def tf_stream_state_internal_model_accuracy(
flags,
folder,
weights_name='best_weights',
accuracy_name='tf_stream_state_internal_model_accuracy_sub_set.txt',
max_test_samples=1000):
"""Compute accuracy of streamable model with internal state using TF.
Testign model with batch size 1 can be slow, so accuracy is evaluated
on subset of data with size max_test_samples
Args:
flags: model and data settings
folder: folder name where accuracy report will be stored
weights_name: file name with model weights
accuracy_name: file name for storing accuracy in path + accuracy_name
max_test_samples: max number of test samples. In this mode model is slow
with TF because of batch size 1, so accuracy is computed on subset of
testing data
Returns:
accuracy
"""
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
logging.info('tf stream model state internal without state resetting'
'between testing sequences')
audio_processor = input_data.AudioProcessor(flags)
set_size = audio_processor.set_size('testing')
set_size = np.minimum(max_test_samples, set_size)
inference_batch_size = 1
tf.keras.backend.set_learning_phase(0)
flags.batch_size = inference_batch_size # set batch size
model = models.MODELS[flags.model_name](flags)
weights_path = os.path.join(flags.train_dir, weights_name)
model.load_weights(weights_path).expect_partial()
model_stream = utils.to_streaming_inference(
model, flags, modes.Modes.STREAM_INTERNAL_STATE_INFERENCE)
total_accuracy = 0.0
count = 0.0
for i in range(0, set_size, inference_batch_size):
test_fingerprints, test_ground_truth = audio_processor.get_data(
inference_batch_size, i, flags, 0.0, 0.0, 0, 'testing', 0.0, 0.0,
sess)
if flags.preprocess == 'raw':
stream_output_prediction = run_stream_inference_classification(
flags, model_stream, test_fingerprints)
stream_output_arg = np.argmax(stream_output_prediction)
else:
# iterate over frames
for t in range(test_fingerprints.shape[1]):
# get new frame from stream of data
stream_update = test_fingerprints[:, t, :]
# [batch, time=1, feature]
stream_update = np.expand_dims(stream_update, axis=1)
# classification result of a current frame
stream_output_prediction = model_stream.predict(stream_update)
stream_output_arg = np.argmax(stream_output_prediction)
total_accuracy = total_accuracy + (test_ground_truth[0]
== stream_output_arg)
count = count + 1
if i % 200 == 0 and i:
logging.info(
'tf test accuracy, stream model state internal = %.2f%% %d out of %d',
*(total_accuracy * 100 / count, i, set_size))
total_accuracy = total_accuracy / count
logging.info(
'TF Final test accuracy of stream model state internal = %.2f%% (N=%d)',
*(total_accuracy * 100, set_size))
path = os.path.join(flags.train_dir, folder)
if not os.path.exists(path):
os.makedirs(path)
fname_summary = 'model_summary_stream_state_internal'
utils.save_model_summary(model_stream,
path,
file_name=fname_summary + '.txt')
tf.keras.utils.plot_model(model_stream,
to_file=os.path.join(path,
fname_summary + '.png'),
show_shapes=True,
expand_nested=True)
with open(os.path.join(path, accuracy_name), 'wt') as fd:
fd.write('%f on set_size %d' % (total_accuracy * 100, set_size))
return total_accuracy * 100
def tf_stream_state_external_model_accuracy(
flags,
folder,
weights_name='best_weights',
accuracy_name='stream_state_external_model_accuracy_sub_set.txt',
reset_state=False,
max_test_samples=1000):
"""Compute accuracy of streamable model with external state using TF.
Args:
flags: model and data settings
folder: folder name where accuracy report will be stored
weights_name: file name with model weights
accuracy_name: file name for storing accuracy in path + accuracy_name
reset_state: reset state between testing sequences.
If True - then it is non streaming testing environment: state will be
reseted on every test and will not be transferred to another one (as
it is done in real streaming).
max_test_samples: max number of test samples. In this mode model is slow
with TF because of batch size 1, so accuracy is computed on subset of
testing data
Returns:
accuracy
"""
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
audio_processor = input_data.AudioProcessor(flags)
set_size = audio_processor.set_size('testing')
set_size = np.minimum(max_test_samples, set_size)
inference_batch_size = 1
tf.keras.backend.set_learning_phase(0)
flags.batch_size = inference_batch_size # set batch size
model = models.MODELS[flags.model_name](flags)
weights_path = os.path.join(flags.train_dir, weights_name)
model.load_weights(weights_path).expect_partial()
model_stream = utils.to_streaming_inference(
model, flags, modes.Modes.STREAM_EXTERNAL_STATE_INFERENCE)
logging.info('tf stream model state external with reset_state %d',
reset_state)
inputs = []
for s in range(len(model_stream.inputs)):
inputs.append(np.zeros(model_stream.inputs[s].shape, dtype=np.float32))
total_accuracy = 0.0
count = 0.0
inference_batch_size = 1
for i in range(0, set_size, inference_batch_size):
test_fingerprints, test_ground_truth = audio_processor.get_data(
inference_batch_size, i, flags, 0.0, 0.0, 0, 'testing', 0.0, 0.0,
sess)
if reset_state:
for s in range(len(model_stream.inputs)):
inputs[s] = np.zeros(model_stream.inputs[s].shape,
dtype=np.float32)
if flags.preprocess == 'raw':
start = 0
end = flags.window_stride_samples
# iterate over time samples with stride = window_stride_samples
while end <= test_fingerprints.shape[1]:
# get new frame from stream of data
stream_update = test_fingerprints[:, start:end]
# update indexes of streamed updates
start = end
end = start + flags.window_stride_samples
# set input audio data (by default input data at index 0)
inputs[0] = stream_update
# run inference
outputs = model_stream.predict(inputs)
# get output states and set it back to input states
# which will be fed in the next inference cycle
for s in range(1, len(model_stream.inputs)):
inputs[s] = outputs[s]
stream_output_arg = np.argmax(outputs[0])
else:
# iterate over frames
for t in range(test_fingerprints.shape[1]):
# get new frame from stream of data
stream_update = test_fingerprints[:, t, :]
# [batch, time=1, feature]
stream_update = np.expand_dims(stream_update, axis=1)
# set input audio data (by default input data at index 0)
inputs[0] = stream_update
# run inference
outputs = model_stream.predict(inputs)
# get output states and set it back to input states
# which will be fed in the next inference cycle
for s in range(1, len(model_stream.inputs)):
inputs[s] = outputs[s]
stream_output_arg = np.argmax(outputs[0])
total_accuracy = total_accuracy + (test_ground_truth[0]
== stream_output_arg)
count = count + 1
if i % 200 == 0 and i:
logging.info(
'tf test accuracy, stream model state external = %.2f%% %d out of %d',
*(total_accuracy * 100 / count, i, set_size))
total_accuracy = total_accuracy / count
logging.info(
'TF Final test accuracy of stream model state external = %.2f%% (N=%d)',
*(total_accuracy * 100, set_size))
path = os.path.join(flags.train_dir, folder)
if not os.path.exists(path):
os.makedirs(path)
fname_summary = 'model_summary_stream_state_external'
utils.save_model_summary(model_stream,
path,
file_name=fname_summary + '.txt')
tf.keras.utils.plot_model(model_stream,
to_file=os.path.join(path,
fname_summary + '.png'),
show_shapes=True,
expand_nested=True)
with open(os.path.join(path, accuracy_name), 'wt') as fd:
fd.write('%f on set_size %d' % (total_accuracy * 100, set_size))
return total_accuracy * 100
def tf_non_stream_model_accuracy(
flags,
folder,
time_shift_samples=0,
weights_name='best_weights',
accuracy_name='tf_non_stream_model_accuracy.txt'):
"""Compute accuracy of non streamable model using TF.
Args:
flags: model and data settings
folder: folder name where accuracy report will be stored
time_shift_samples: time shift of audio data it will be applied in range:
-time_shift_samples...time_shift_samples
We can use non stream model for processing stream of audio.
By default it will be slow, so to speed it up
we can use non stream model on sampled audio data:
for example instead of computing non stream model
on every 20ms, we can run it on every 200ms of audio stream.
It will reduce total latency by 10 times.
To emulate sampling effect we use time_shift_samples.
weights_name: file name with model weights
accuracy_name: file name for storing accuracy in path + accuracy_name
Returns:
accuracy
"""
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
audio_processor = input_data.AudioProcessor(flags)
set_size = audio_processor.set_size('testing')
tf.keras.backend.set_learning_phase(0)
flags.batch_size = 100 # set batch size for inference
set_size = int(set_size / flags.batch_size) * flags.batch_size
model = models.MODELS[flags.model_name](flags)
weights_path = os.path.join(flags.train_dir, weights_name)
model.load_weights(weights_path).expect_partial()
total_accuracy = 0.0
count = 0.0
for i in range(0, set_size, flags.batch_size):
test_fingerprints, test_ground_truth = audio_processor.get_data(
flags.batch_size, i, flags, 0.0, 0.0, time_shift_samples,
'testing', 0.0, 0.0, sess)
predictions = model.predict(test_fingerprints)
predicted_labels = np.argmax(predictions, axis=1)
total_accuracy = total_accuracy + np.sum(
predicted_labels == test_ground_truth)
count = count + len(test_ground_truth)
total_accuracy = total_accuracy / count
logging.info('TF Final test accuracy on non stream model = %.2f%% (N=%d)',
*(total_accuracy * 100, set_size))
path = os.path.join(flags.train_dir, folder)
if not os.path.exists(path):
os.makedirs(path)
fname_summary = 'model_summary_non_stream'
utils.save_model_summary(model, path, file_name=fname_summary + '.txt')
tf.keras.utils.plot_model(model,
to_file=os.path.join(path,
fname_summary + '.png'),
show_shapes=True,
expand_nested=True)
with open(os.path.join(path, accuracy_name), 'wt') as fd:
fd.write('%f on set_size %d' % (total_accuracy * 100, set_size))
return total_accuracy * 100
def train(flags):
"""Model training."""
flags.training = True
# Set the verbosity based on flags (default is INFO, so we see all messages)
logging.set_verbosity(flags.verbosity)
# Start a new TensorFlow session.
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
audio_processor = input_data.AudioProcessor(flags)
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.
training_steps_list = list(
map(int, flags.how_many_training_steps.split(',')))
learning_rates_list = list(map(float, flags.learning_rate.split(',')))
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)))
logging.info(flags)
model = models.MODELS[flags.model_name](flags)
logging.info(model.summary())
# save model summary
utils.save_model_summary(model, flags.train_dir)
# save model and data flags
with open(os.path.join(flags.train_dir, 'flags.txt'), 'wt') as f:
pprint.pprint(flags, stream=f)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(epsilon=flags.optimizer_epsilon)
if flags.optimizer == 'adam':
optimizer = tf.keras.optimizers.Adam(epsilon=flags.optimizer_epsilon)
elif flags.optimizer == 'momentum':
optimizer = tf.keras.optimizers.SGD(momentum=0.9)
elif flags.optimizer == 'novograd':
optimizer = tfa.optimizers.NovoGrad(
lr=0.05,
beta_1=flags.novograd_beta_1,
beta_2=flags.novograd_beta_2,
weight_decay=flags.novograd_weight_decay,
grad_averaging=bool(flags.novograd_grad_averaging))
else:
raise ValueError('Unsupported optimizer:%s' % flags.optimizer)
model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
train_writer = tf.summary.FileWriter(flags.summaries_dir + '/train',
sess.graph)
validation_writer = tf.summary.FileWriter(flags.summaries_dir +
'/validation')
sess.run(tf.global_variables_initializer())
start_step = 1
logging.info('Training from step: %d ', start_step)
# Save graph.pbtxt.
tf.train.write_graph(sess.graph_def, flags.train_dir, 'graph.pbtxt')
# Save list of words.
with tf.io.gfile.GFile(os.path.join(flags.train_dir, 'labels.txt'),
'w') as f:
f.write('\n'.join(audio_processor.words_list))
best_accuracy = 0.0
# prepare parameters for exp learning rate decay
training_steps_max = np.sum(training_steps_list)
lr_init = learning_rates_list[0]
exp_rate = -np.log(learning_rates_list[-1] / lr_init) / training_steps_max
# Training loop.
for training_step in range(start_step, training_steps_max + 1):
offset = (training_step -
1) * flags.batch_size if flags.pick_deterministically else 0
# Pull the audio samples we'll use for training.
train_fingerprints, train_ground_truth = audio_processor.get_data(
flags.batch_size, offset, flags, flags.background_frequency,
flags.background_volume, time_shift_samples, 'training',
flags.resample, flags.volume_resample, sess)
if flags.lr_schedule == 'exp':
learning_rate_value = lr_init * np.exp(-exp_rate * training_step)
elif flags.lr_schedule == 'linear':
# 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
else:
raise ValueError('Wrong lr_schedule: %s' % flags.lr_schedule)
tf.keras.backend.set_value(model.optimizer.lr, learning_rate_value)
result = model.train_on_batch(train_fingerprints, train_ground_truth)
summary = tf.Summary(value=[
tf.Summary.Value(tag='accuracy', simple_value=result[1]),
])
train_writer.add_summary(summary, training_step)
logging.info(
'Step #%d: rate %f, accuracy %.2f%%, cross entropy %f',
*(training_step, learning_rate_value, result[1] * 100, result[0]))
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('validation')
set_size = int(set_size / flags.batch_size) * flags.batch_size
total_accuracy = 0.0
count = 0.0
for i in range(0, set_size, flags.batch_size):
validation_fingerprints, validation_ground_truth = (
audio_processor.get_data(flags.batch_size, i, flags, 0.0,
0.0, 0, 'validation', 0.0, 0.0,
sess))
# Run a validation step and capture training summaries for TensorBoard
# with the `merged` op.
result = model.test_on_batch(validation_fingerprints,
validation_ground_truth)
summary = tf.Summary(value=[
tf.Summary.Value(tag='accuracy', simple_value=result[1]),
])
validation_writer.add_summary(summary, training_step)
total_accuracy += result[1]
count = count + 1.0
total_accuracy = total_accuracy / count
logging.info('Step %d: Validation accuracy = %.2f%% (N=%d)',
*(training_step, total_accuracy * 100, set_size))
model.save_weights(flags.train_dir + 'train/' +
str(int(best_accuracy * 10000)) + 'weights_' +
str(training_step))
# Save the model checkpoint when validation accuracy improves
if total_accuracy >= best_accuracy:
best_accuracy = total_accuracy
# overwrite the best model weights
model.save_weights(flags.train_dir + 'best_weights')
logging.info('So far the best validation accuracy is %.2f%%',
(best_accuracy * 100))
tf.keras.backend.set_learning_phase(0)
set_size = audio_processor.set_size('testing')
set_size = int(set_size / flags.batch_size) * flags.batch_size
logging.info('set_size=%d', set_size)
total_accuracy = 0.0
count = 0.0
for i in range(0, set_size, flags.batch_size):
test_fingerprints, test_ground_truth = audio_processor.get_data(
flags.batch_size, i, flags, 0.0, 0.0, 0, 'testing', 0.0, 0.0, sess)
result = model.test_on_batch(test_fingerprints, test_ground_truth)
total_accuracy += result[1]
count = count + 1.0
total_accuracy = total_accuracy / count
logging.info('Final test accuracy = %.2f%% (N=%d)',
*(total_accuracy * 100, set_size))
with open(os.path.join(flags.train_dir, 'accuracy_last.txt'), 'wt') as fd:
fd.write(str(total_accuracy * 100))
model.save_weights(flags.train_dir + 'last_weights')
# %%
audio_processor = input_data.AudioProcessor(data_settings)
# %%
model = keyword_marvin_v3_vl_0_3(input_shape=(16000), data_settings=data_settings)
logging.info(model.summary())
if not os.path.exists(training_settings.train_dir):
os.mkdir(training_settings.train_dir)
if not os.path.exists(os.path.join(training_settings.train_dir, model.name)):
os.mkdir(os.path.join(training_settings.train_dir, model.name))
# save model summary
utils.save_model_summary(model, training_settings.train_dir + model.name)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(epsilon=training_settings.optimizer_epsilon)
model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
# %%
if not os.path.exists(training_settings.summaries_dir):
os.mkdir(training_settings.summaries_dir)
if not os.path.exists(os.path.join(training_settings.summaries_dir, model.name)):
os.mkdir(os.path.join(training_settings.summaries_dir, model.name))
train_writer = tf.summary.FileWriter(training_settings.summaries_dir + model.name + '/train', sess.graph)
model_json = model.to_json()
with open(os.path.join(path_model, "model.json"), "w") as json_file:
json_file.write(model_json)
# In[8]:
model.summary()
if not os.path.exists(train_dir):
os.mkdir(train_dir)
if not os.path.exists(os.path.join(train_dir, model.name)):
os.mkdir(os.path.join(train_dir, model.name))
# save model.summary()
utils.save_model_summary(model, train_dir + '/' + model.name)
# In[ ]:
save_best_weights = tf.keras.callbacks.ModelCheckpoint(
train_dir + '/' + model.name + '/best_weights',
monitor='val_loss',
mode='min',
verbose=0,
save_best_only=True,
save_weights_only=True)
logdir = "logs/scalars/" + model.name + "/" + datetime.now().strftime(
"%Y%m%d-%H%M%S")
# callbacks functions to use Tensorboard
def train(flags):
"""Model training."""
flags.training = True
# Set the verbosity based on flags (default is INFO, so we see all messages)
logging.set_verbosity(flags.verbosity)
# Start a new TensorFlow session.
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
audio_processor = input_data.AudioProcessor(flags)
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.
training_steps_list = list(map(int, flags.how_many_training_steps.split(',')))
learning_rates_list = list(map(float, flags.learning_rate.split(',')))
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)))
logging.info(flags)
model = models.MODELS[flags.model_name](flags)
if flags.distill_teacher_json:
with open(flags.distill_teacher_json, 'r') as f:
teacher_flags = json.load(f, object_hook=lambda d: SimpleNamespace(
**{ k: v for k, v in flags.__dict__.items() if not k in d },
**d))
teacher_base = models.MODELS[teacher_flags.model_name](teacher_flags)
hard_labels = tf.keras.layers.Lambda(lambda logits: tf.one_hot(tf.math.argmax(logits, axis=-1), depth=flags.label_count))
teacher = tf.keras.models.Sequential([teacher_base, hard_labels])
teacher_base.trainable = False
teacher.trainable = False
else:
teacher = None
teacher_flags = None
base_model = model
logging.info(model.summary())
# save model summary
utils.save_model_summary(model, flags.train_dir)
# save model and data flags
with open(os.path.join(flags.train_dir, 'flags.txt'), 'wt') as f:
pprint.pprint(flags, stream=f)
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=flags.label_smoothing)
metrics = ['accuracy']
if flags.optimizer == 'adam':
optimizer = tf.keras.optimizers.Adam(epsilon=flags.optimizer_epsilon)
elif flags.optimizer == 'momentum':
optimizer = tf.keras.optimizers.SGD(momentum=0.9)
elif flags.optimizer == 'novograd':
optimizer = tfa.optimizers.NovoGrad(
lr=0.05,
beta_1=flags.novograd_beta_1,
beta_2=flags.novograd_beta_2,
weight_decay=flags.novograd_weight_decay,
grad_averaging=bool(flags.novograd_grad_averaging))
elif flags.optimizer == 'adamw':
# Exclude some layers for weight decay
exclude = ["pos_emb", "class_emb", "layer_normalization", "bias"]
optimizer = AdamWeightDecay(learning_rate=0.05, weight_decay_rate=flags.l2_weight_decay, exclude_from_weight_decay=exclude)
else:
raise ValueError('Unsupported optimizer:%s' % flags.optimizer)
loss_weights = [ 0.5, 0.5, 0.0 ] if teacher else [ 1. ] # equally weight losses form label and teacher, ignore ensemble output
model.compile(optimizer=optimizer, loss=loss, loss_weights=loss_weights, metrics=metrics)
train_writer = tf.summary.FileWriter(flags.summaries_dir + '/train',
sess.graph)
validation_writer = tf.summary.FileWriter(flags.summaries_dir + '/validation')
sess.run(tf.global_variables_initializer())
if flags.start_checkpoint:
model.load_weights(flags.start_checkpoint).expect_partial()
logging.info('Weights loaded from %s', flags.start_checkpoint)
if teacher_flags and teacher_flags.start_checkpoint:
# Load weights into teacher base as this is the actual model that was saved, teacher includes hard label head
teacher_base.load_weights(teacher_flags.start_checkpoint).assert_existing_objects_matched()
logging.info('Distillation teacher weights loaded from %s', teacher_flags.start_checkpoint)
start_step = 0
logging.info('Training from step: %d ', start_step)
# Save graph.pbtxt.
tf.train.write_graph(sess.graph_def, flags.train_dir, 'graph.pbtxt')
# Save list of words.
with tf.io.gfile.GFile(os.path.join(flags.train_dir, 'labels.txt'), 'w') as f:
f.write('\n'.join(audio_processor.words_list))
best_accuracy = 0.0
# prepare parameters for exp learning rate decay
training_steps_max = np.sum(training_steps_list)
lr_init = learning_rates_list[0]
exp_rate = -np.log(learning_rates_list[-1] / lr_init)/training_steps_max
mode = 'training'
if flags.lr_schedule == 'cosine':
# Currently, no restarts are performed, so it is just a cosine decay over the entire
# training process. I think this is how DeiT does it.
lr_init = lr_init * flags.batch_size / 512
num_train = audio_processor.set_size(mode)
warmup_steps = int((num_train / flags.batch_size) * flags.warmup_epochs)
first_decay_steps=training_steps_max
# Training loop.
for training_step in range(start_step, training_steps_max + 1):
if training_step > 0:
offset = (training_step -
1) * flags.batch_size if flags.pick_deterministically else 0
# Pull the audio samples we'll use for training.
train_fingerprints, train_ground_truth = audio_processor.get_data(
flags.batch_size, offset, flags, flags.background_frequency,
flags.background_volume, time_shift_samples, mode,
flags.resample, flags.volume_resample, sess)
if flags.lr_schedule == 'exp':
learning_rate_value = lr_init * np.exp(-exp_rate * training_step)
elif flags.lr_schedule == 'linear':
# 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
elif flags.lr_schedule == 'cosine':
learning_rate_value = lr_init * min(1, float(training_step) / max(1, warmup_steps)) * (math.cos(math.pi * training_step / training_steps_max) + 1) / 2.
else:
raise ValueError('Wrong lr_schedule: %s' % flags.lr_schedule)
tf.keras.backend.set_value(model.optimizer.learning_rate, learning_rate_value)
one_hot_labels = tf.keras.utils.to_categorical(train_ground_truth, num_classes=flags.label_count)
if teacher:
teacher_labels = teacher.predict_on_batch(train_fingerprints)
one_hot_labels = [ one_hot_labels, teacher_labels, one_hot_labels ] # third is for the ensemble output, gradient is unused
result = model.train_on_batch(train_fingerprints, one_hot_labels)
if teacher:
loss_total, loss_label, loss_teacher, loss_average, acc_label, acc_teacher, acc_ensemble = result
differences = (teacher_labels != one_hot_labels).astype(dtype=int).sum()
logging.info(
'Step #%d: rate %f, accuracy %.2f%%, cross entropy %f, teacher acc %.2f%% (%d diff), teacher cross entropy %f, ensemble acc %.2f%%',
*(training_step, learning_rate_value, acc_label * 100, loss_total, acc_teacher * 100, differences, loss_teacher, acc_ensemble * 100))
summary = tf.Summary(value=[
tf.Summary.Value(tag='accuracy', simple_value=acc_label),
tf.Summary.Value(tag='teacher_accuracy', simple_value=acc_teacher),
tf.Summary.Value(tag='ensemble_accuracy', simple_value=acc_ensemble),
])
else:
loss_label, acc_label = result
logging.info(
'Step #%d: rate %f, accuracy %.2f%%, cross entropy %f',
*(training_step, learning_rate_value, acc_label * 100, loss_label))
summary = tf.Summary(value=[
tf.Summary.Value(tag='accuracy', simple_value=acc_label),
])
train_writer.add_summary(summary, training_step)
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('validation')
set_size = int(set_size / flags.batch_size) * flags.batch_size
total_accuracy = 0.0
count = 0.0
for i in range(0, set_size, flags.batch_size):
validation_fingerprints, validation_ground_truth = audio_processor.get_data(
flags.batch_size, i, flags, 0.0,
0.0, 0, 'validation',
0.0, 0.0, sess)
one_hot_labels = tf.keras.utils.to_categorical(validation_ground_truth, num_classes=flags.label_count)
if teacher:
one_hot_labels = [ one_hot_labels, one_hot_labels, one_hot_labels ]
# Run a validation step and capture training summaries for TensorBoard
# with the `merged` op.
result = model.test_on_batch(validation_fingerprints,
one_hot_labels)
if teacher:
loss_total, loss_label, loss_teacher, loss_average, acc_label, acc_teacher, acc_ensemble = result
summary = tf.Summary(value=[
tf.Summary.Value(tag='accuracy', simple_value=acc_ensemble),
tf.Summary.Value(tag='label_head_accuracy', simple_value=acc_label),
tf.Summary.Value(tag='distill_head_accuracy', simple_value=acc_teacher),
])
accuracy = acc_ensemble
else:
loss_label, acc_label = result
summary = tf.Summary(value=[
tf.Summary.Value(tag='accuracy', simple_value=acc_label),])
accuracy = acc_label
validation_writer.add_summary(summary, training_step)
total_accuracy += accuracy
count = count + 1.0
total_accuracy = total_accuracy / count
logging.info('Step %d: Validation accuracy = %.2f%% (N=%d)',
*(training_step, total_accuracy * 100, set_size))
# Save the model checkpoint when validation accuracy improves
if total_accuracy >= best_accuracy:
best_accuracy = total_accuracy
# overwrite the best model weights
model.save_weights(flags.train_dir + 'best_weights')
logging.info('So far the best validation accuracy is %.2f%%',
(best_accuracy * 100))
tf.keras.backend.set_learning_phase(0)
set_size = audio_processor.set_size('testing')
set_size = int(set_size / flags.batch_size) * flags.batch_size
logging.info('set_size=%d', set_size)
total_accuracy = 0.0
count = 0.0
for i in range(0, set_size, flags.batch_size):
test_fingerprints, test_ground_truth = audio_processor.get_data(
flags.batch_size, i, flags, 0.0, 0.0, 0, 'testing', 0.0, 0.0, sess)
one_hot_labels = tf.keras.utils.to_categorical(test_ground_truth, num_classes=flags.label_count)
if teacher:
one_hot_labels = [ one_hot_labels, one_hot_labels, one_hot_labels ]
result = model.test_on_batch(test_fingerprints, one_hot_labels)
total_accuracy += result[-1] if teacher else result[1]
count = count + 1.0
total_accuracy = total_accuracy / count
logging.info('Final test accuracy = %.2f%% (N=%d)',
*(total_accuracy * 100, set_size))
with open(os.path.join(flags.train_dir, 'accuracy_last.txt'), 'wt') as fd:
fd.write(str(total_accuracy * 100))
model.save_weights(flags.train_dir + 'last_weights')