune = tf.nn.softmax(logits_for_tune, axis=1)
agg_prob = tf.reduce_sum(probs_for_tune, axis=0)
# the first label is silence which is not present for many ids
loss_for_tune = tf.losses.softmax_cross_entropy(
onehot_labels=ground_truth_label, logits=logits_for_tune)
predicted_tuned_indices = tf.argmax(logits_for_tune, axis=1)
tune_acc = tf.reduce_mean(
tf.cast(
tf.equal(predicted_tuned_indices, tf.argmax(ground_truth_label,
1)), tf.float32))
control_dependencies = []
if FLAGS.check_nans:
checks = tf.add_check_numerics_ops()
control_dependencies = [checks]
predicted_indices = tf.argmax(label_logits, 1)
expected_indices = tf.argmax(ground_truth_label, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
global_step = tf.contrib.framework.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
lvars = [
var for var in tf.global_variables() if var.name.find("tune_var") < 0
]
saver = tf.train.Saver(lvars)
opt = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.mom, use_nesterov=True)
# Create the back propagation and training evaluation machinery in the graph.
with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
tune_op = opt.minimize(loss_for_tune, var_list=[tune_var])
tf.global_variables_initializer().run()
print(tf.trainable_variables())
sys.stdout.flush()
if FLAGS.model_dir:
saver.restore(sess, FLAGS.model_dir)
start_step = global_step.eval(session=sess)
ph_uidx = tf.placeholder(tf.int32, [])
tf_emb = tf.nn.embedding_lookup(emb_mat, [ph_uidx])
tf_emb = tf.sigmoid(tf_emb)
tf_emb /= tf.expand_dims(tf.norm(tf_emb, axis=1), axis=1)
set_size = audio_processor.set_size('training')
tf.logging.info('set_size=%d', set_size)
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
for split in ['validation', 'testing']:
_t1, _t2, _t3 = 0, 0, 0
_i1, _i2, _i3 = 0, 0, 0
num_try = 10
# agg_acc = np.zeros(uid_count)
for test_fingerprints, test_ground_truth in audio_processor.get_data_per_domain(
model_settings, 0.0, 0.0, 0, split, sess):
sess.run(tune_var.initializer)
label_dist = np.sum(test_ground_truth, axis=0)
if label_dist[0] > 0:
continue
print("Num samples: %d label dist: %s" %
(len(test_fingerprints), label_dist))
_ln = len(test_ground_truth)
max_test = -1
ruid = np.random.randint(0, uid_count)
fd = {
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth,
}
num_try = 10
agg_acc = np.zeros([num_try + 1])
all_acc = []
for uidx in tqdm.tqdm(
np.random.choice(np.arange(uid_count), num_try).tolist() +
[ruid]):
uids = np.zeros([_ln, uid_count])
uids[np.arange(_ln), uidx] = 1
test_accuracy, conf_matrix = sess.run(
[evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth,
ground_truth_style: uids,
})
all_acc.append(test_accuracy)
if test_accuracy > max_test:
max_test = test_accuracy
best_wt = sess.run(tf_emb, feed_dict={ph_uidx: uidx})[0]
#print ("Test acc: %0.4f" % test_accuracy)
if uidx == ruid:
base = test_accuracy
_t3 += base
_i3 += 1
_t1 += max_test
_i1 += 1.
agg_acc += np.array(sorted(all_acc))
print("Base Test acc: %0.4f" % (base))
print("Best Test acc: %0.4f -- wt: %s" % (max_test, best_wt))
np_emb_wt = sess.run(emb_mat)
rand_emb = np_emb_wt[ruid, :]
sess.run(tf.assign(tune_var, rand_emb))
for _it in range(FLAGS.iters):
_, np_l, np_wts, np_wts2 = sess.run(
[tune_op, loss_for_tune, c_wts, tune_var], feed_dict=fd)
if _it % 100 == 0:
print("Loss: %f wts: %s %s" % (np_l, np_wts, np_wts2))
np_tuned_acc, np_preds = sess.run(
[tune_acc,
tf.one_hot(predicted_tuned_indices, label_count)],
feed_dict=fd)
_t2 += np_tuned_acc
_i2 += 1
print("Tuned acc: %f dist: %s" %
(100 * np_tuned_acc, np.sum(np_preds, axis=0)))
#print (conf_matrix)
print("Defau Avg test accuracy: %f over %d domains" %
((_t3 / _i3), _i3))
print("Brute Avg test accuracy: %f over %d domains" %
((_t1 / _i1), _i1))
print("Tuned Avg test accuracy: %f over %d domains" %
((_t2 / _i2), _i2))
print("")
def eval_mos(_):
# We want to see all the logging messages for this tutorial.
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new TensorFlow session.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession()
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
# audio_processor = input_data.AudioProcessor(
# FLAGS.data_url, FLAGS.data_dir, FLAGS.silence_percentage,
# FLAGS.unknown_percentage,
# FLAGS.wanted_words.split(','), FLAGS.training_percentage,
# FLAGS.validation_percentage, FLAGS.testing_percentage, model_settings)
val_fname, test_fname = 'extra-data/all_paths.txt', 'extra-data/all_paths.txt'
audio_processor = input_data.make_processor(val_fname, test_fname,
FLAGS.wanted_words.split(','),
FLAGS.data_dir, model_settings)
audio_processor.num_uids = FLAGS.training_percentage + 2
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
uid_count = audio_processor.num_uids
print("Label count: %d uid count: %d" % (label_count, uid_count))
#sys.exit()
fingerprint_input = tf.placeholder(tf.float32, [None, fingerprint_size],
name='fingerprint_input')
net = lambda scope: lambda inp: models.create_model(inp,
model_settings,
FLAGS.
model_architecture,
is_training=True,
scope=scope)
# Define loss and optimizer
ground_truth_label = tf.placeholder(tf.float32, [None, label_count],
name='groundtruth_input')
ground_truth_style = tf.placeholder(tf.float32, [None, uid_count],
name='groundtruth_style')
ph_reprs = tf.placeholder(tf.float32, [None, 128],
name='representations_input')
with tf.variable_scope('crossgrad'):
label_net = net('label')
label_embedding = label_net(fingerprint_input)
with tf.variable_scope(''):
cross_entropy, label_logits = losses.mos(label_embedding,
ground_truth_style,
ground_truth_label,
label_count, uid_count,
fingerprint_input, FLAGS)
tune_var = tf.get_variable("tune_var", [FLAGS.num_uids],
initializer=tf.zeros_initializer)
c_wts = tf.sigmoid(tune_var)
c_wts /= tf.norm(c_wts)
with tf.variable_scope('', reuse=True):
emb_mat = tf.get_variable("emb_mat", [uid_count, FLAGS.num_uids])
tf_reprs = losses.mos_tune_project(label_embedding)
logits_for_tune, _ = losses.mos_tune(tf_reprs, c_wts,
ground_truth_label, label_count,
FLAGS)
logits_for_tune2, _ = losses.mos_tune(ph_reprs, c_wts,
ground_truth_label, label_count,
FLAGS)
sm_w = tf.get_variable("sm_w",
shape=[FLAGS.num_uids, 128, label_count])
probs_for_tune = tf.nn.softmax(logits_for_tune, axis=1)
loss_for_tune = tf.losses.softmax_cross_entropy(
onehot_labels=ground_truth_label, logits=logits_for_tune)
loss_for_tune2 = tf.losses.softmax_cross_entropy(
onehot_labels=ground_truth_label, logits=logits_for_tune2)
predicted_tuned_indices = tf.argmax(logits_for_tune, axis=1)
tune_acc = tf.reduce_mean(
tf.cast(
tf.equal(predicted_tuned_indices, tf.argmax(ground_truth_label,
1)), tf.float32))
control_dependencies = []
if FLAGS.check_nans:
checks = tf.add_check_numerics_ops()
control_dependencies = [checks]
predicted_indices = tf.argmax(label_logits, 1)
expected_indices = tf.argmax(ground_truth_label, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
global_step = tf.contrib.framework.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
common_var = [
var for var in tf.all_variables() if var.name.find('common_var') >= 0
][0]
lvars = [
var for var in tf.global_variables() if var.name.find("tune_var") < 0
]
saver = tf.train.Saver(lvars)
opt = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.mom, use_nesterov=True)
# Create the back propagation and training evaluation machinery in the graph.
with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
tune_op = opt.minimize(loss_for_tune, var_list=[tune_var])
tune_op2 = opt.minimize(loss_for_tune2, var_list=[tune_var])
tf.global_variables_initializer().run()
print(tf.trainable_variables())
sys.stdout.flush()
if FLAGS.model_dir:
saver.restore(sess, FLAGS.model_dir)
start_step = global_step.eval(session=sess)
np_common_var = sess.run(common_var)
ph_uidx = tf.placeholder(tf.int32, [])
tf_emb = tf.nn.embedding_lookup(emb_mat, [ph_uidx])
tf_emb = tf.sigmoid(tf_emb)
tf_emb /= tf.expand_dims(tf.norm(tf_emb, axis=1), axis=1)
set_size = audio_processor.set_size('training')
tf.logging.info('set_size=%d', set_size)
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
wfname = "results.txt"
wf = open(wfname, "a")
params = "|".join([
str(_) for _ in [
"mos", FLAGS.lr, FLAGS.mom, FLAGS.iters, FLAGS.inneriters,
FLAGS.nseeds
]
])
wf.write("|" + params)
# with open("cache/smws.pkl", "wb") as f1, open("cache/smbs,pkl", "wb") as f2:
# pickle.dump(sess.run(sm_w), f1)
# pickle.dump(sess.run(sm_bias), f2)
# select centers for each label cluster
def select_centers(np_probs, np_reprs, N=20):
preds = np.argmax(np_probs, axis=1)
mean_vecs = {}
count = {}
for _p in preds:
count[_p] = count.get(_p, 0) + 1
for _p in np.unique(preds):
# tested
# print ("Shape: ", np.shape(np_reprs[np.where(np.equal(preds, _p))]))
# print ("Count: ", count[_p])
mean_vecs[_p] = np.mean(np_reprs[np.where(np.equal(preds, _p))],
axis=0)
label_avg = np.array([mean_vecs[_p] for _p in preds])
dists = np.linalg.norm(np_reprs - label_avg, axis=1)
# select centers
return np.argpartition(dists, kth=N)[:N]
def get_centering_data(np_probs, np_reprs):
preds = np.argmax(np_probs, axis=1)
mean_vecs = {}
count = {}
for _p in preds:
count[_p] = count.get(_p, 0) + 1
for _p in np.unique(preds):
mean_vecs[_p] = np.mean(np_reprs[np.where(np.equal(preds, _p))],
axis=0)
label_avg = np.array([mean_vecs[_p] for _p in preds])
x, y = [], []
for _p in np.unique(preds):
if count[_p] >= 0:
_l = np.zeros(np.shape(np_probs)[-1])
_l[_p] = 1
x.append(mean_vecs[_p])
y.append(_l)
x, y = np.array(x), np.array(y)
return np.array(x), np.array(y)
for split in ['validation', 'testing']:
_t1, _t2, _t3, _t4 = 0, 0, 0, 0
_i1, _i2, _i3, _i4 = 0, 0, 0, 0
num_try = 10
agg_acc = np.zeros([num_try + 1])
# agg_acc = np.zeros(uid_count)
for test_fingerprints, test_ground_truth in audio_processor.get_data_per_domain(
model_settings, 0.0, 0.0, 0, split, sess):
sess.run(tune_var.initializer)
label_dist = np.sum(test_ground_truth, axis=0)
# ignoring the one domain which has only silence examples
if label_dist[0] > 0:
continue
print("Num samples: %d label dist: %s" %
(len(test_fingerprints), label_dist))
_ln = len(test_ground_truth)
max_test = -1
ruid = np.random.randint(0, uid_count)
all_acc = []
# range(uid_count)
# np.random.choice(np.arange(uid_count), num_try).tolist() + [ruid]
for uidx in tqdm.tqdm(
np.random.choice(np.arange(uid_count), num_try).tolist() +
[ruid]):
uids = np.zeros([_ln, uid_count])
uids[np.arange(_ln), uidx] = 1
test_accuracy, conf_matrix = sess.run(
[evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth,
ground_truth_style: uids,
})
all_acc.append(test_accuracy)
if test_accuracy > max_test:
max_test = test_accuracy
best_wt = sess.run(tf_emb, feed_dict={ph_uidx: uidx})[0]
#print ("Test acc: %0.4f" % test_accuracy)
if uidx == ruid:
base = test_accuracy
_t3 += base
_i3 += 1
_t1 += max_test
_i1 += 1.
agg_acc += np.array(sorted(all_acc))
fd = {
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth,
}
np_emb_wt = sess.run(emb_mat)
rand_emb = np_emb_wt[ruid, :]
# sess.run(tf.assign(tune_var, rand_emb))
sess.run(tf.assign(tune_var, np_common_var))
np_tuned_acc = sess.run(tune_acc, feed_dict=fd)
_t4 += np_tuned_acc
_i4 += 1
print("Base Test acc: %0.4f" % (base))
print("Common Test acc: %0.4f" % (np_tuned_acc))
print("Best Test acc: %0.4f -- wt: %s" % (max_test, best_wt))
# sess.run(tune_var.initializer)
for _it in range(FLAGS.iters):
np_probs, np_reprs = sess.run([probs_for_tune, tf_reprs],
feed_dict=fd)
if True:
# _mxprobs = np.max(np_probs, axis=1)
# c_idxs = np.argpartition(-_mxprobs, kth=FLAGS.nseeds)[:FLAGS.nseeds]
c_idxs = select_centers(np_probs, np_reprs, FLAGS.nseeds)
all_input = fd[fingerprint_input]
all_labels = np_probs
new_inp_pl, new_label_pl = [], []
for ci in c_idxs:
new_inp_pl.append(all_input[ci])
_pred = np.argmax(all_labels[ci])
z = np.zeros([label_count], np.int32)
z[_pred] = 1
new_label_pl.append(z)
select_insts = {}
select_insts[fingerprint_input] = new_inp_pl
select_insts[ground_truth_label] = new_label_pl
for _ in range(FLAGS.inneriters):
_, np_l, np_wts, np_wts2 = sess.run(
[tune_op, loss_for_tune, c_wts, tune_var],
feed_dict=select_insts)
else:
new_inp_pl, new_label_pl = get_centering_data(
np_probs, np_reprs)
select_insts = {}
select_insts[ph_reprs] = new_inp_pl
select_insts[ground_truth_label] = new_label_pl
for _ in range(FLAGS.inneriters):
_, np_l, np_wts, np_wts2 = sess.run(
[tune_op2, loss_for_tune2, c_wts, tune_var],
feed_dict=select_insts)
if _it % 50 == 0:
print("Loss: %f wts: %s %s" % (np_l, np_wts, np_wts2))
np_tuned_acc, np_preds = sess.run(
[tune_acc,
tf.one_hot(predicted_tuned_indices, label_count)],
feed_dict=fd)
_t2 += np_tuned_acc
_i2 += 1
print("Tuned acc: %f dist: %s" %
(100 * np_tuned_acc, np.sum(np_preds, axis=0)))
#print (conf_matrix)
print("Defau Avg test accuracy: %f over %d domains" %
((_t3 / _i3), _i3))
print("Brute Avg test accuracy: %f over %d domains" %
((_t1 / _i1), _i1))
print("Tuned Avg test accuracy: %f over %d domains" %
((_t2 / _i2), _i2))
print("Common Avg test accuracy: %f over %d domains" %
((_t4 / _i4), _i4))
fields = [
"%0.2f" % (100 * _)
for _ in [(_t3 / _i3), (_t1 / _i1), (_t2 / _i2), (_t4 / _i4)]
]
wf.write("|" + "|".join(fields))
agg_acc /= _i1
for pi in range(0, 110, 10):
print(pi, np.percentile(agg_acc, pi))
wf.write("|\n")
wf.close()
def eval_common(_):
# We want to see all the logging messages for this tutorial.
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new TensorFlow session.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession()
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
audio_processor = input_data.AudioProcessor(
FLAGS.data_url, FLAGS.data_dir,
FLAGS.silence_percentage, FLAGS.unknown_percentage,
FLAGS.wanted_words.split(','), FLAGS.training_percentage,
FLAGS.validation_percentage, FLAGS.testing_percentage, model_settings)
# val_fname, test_fname = 'extra-data/all_paths.txt', 'extra-data/all_paths.txt'
# audio_processor = input_data.make_processor(val_fname, test_fname, FLAGS.wanted_words.split(','), FLAGS.data_dir, model_settings)
# audio_processor.num_uids = FLAGS.training_percentage + 2
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
uid_count = audio_processor.num_uids
print("Label count: %d uid count: %d" % (label_count, uid_count))
#sys.exit()
fingerprint_input = tf.placeholder(tf.float32, [None, fingerprint_size],
name='fingerprint_input')
net = lambda scope: lambda inp: models.create_model(inp,
model_settings,
FLAGS.
model_architecture,
is_training=True,
scope=scope)
# Define loss and optimizer
ground_truth_label = tf.placeholder(tf.float32, [None, label_count],
name='groundtruth_input')
ground_truth_style = tf.placeholder(tf.float32, [None, uid_count],
name='groundtruth_style')
ph_reprs = tf.placeholder(tf.float32, [None, 128],
name='representations_input')
with tf.variable_scope('crossgrad'):
label_net = net('label')
label_embedding = label_net(fingerprint_input)
with tf.variable_scope(''):
cross_entropy, _, label_logits = losses.mos(label_embedding,
ground_truth_style,
ground_truth_label,
label_count, uid_count,
fingerprint_input, FLAGS)
predicted_indices = tf.argmax(label_logits, 1)
expected_indices = tf.argmax(ground_truth_label, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
global_step = tf.contrib.framework.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
common_var = [
var for var in tf.all_variables() if var.name.find('common_var') >= 0
][0]
lvars = [
var for var in tf.global_variables() if var.name.find("tune_var") < 0
]
saver = tf.train.Saver(lvars)
tf.global_variables_initializer().run()
sys.stdout.flush()
if FLAGS.model_dir:
saver.restore(sess, FLAGS.model_dir)
start_step = global_step.eval(session=sess)
np_common_var = sess.run(common_var)
set_size = audio_processor.set_size('training')
tf.logging.info('set_size=%d', set_size)
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
wfname = "results.org"
wf = open(wfname, "a")
# wf.write(FLAGS.model_dir + "\n")
params = "|".join([str(_) for _ in ["mos"]])
# wf.write("|" + params)
for split in ['validation', 'testing']:
_t, _i = 0, 0
# agg_acc = np.zeros(uid_count)
num_correct, total = 0, 0
test_fingerprints, test_ground_truth, _ = audio_processor.get_data(
-1, 0, model_settings, 0.0, 0.0, 0, split, sess)
np_acc = sess.run(evaluation_step,
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth
})
print("Accuracy: %f" % np_acc)
def main(_):
# We want to see all the logging messages for this tutorial.
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new TensorFlow session.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.4)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession()
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# Begin by making sure we have the training data we need. If you already have
# training data of your own, use `--data_url= ` on the command line to avoid
# downloading.
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
audio_processor = input_data.AudioProcessor(
FLAGS.data_url, FLAGS.data_dir,
FLAGS.silence_percentage, FLAGS.unknown_percentage,
FLAGS.wanted_words.split(','), FLAGS.training_percentage,
FLAGS.validation_percentage, FLAGS.testing_percentage, model_settings)
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
# uid_count = audio_processor.num_uids
uid_count = audio_processor.num_uids
print("Label count: %d uid count: %d" % (label_count, uid_count))
#sys.exit()
time_shift_samples = int((FLAGS.time_shift_ms * FLAGS.sample_rate) / 1000)
# Figure out the learning rates for each training phase. Since it's often
# effective to have high learning rates at the start of training, followed by
# lower levels towards the end, the number of steps and learning rates can be
# specified as comma-separated lists to define the rate at each stage. For
# example --how_many_training_steps=10000,3000 --learning_rate=0.001,0.0001
# will run 13,000 training loops in total, with a rate of 0.001 for the first
# 10,000, and 0.0001 for the final 3,000.
set_size = audio_processor.set_size('training')
tf.logging.info('Train set_size=%d', set_size)
lr = float(FLAGS.learning_rate)
FLAGS.how_many_training_steps = (FLAGS.how_many_epochs *
set_size) // FLAGS.batch_size
print("Running for %d training steps with lr: %f" %
(FLAGS.how_many_training_steps, lr))
# training_steps_list = list(map(int, FLAGS.how_many_training_steps.split(',')))
# learning_rates_list = list(map(float, FLAGS.learning_rate.split(',')))
training_steps_list = [FLAGS.how_many_training_steps, 1200]
learning_rates_list = [lr, 1e-4]
if len(training_steps_list) != len(learning_rates_list):
raise Exception(
'--how_many_training_steps and --learning_rate must be equal length '
'lists, but are %d and %d long instead' %
(len(training_steps_list), len(learning_rates_list)))
fingerprint_input = tf.placeholder(tf.float32, [None, fingerprint_size],
name='fingerprint_input')
net = lambda scope: lambda inp: models.create_model(inp,
model_settings,
FLAGS.
model_architecture,
is_training=True,
scope=scope)
# Define loss and optimizer
ground_truth_label = tf.placeholder(tf.float32, [None, label_count],
name='groundtruth_input')
ground_truth_style = tf.placeholder(tf.float32, [None, uid_count],
name='groundtruth_style')
# the supervised bunch
if FLAGS.model in [
"cg", "ucgv3", "ucgv6", "ucgv7", "ucgv8", "ucgv8_wosx", "ucgv11",
"ucgv12", "ucgv13"
]:
with tf.variable_scope('crossgrad'):
label_net_fn = net('label')
style_net_fn = net('style')
label_embedding = label_net_fn(fingerprint_input)
style_embedding = style_net_fn(fingerprint_input)
scales = {"epsilon": FLAGS.epsilon, "alpha": FLAGS.alpha}
if FLAGS.model == "cg":
cross_entropy, label_logits = losses.cg_losses(
label_embedding,
style_embedding,
ground_truth_style,
ground_truth_label,
uid_count,
label_count,
fingerprint_input,
scales,
label_net_fn=label_net_fn,
style_net_fn=style_net_fn)
elif FLAGS.model == "mos" or FLAGS.model == 'mos2' or FLAGS.model == "simple":
with tf.variable_scope('crossgrad'):
label_net = net('label')
label_embedding = label_net(fingerprint_input)
if FLAGS.model == "mos":
cross_entropy, _, label_logits = losses.mos(
label_embedding, ground_truth_style, ground_truth_label,
label_count, uid_count, fingerprint_input, FLAGS)
elif FLAGS.model == "mos2":
cross_entropy, _, label_logits = losses.mos2(
label_embedding, ground_truth_style, ground_truth_label,
label_count, uid_count, fingerprint_input, FLAGS)
elif FLAGS.model == "simple":
cross_entropy, label_logits = losses.simple(
label_embedding, ground_truth_style, ground_truth_label,
label_count, uid_count, fingerprint_input, FLAGS)
else:
raise NotImplementedError('Unknown model: %s' % FLAGS.model)
#cross_entropy = losses.dan_loss(label_embedding, ground_truth_style, uid_count, label_logits, ground_truth_label)
# Optionally we can add runtime checks to spot when NaNs or other symptoms of
# numerical errors start occurring during training.
control_dependencies = []
if FLAGS.check_nans:
checks = tf.add_check_numerics_ops()
control_dependencies = [checks]
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES):
tf.summary.scalar('losses/%s' % loss.op.name, loss)
total_loss = tf.losses.get_total_loss()
tf.summary.scalar('total_loss', total_loss)
# Create the back propagation and training evaluation machinery in the graph.
with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
learning_rate_input = tf.placeholder(tf.float32, [],
name='learning_rate_input')
train_step = tf.train.MomentumOptimizer(
learning_rate_input, momentum=0.9,
use_nesterov=True).minimize(total_loss)
# train_step = tf.train.AdamOptimizer(learning_rate_input).minimize(total_loss)
# train_step = tf.train.AdadeltaOptimizer(learning_rate_input).minimize(total_loss)
emb_mat = [
var for var in tf.trainable_variables()
if var.name.find('emb_mat') >= 0
]
predicted_indices = tf.argmax(label_logits, 1)
expected_indices = tf.argmax(ground_truth_label, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', evaluation_step)
global_step = tf.contrib.framework.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
saver = tf.train.Saver(tf.global_variables())
# Merge all the summaries and write them out to /tmp/retrain_logs (by default)
merged_summaries = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
sess.graph)
validation_writer = tf.summary.FileWriter(FLAGS.summaries_dir +
'/validation')
tf.global_variables_initializer().run()
print(tf.trainable_variables())
sys.stdout.flush()
start_step = 1
if FLAGS.start_checkpoint:
models.load_variables_from_checkpoint(sess, FLAGS.start_checkpoint)
start_step = global_step.eval(session=sess)
tf.logging.info('Training from step: %d ', start_step)
# Save graph.pbtxt.
tf.train.write_graph(sess.graph_def, FLAGS.train_dir,
FLAGS.model_architecture + '.pbtxt')
# Save list of words.
with gfile.GFile(
os.path.join(FLAGS.train_dir,
FLAGS.model_architecture + '_labels.txt'), 'w') as f:
f.write('\n'.join(audio_processor.words_list))
# Training loop.
training_steps_max = np.sum(training_steps_list)
sess.graph.finalize()
best_ind_val_acc, total_accuracy = -1, -1
for training_step in xrange(start_step, training_steps_max + 1):
# Figure out what the current learning rate is.
training_steps_sum = 0
for i in range(len(training_steps_list)):
training_steps_sum += training_steps_list[i]
if training_step <= training_steps_sum:
learning_rate_value = learning_rates_list[i]
break
# Pull the audio samples we'll use for training.
train_fingerprints, train_ground_truth, train_uids = audio_processor.get_data(
FLAGS.batch_size, 0, model_settings, FLAGS.background_frequency,
FLAGS.background_volume, time_shift_samples, 'training', sess)
# Run the graph with this batch of training data.
train_summary, train_accuracy, cross_entropy_value, _, _, _ = sess.run(
[
merged_summaries, evaluation_step, cross_entropy, total_loss,
train_step, increment_global_step
],
feed_dict={
fingerprint_input: train_fingerprints,
ground_truth_label: train_ground_truth,
ground_truth_style: train_uids,
learning_rate_input: learning_rate_value
})
train_writer.add_summary(train_summary, training_step)
tf.logging.info(
'Step #%d: rate %f, accuracy %.1f%%, cross entropy %f' %
(training_step, learning_rate_value, train_accuracy * 100,
cross_entropy_value))
is_last_step = (training_step == training_steps_max)
if (training_step % FLAGS.eval_step_interval) == 0 or is_last_step:
set_size = audio_processor.set_size('ind-validation')
total_accuracy = 0
for i in xrange(0, set_size, FLAGS.batch_size):
validation_fingerprints, validation_ground_truth, validation_uids = (
audio_processor.get_data(FLAGS.batch_size, i,
model_settings, 0.0, 0.0, 0,
'ind-validation', sess))
# Run a validation step and capture training summaries for TensorBoard
# with the `merged` op.
validation_summary, validation_accuracy, conf_matrix = sess.run(
[merged_summaries, evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: validation_fingerprints,
ground_truth_label: validation_ground_truth,
ground_truth_style: validation_uids,
})
validation_writer.add_summary(validation_summary,
training_step)
batch_size = min(FLAGS.batch_size, set_size - i)
total_accuracy += (validation_accuracy * batch_size) / set_size
tf.logging.info('Step %d: Validation accuracy = %.1f%% (N=%d)' %
(training_step, total_accuracy * 100, set_size))
# Save the model checkpoint periodically.
# if (training_step % FLAGS.save_step_interval == 0 or
# training_step == training_steps_max):
if total_accuracy > best_ind_val_acc:
best_ind_val_acc = total_accuracy
checkpoint_path = os.path.join(FLAGS.train_dir,
FLAGS.model_architecture + '.ckpt')
tf.logging.info('Saving to "%s-%d"', checkpoint_path,
training_step)
saver.save(sess, checkpoint_path, global_step=training_step)
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
total_accuracy = 0
for i in xrange(0, set_size, FLAGS.batch_size):
test_fingerprints, test_ground_truth, test_uids = audio_processor.get_data(
FLAGS.batch_size, i, model_settings, 0.0, 0.0, 0, 'testing', sess)
test_accuracy, conf_matrix = sess.run(
[evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_label: test_ground_truth,
ground_truth_style: test_uids,
})
batch_size = min(FLAGS.batch_size, set_size - i)
total_accuracy += (test_accuracy * batch_size) / set_size
tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
(total_accuracy * 100, set_size))