def markdown_table(step):
# The text summary can also contain Markdown, including Markdown
# tables. Markdown tables look like this:
#
# | hello | there |
# |-------|-------|
# | this | is |
# | a | table |
#
# The leading and trailing pipes in each row are optional, and the text
# doesn't actually have to be neatly aligned, so we can create these
# pretty easily. Let's do so.
header_row = 'Pounds of chocolate | Happiness'
chocolate = tf.range(step)
happiness = tf.square(chocolate + 1)
chocolate_column = tf.as_string(chocolate)
happiness_column = tf.as_string(happiness)
table_rows = tf.string_join([chocolate_column, " | ", happiness_column])
table_body = tf.reduce_join(table_rows, separator='\n')
table = tf.string_join([header_row, "---|---", table_body], separator='\n')
preamble = 'We conducted an experiment and found the following data:\n\n'
result = tf.string_join([preamble, table])
tf.summary.text('chocolate_study', result)
python类reduce_join()的实例源码
def create_metric_ops(self, _inputs, labels, predictions):
"""Creates (value, update_op) tensors
"""
with tf.variable_scope(self._name):
# Join tokens into single strings
predictions_flat = tf.reduce_join(
predictions["predicted_tokens"], 1, separator=self._separator)
labels_flat = tf.reduce_join(
labels["target_tokens"], 1, separator=self._separator)
sources_value, sources_update = accumulate_strings(
values=predictions_flat, name="sources")
targets_value, targets_update = accumulate_strings(
values=labels_flat, name="targets")
metric_value = tf.py_func(
func=self._py_func,
inp=[sources_value, targets_value],
Tout=tf.float32,
name="value")
with tf.control_dependencies([sources_update, targets_update]):
update_op = tf.identity(metric_value, name="update_op")
return metric_value, update_op
def get_words_from_chars(characters_list: List[str], sequence_lengths: List[int], name='chars_conversion'):
with tf.name_scope(name=name):
def join_charcaters_fn(coords):
return tf.reduce_join(characters_list[coords[0]:coords[1]])
def coords_several_sequences():
end_coords = tf.cumsum(sequence_lengths)
start_coords = tf.concat([[0], end_coords[:-1]], axis=0)
coords = tf.stack([start_coords, end_coords], axis=1)
coords = tf.cast(coords, dtype=tf.int32)
return tf.map_fn(join_charcaters_fn, coords, dtype=tf.string)
def coords_single_sequence():
return tf.reduce_join(characters_list, keep_dims=True)
words = tf.cond(tf.shape(sequence_lengths)[0] > 1,
true_fn=lambda: coords_several_sequences(),
false_fn=lambda: coords_single_sequence())
return words
def create_metric_ops(self, _inputs, labels, predictions):
"""Creates (value, update_op) tensors
"""
with tf.variable_scope(self._name):
# Join tokens into single strings
predictions_flat = tf.reduce_join(
predictions["predicted_tokens"], 1, separator=self._separator)
labels_flat = tf.reduce_join(
labels["target_tokens"], 1, separator=self._separator)
sources_value, sources_update = accumulate_strings(
values=predictions_flat, name="sources")
targets_value, targets_update = accumulate_strings(
values=labels_flat, name="targets")
metric_value = tf.py_func(
func=self._py_func,
inp=[sources_value, targets_value],
Tout=tf.float32,
name="value")
with tf.control_dependencies([sources_update, targets_update]):
update_op = tf.identity(metric_value, name="update_op")
return metric_value, update_op
def create_metric_ops(self, _inputs, labels, predictions):
"""Creates (value, update_op) tensors
"""
with tf.variable_scope(self._name):
# Join tokens into single strings
predictions_flat = tf.reduce_join(
predictions["predicted_tokens"], 1, separator=self._separator)
labels_flat = tf.reduce_join(
labels["target_tokens"], 1, separator=self._separator)
sources_value, sources_update = accumulate_strings(
values=predictions_flat, name="sources")
targets_value, targets_update = accumulate_strings(
values=labels_flat, name="targets")
metric_value = tf.py_func(
func=self._py_func,
inp=[sources_value, targets_value],
Tout=tf.float32,
name="value")
with tf.control_dependencies([sources_update, targets_update]):
update_op = tf.identity(metric_value, name="update_op")
return metric_value, update_op
def create_metric_ops(self, _inputs, labels, predictions):
"""Creates (value, update_op) tensors
"""
with tf.variable_scope(self._name):
# Join tokens into single strings
predictions_flat = tf.reduce_join(
predictions["predicted_tokens"], 1, separator=self._separator)
labels_flat = tf.reduce_join(
labels["target_tokens"], 1, separator=self._separator)
sources_value, sources_update = accumulate_strings(
values=predictions_flat, name="sources")
targets_value, targets_update = accumulate_strings(
values=labels_flat, name="targets")
metric_value = tf.py_func(
func=self._py_func,
inp=[sources_value, targets_value],
Tout=tf.float32,
name="value")
with tf.control_dependencies([sources_update, targets_update]):
update_op = tf.identity(metric_value, name="update_op")
return metric_value, update_op
def read_image_from_disk(input_queue):
label = input_queue[1]
file_name = tf.reduce_join([TENSOR_BASE_PATH, input_queue[0]], 0)
file_contents = tf.read_file(file_name)
rgb_image = tf.image.decode_jpeg(file_contents, channels=FLAGS.image_channels,
name="decode_jpeg")
rgb_image = tf.image.convert_image_dtype(rgb_image, dtype=tf.float32,
name="image_convert_float32")
rgb_image = tf.image.resize_images(rgb_image,
[FLAGS.image_size, FLAGS.image_size])
return rgb_image, label
def evaluate(self, path_to_checkpoint, path_to_tfrecords_file, num_examples, global_step):
batch_size = 128
num_batches = num_examples / batch_size
needs_include_length = False
with tf.Graph().as_default():
image_batch, length_batch, digits_batch = Donkey.build_batch(path_to_tfrecords_file,
num_examples=num_examples,
batch_size=batch_size,
shuffled=False)
# length_logits, digits_logits = Model.inference(image_batch, drop_rate=0.0)
length_logits, digits_logits = Model.forward(image_batch, 1.0)
length_predictions = tf.argmax(length_logits, axis=1)
digits_predictions = tf.argmax(digits_logits, axis=2)
if needs_include_length:
labels = tf.concat([tf.reshape(length_batch, [-1, 1]), digits_batch], axis=1)
predictions = tf.concat([tf.reshape(length_predictions, [-1, 1]), digits_predictions], axis=1)
else:
labels = digits_batch
predictions = digits_predictions
labels_string = tf.reduce_join(tf.as_string(labels), axis=1)
predictions_string = tf.reduce_join(tf.as_string(predictions), axis=1)
accuracy, update_accuracy = tf.metrics.accuracy(
labels=labels_string,
predictions=predictions_string
)
tf.summary.image('image', image_batch)
tf.summary.scalar('accuracy', accuracy)
tf.summary.histogram('variables',
tf.concat([tf.reshape(var, [-1]) for var in tf.trainable_variables()], axis=0))
summary = tf.summary.merge_all()
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
restorer = tf.train.Saver()
restorer.restore(sess, path_to_checkpoint)
for _ in range(int(num_batches)):
sess.run(update_accuracy)
accuracy_val, summary_val = sess.run([accuracy, summary])
self.summary_writer.add_summary(summary_val, global_step=global_step)
coord.request_stop()
coord.join(threads)
return accuracy_val
