code stringlengths 3 6.57k |
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tf.keras.backend.set_session(sess) |
input_data.AudioProcessor(flags) |
int((flags.time_shift_ms * flags.sample_rate) |
list(map(int, flags.how_many_training_steps.split(',') |
list(map(float, flags.learning_rate.split(',') |
len(training_steps_list) |
len(learning_rates_list) |
len(training_steps_list) |
len(learning_rates_list) |
logging.info(flags) |
open(flags.distill_teacher_json, 'r') |
flags.__dict__.items() |
tf.keras.layers.Lambda(lambda logits: tf.one_hot(tf.math.argmax(logits, axis=-1) |
tf.keras.models.Sequential([teacher_base, hard_labels]) |
logging.info(model.summary() |
utils.save_model_summary(model, flags.train_dir) |
open(os.path.join(flags.train_dir, 'flags.txt') |
pprint.pprint(flags, stream=f) |
tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=flags.label_smoothing) |
tf.keras.optimizers.Adam(epsilon=flags.optimizer_epsilon) |
tf.keras.optimizers.SGD(momentum=0.9) |
bool(flags.novograd_grad_averaging) |
AdamWeightDecay(learning_rate=0.05, weight_decay_rate=flags.l2_weight_decay, exclude_from_weight_decay=exclude) |
ValueError('Unsupported optimizer:%s' % flags.optimizer) |
model.compile(optimizer=optimizer, loss=loss, loss_weights=loss_weights, metrics=metrics) |
tf.summary.FileWriter(flags.summaries_dir + '/validation') |
sess.run(tf.global_variables_initializer() |
model.load_weights(flags.start_checkpoint) |
expect_partial() |
logging.info('Weights loaded from %s', flags.start_checkpoint) |
teacher_base.load_weights(teacher_flags.start_checkpoint) |
assert_existing_objects_matched() |
logging.info('Distillation teacher weights loaded from %s', teacher_flags.start_checkpoint) |
logging.info('Training from step: %d ', start_step) |
tf.train.write_graph(sess.graph_def, flags.train_dir, 'graph.pbtxt') |
tf.io.gfile.GFile(os.path.join(flags.train_dir, 'labels.txt') |
f.write('\n'.join(audio_processor.words_list) |
np.sum(training_steps_list) |
np.log(learning_rates_list[-1] / lr_init) |
audio_processor.set_size(mode) |
int((num_train / flags.batch_size) |
range(start_step, training_steps_max + 1) |
np.exp(-exp_rate * training_step) |
range(len(training_steps_list) |
min(1, float(training_step) |
max(1, warmup_steps) |
math.cos(math.pi * training_step / training_steps_max) |
ValueError('Wrong lr_schedule: %s' % flags.lr_schedule) |
tf.keras.backend.set_value(model.optimizer.learning_rate, learning_rate_value) |
tf.keras.utils.to_categorical(train_ground_truth, num_classes=flags.label_count) |
teacher.predict_on_batch(train_fingerprints) |
model.train_on_batch(train_fingerprints, one_hot_labels) |
astype(dtype=int) |
sum() |
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) |
tf.Summary.Value(tag='accuracy', simple_value=acc_label) |
train_writer.add_summary(summary, training_step) |
if (training_step % flags.eval_step_interval) |
audio_processor.set_size('validation') |
int(set_size / flags.batch_size) |
range(0, set_size, flags.batch_size) |
tf.keras.utils.to_categorical(validation_ground_truth, num_classes=flags.label_count) |
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) |
tf.Summary.Value(tag='accuracy', simple_value=acc_label) |
validation_writer.add_summary(summary, training_step) |
logging.info('Step %d: Validation accuracy = %.2f%% (N=%d) |
model.save_weights(flags.train_dir + 'best_weights') |
tf.keras.backend.set_learning_phase(0) |
audio_processor.set_size('testing') |
int(set_size / flags.batch_size) |
logging.info('set_size=%d', set_size) |
range(0, set_size, flags.batch_size) |
tf.keras.utils.to_categorical(test_ground_truth, num_classes=flags.label_count) |
model.test_on_batch(test_fingerprints, one_hot_labels) |
logging.info('Final test accuracy = %.2f%% (N=%d) |
open(os.path.join(flags.train_dir, 'accuracy_last.txt') |
fd.write(str(total_accuracy * 100) |
model.save_weights(flags.train_dir + 'last_weights') |
model_flags.update_flags(None) |
train(flags) |
Copyright (c) |
HTTPBasicsTest (BitcoinTestFramework) |
set_test_params(self) |
setup_chain(self) |
super() |
setup_chain() |
open(os.path.join(self.options.tmpdir+"/node0", "gauntlet.conf") |
f.write(rpcauth+"\n") |
f.write(rpcauth2+"\n") |
open(os.path.join(self.options.tmpdir+"/node1", "gauntlet.conf") |
f.write(rpcuser+"\n") |
f.write(rpcpassword+"\n") |
run_test(self) |
urllib.parse.urlparse(self.nodes[0].url) |
str_to_b64str(authpair) |
http.client.HTTPConnection(url.hostname, url.port) |
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