我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用tensorflow.as_string()。
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 data_loader(csv_filename: str, params: Params, batch_size: int=128, data_augmentation: bool=False, num_epochs: int=None, image_summaries: bool=False): def input_fn(): # Choose case one csv file or list of csv files if not isinstance(csv_filename, list): filename_queue = tf.train.string_input_producer([csv_filename], num_epochs=num_epochs, name='filename_queue') elif isinstance(csv_filename, list): filename_queue = tf.train.string_input_producer(csv_filename, num_epochs=num_epochs, name='filename_queue') # Skip lines that have already been processed reader = tf.TextLineReader(name='CSV_Reader', skip_header_lines=0) key, value = reader.read(filename_queue, name='file_reading_op') default_line = [['None'], ['None']] path, label = tf.decode_csv(value, record_defaults=default_line, field_delim=params.csv_delimiter, name='csv_reading_op') image, img_width = image_reading(path, resized_size=params.input_shape, data_augmentation=data_augmentation, padding=True) to_batch = {'images': image, 'images_widths': img_width, 'filenames': path, 'labels': label} prepared_batch = tf.train.shuffle_batch(to_batch, batch_size=batch_size, min_after_dequeue=500, num_threads=15, capacity=4000, allow_smaller_final_batch=False, name='prepared_batch_queue') if image_summaries: tf.summary.image('input/image', prepared_batch.get('images'), max_outputs=1) tf.summary.text('input/labels', prepared_batch.get('labels')[:10]) tf.summary.text('input/widths', tf.as_string(prepared_batch.get('images_widths'))) return prepared_batch, prepared_batch.get('labels') return input_fn
def build_metagraph_list(self): """ Convert MetaParams into TF Summary Format and create summary_op Args: None Returns: Merged TF Op for TEXT summary elements, should only be executed once to reduce data duplication """ ops = [] self.ignore_unknown_dtypes = True for key in sorted(self.meta_params): value = self.convert_data_to_string(self.meta_params[key]) if len(value) == 0: continue if isinstance(value,str): ops.append(tf.summary.text(key, tf.convert_to_tensor(str(value)))) else: ops.append(tf.summary.text(key, tf.as_string(tf.convert_to_tensor(value)))) with tf.control_dependencies(tf.tuple(ops)): self.summary_merged = tf.summary.merge_all() return self.summary_merged
def simple_example(step): # Text summaries log arbitrary text. This can be encoded with ASCII or # UTF-8. Here's a simple example, wherein we greet the user on each # step: step_string = tf.as_string(step) greeting = tf.string_join(['Hello from step ', step_string, '!']) tf.summary.text('greeting', greeting)
def higher_order_tensors(step): # We're not limited to passing scalar tensors to the summary # operation. If we pass a rank-1 or rank-2 tensor, it'll be visualized # as a table in TensorBoard. (For higher-ranked tensors, you'll see # just a 2D slice of the data.) # # To demonstrate this, let's create a multiplication table. # First, we'll create the table body, a `step`-by-`step` array of # strings. numbers = tf.range(step) numbers_row = tf.expand_dims(numbers, 0) # shape: [1, step] numbers_column = tf.expand_dims(numbers, 1) # shape: [step, 1] products = tf.matmul(numbers_column, numbers_row) # shape: [step, step] table_body = tf.as_string(products) # Next, we'll create a header row and column, and a little # multiplication sign to put in the corner. bold_numbers = tf.string_join(['**', tf.as_string(numbers), '**']) bold_row = tf.expand_dims(bold_numbers, 0) bold_column = tf.expand_dims(bold_numbers, 1) corner_cell = tf.constant(u'\u00d7'.encode('utf-8')) # MULTIPLICATION SIGN # Now, we have to put the pieces together. Using `axis=0` stacks # vertically; using `axis=1` juxtaposes horizontally. table_body_and_top_row = tf.concat([bold_row, table_body], axis=0) table_left_column = tf.concat([[[corner_cell]], bold_column], axis=0) table_full = tf.concat([table_left_column, table_body_and_top_row], axis=1) # The result, `table_full`, is a rank-2 string tensor of shape # `[step + 1, step + 1]`. We can pass it directly to the summary, and # we'll get a nicely formatted table in TensorBoard. tf.summary.text('multiplication_table', table_full)
def generate_run(self, run_name, include_graph): """Create a run with a text summary, metadata, and optionally a graph.""" tf.reset_default_graph() k1 = tf.constant(math.pi, name='k1') k2 = tf.constant(math.e, name='k2') result = (k1 ** k2) - k1 expected = tf.constant(20.0, name='expected') error = tf.abs(result - expected, name='error') message_prefix_value = 'error ' * 1000 true_length = len(message_prefix_value) assert true_length > self._MESSAGE_PREFIX_LENGTH_LOWER_BOUND, true_length message_prefix = tf.constant(message_prefix_value, name='message_prefix') error_message = tf.string_join([message_prefix, tf.as_string(error, name='error_string')], name='error_message') summary_message = tf.summary.text('summary_message', error_message) sess = tf.Session() writer = tf.summary.FileWriter(os.path.join(self.logdir, run_name)) if include_graph: writer.add_graph(sess.graph) options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() s = sess.run(summary_message, options=options, run_metadata=run_metadata) writer.add_summary(s) writer.add_run_metadata(run_metadata, self._METADATA_TAG) writer.close()
def _read_from_disk_spatial(fpath, nframes, num_samples=25, start_frame=0, file_prefix='', file_zero_padding=4, file_index=1, dataset_dir='', step=None): duration = nframes if step is None: if num_samples == 1: step = tf.random_uniform([1], 0, nframes, dtype='int32')[0] else: step = tf.cast((duration-tf.constant(1)) / (tf.constant(num_samples-1)), 'int32') allimgs = [] with tf.variable_scope('read_rgb_video'): for i in range(num_samples): if num_samples == 1: i = 1 # so that the random step value can be used with tf.variable_scope('read_rgb_image'): prefix = file_prefix + '_' if file_prefix else '' impath = tf.string_join([ tf.constant(dataset_dir + '/'), fpath, tf.constant('/'), prefix, tf.as_string(start_frame + i * step + file_index, width=file_zero_padding, fill='0'), tf.constant('.jpg')]) img_str = tf.read_file(impath) allimgs.append(img_str) return allimgs
def _read_from_disk_temporal( fpath, nframes, num_samples=25, optical_flow_frames=10, start_frame=0, file_prefix='', file_zero_padding=4, file_index=1, dataset_dir='', step=None): duration = nframes if step is None: if num_samples == 1: step = tf.random_uniform([1], 0, nframes-optical_flow_frames-1, dtype='int32')[0] else: step = tf.cast((duration-tf.constant(optical_flow_frames)) / (tf.constant(num_samples)), 'int32') allimgs = [] with tf.variable_scope('read_flow_video'): for i in range(num_samples): if num_samples == 1: i = 1 # so that the random step value can be used with tf.variable_scope('read_flow_image'): flow_img = [] for j in range(optical_flow_frames): with tf.variable_scope('read_flow_channels'): for dr in ['x', 'y']: prefix = file_prefix + '_' if file_prefix else '' impath = tf.string_join([ tf.constant(dataset_dir + '/'), fpath, tf.constant('/'), prefix, '%s_' % dr, tf.as_string(start_frame + i * step + file_index + j, width=file_zero_padding, fill='0'), tf.constant('.jpg')]) img_str = tf.read_file(impath) flow_img.append(img_str) allimgs.append(flow_img) return allimgs
def op(name, images, max_outputs=3, display_name=None, description=None, collections=None): """Create an image summary op for use in a TensorFlow graph. Arguments: name: A unique name for the generated summary node. images: A `Tensor` representing pixel data with shape `[k, w, h, c]`, where `k` is the number of images, `w` and `h` are the width and height of the images, and `c` is the number of channels, which should be 1, 3, or 4. Any of the dimensions may be statically unknown (i.e., `None`). max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this many images will be emitted at each step. When more than `max_outputs` many images are provided, the first `max_outputs` many images will be used and the rest silently discarded. display_name: Optional name for this summary in TensorBoard, as a constant `str`. Defaults to `name`. description: Optional long-form description for this summary, as a constant `str`. Markdown is supported. Defaults to empty. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[Graph Keys.SUMMARIES]`. Returns: A TensorFlow summary op. """ if display_name is None: display_name = name summary_metadata = metadata.create_summary_metadata( display_name=display_name, description=description) with tf.name_scope(name), \ tf.control_dependencies([tf.assert_rank(images, 4), tf.assert_type(images, tf.uint8), tf.assert_non_negative(max_outputs)]): limited_images = images[:max_outputs] encoded_images = tf.map_fn(tf.image.encode_png, limited_images, dtype=tf.string, name='encode_each_image') image_shape = tf.shape(images) dimensions = tf.stack([tf.as_string(image_shape[1], name='width'), tf.as_string(image_shape[2], name='height')], name='dimensions') tensor = tf.concat([dimensions, encoded_images], axis=0) return tf.summary.tensor_summary(name='image_summary', tensor=tensor, collections=collections, summary_metadata=summary_metadata)
