def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
python类GetListOfFeatureNamesAndSizes()的实例源码
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_dir is "":
raise ValueError("'output_dir' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.model_checkpoint_path, FLAGS.input_data_pattern,
FLAGS.output_dir, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_dir is "":
raise ValueError("'output_dir' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.model_checkpoint_path, FLAGS.input_data_pattern,
FLAGS.output_dir, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.checkpoint_file, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.checkpoint_file, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.checkpoint_file, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def get_reader():
# Convert feature_names and feature_sizes to lists of values.
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(
num_classes = FLAGS.truncated_num_classes,
decode_zlib = FLAGS.decode_zlib,
feature_names=feature_names, feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(
num_classes = FLAGS.truncated_num_classes,
decode_zlib = FLAGS.decode_zlib,
feature_names=feature_names, feature_sizes=feature_sizes)
return reader
def get_reader():
# Convert feature_names and feature_sizes to lists of values.
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(
num_classes = FLAGS.truncated_num_classes,
feature_names=feature_names, feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(
num_classes = FLAGS.truncated_num_classes,
decode_zlib = FLAGS.decode_zlib,
feature_names=feature_names, feature_sizes=feature_sizes, feature_calcs=FLAGS.c_vars, feature_remove=FLAGS.r_vars)
return reader
def get_reader():
# Convert feature_names and feature_sizes to lists of values.
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(
feature_names=feature_names, feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(
feature_names=feature_names, feature_sizes=feature_sizes)
return reader
############################################################
#By Dalong????????????????????????
#???
#
############################################################
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
feature_sizes=feature_sizes)
if FLAGS.output_file is "":
raise ValueError("'output_file' was not specified. "
"Unable to continue with inference.")
if FLAGS.input_data_pattern is "":
raise ValueError("'input_data_pattern' was not specified. "
"Unable to continue with inference.")
inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)
def build_model(self):
"""Find the model and build the graph."""
# Convert feature_names and feature_sizes to lists of values.
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
if FLAGS.frame_only:
reader = readers.YT8MFrameFeatureOnlyReader(
feature_names=feature_names, feature_sizes=feature_sizes)
else:
reader = readers.YT8MFrameFeatureReader(
feature_names=feature_names, feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(
feature_names=feature_names, feature_sizes=feature_sizes)
# Find the model.
model = find_class_by_name(FLAGS.model,
[labels_embedding])()
label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses_embedding])()
optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])
build_graph(reader=reader,
model=model,
optimizer_class=optimizer_class,
clip_gradient_norm=FLAGS.clip_gradient_norm,
train_data_pattern=FLAGS.train_data_pattern,
label_loss_fn=label_loss_fn,
base_learning_rate=FLAGS.base_learning_rate,
learning_rate_decay=FLAGS.learning_rate_decay,
learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
regularization_penalty=FLAGS.regularization_penalty,
num_readers=FLAGS.num_readers,
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs)
logging.info("%s: Built graph.", task_as_string(self.task))
return tf.train.Saver(max_to_keep=2, keep_checkpoint_every_n_hours=0.25)
def lstmoutput(self, model_input, vocab_size, num_frames):
number_of_layers = FLAGS.lstm_layers
lstm_sizes = map(int, FLAGS.lstm_cells.split(","))
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
sub_inputs = [tf.nn.l2_normalize(x, dim=2) for x in tf.split(model_input, feature_sizes, axis = 2)]
assert len(lstm_sizes) == len(feature_sizes), \
"length of lstm_sizes (={}) != length of feature_sizes (={})".format( \
len(lstm_sizes), len(feature_sizes))
outputs = []
for i in xrange(len(feature_sizes)):
with tf.variable_scope("RNN%d" % i):
sub_input = sub_inputs[i]
lstm_size = lstm_sizes[i]
## Batch normalize the input
stacked_lstm = tf.contrib.rnn.MultiRNNCell(
[
tf.contrib.rnn.BasicLSTMCell(
lstm_size, forget_bias=1.0, state_is_tuple=True)
for _ in range(number_of_layers)
],
state_is_tuple=True)
output, state = tf.nn.dynamic_rnn(stacked_lstm, sub_input,
sequence_length=num_frames,
swap_memory=FLAGS.rnn_swap_memory,
dtype=tf.float32)
outputs.append(output)
# concat
final_output = tf.concat(outputs, axis=2)
return final_output
