我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用tensorflow.reduce_join()。
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)
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 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
