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.assert_type()。
def accumulate_strings(values, name="strings"): """Accumulates strings into a vector. Args: values: A 1-d string tensor that contains values to add to the accumulator. Returns: A tuple (value_tensor, update_op). """ tf.assert_type(values, tf.string) strings = tf.Variable( name=name, initial_value=[], dtype=tf.string, trainable=False, collections=[], validate_shape=True) value_tensor = tf.identity(strings) update_op = tf.assign( ref=strings, value=tf.concat([strings, values], 0), validate_shape=False) return value_tensor, update_op
def convolve(image, pixel_filter, channels=3, name=None): """Perform a 2D pixel convolution on the given image. Arguments: image: A 3D `float32` `Tensor` of shape `[height, width, channels]`, where `channels` is the third argument to this function and the first two dimensions are arbitrary. pixel_filter: A 2D `Tensor`, representing pixel weightings for the kernel. This will be used to create a 4D kernel---the extra two dimensions are for channels (see `tf.nn.conv2d` documentation), and the kernel will be constructed so that the channels are independent: each channel only observes the data from neighboring pixels of the same channel. channels: An integer representing the number of channels in the image (e.g., 3 for RGB). Returns: A 3D `float32` `Tensor` of the same shape as the input. """ with tf.name_scope(name, 'convolve'): tf.assert_type(image, tf.float32) channel_filter = tf.eye(channels) filter_ = (tf.expand_dims(tf.expand_dims(pixel_filter, -1), -1) * tf.expand_dims(tf.expand_dims(channel_filter, 0), 0)) result_batch = tf.nn.conv2d(tf.stack([image]), # batch filter=filter_, strides=[1, 1, 1, 1], padding='SAME') return result_batch[0] # unbatch
def _buckets(data, bucket_count=None): """Create a TensorFlow op to group data into histogram buckets. Arguments: data: A `Tensor` of any shape. Must be castable to `float64`. bucket_count: Optional positive `int` or scalar `int32` `Tensor`. Returns: A `Tensor` of shape `[k, 3]` and type `float64`. The `i`th row is a triple `[left_edge, right_edge, count]` for a single bucket. The value of `k` is either `bucket_count` or `1` or `0`. """ if bucket_count is None: bucket_count = DEFAULT_BUCKET_COUNT with tf.name_scope('buckets', values=[data, bucket_count]), \ tf.control_dependencies([tf.assert_scalar(bucket_count), tf.assert_type(bucket_count, tf.int32)]): data = tf.reshape(data, shape=[-1]) # flatten data = tf.cast(data, tf.float64) is_empty = tf.equal(tf.size(data), 0) def when_empty(): return tf.constant([], shape=(0, 3), dtype=tf.float64) def when_nonempty(): min_ = tf.reduce_min(data) max_ = tf.reduce_max(data) range_ = max_ - min_ is_singular = tf.equal(range_, 0) def when_nonsingular(): bucket_width = range_ / tf.cast(bucket_count, tf.float64) offsets = data - min_ bucket_indices = tf.cast(tf.floor(offsets / bucket_width), dtype=tf.int32) clamped_indices = tf.minimum(bucket_indices, bucket_count - 1) one_hots = tf.one_hot(clamped_indices, depth=bucket_count) bucket_counts = tf.cast(tf.reduce_sum(one_hots, axis=0), dtype=tf.float64) edges = tf.lin_space(min_, max_, bucket_count + 1) left_edges = edges[:-1] right_edges = edges[1:] return tf.transpose(tf.stack( [left_edges, right_edges, bucket_counts])) def when_singular(): center = min_ bucket_starts = tf.stack([center - 0.5]) bucket_ends = tf.stack([center + 0.5]) bucket_counts = tf.stack([tf.cast(tf.size(data), tf.float64)]) return tf.transpose( tf.stack([bucket_starts, bucket_ends, bucket_counts])) return tf.cond(is_singular, when_singular, when_nonsingular) return tf.cond(is_empty, when_empty, when_nonempty)
def op(name, data, display_name=None, description=None, collections=None): """Create a text summary op. Text data summarized via this plugin will be visible in the Text Dashboard in TensorBoard. The standard TensorBoard Text Dashboard will render markdown in the strings, and will automatically organize 1D and 2D tensors into tables. If a tensor with more than 2 dimensions is provided, a 2D subarray will be displayed along with a warning message. (Note that this behavior is not intrinsic to the text summary API, but rather to the default TensorBoard text plugin.) Args: name: A name for the generated node. Will also serve as a series name in TensorBoard. data: A string-type Tensor to summarize. The text must be encoded in UTF-8. 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 ops.GraphKeys. The collections to which to add the summary. Defaults to [Graph Keys.SUMMARIES]. Returns: A TensorSummary op that is configured so that TensorBoard will recognize that it contains textual data. The TensorSummary is a scalar `Tensor` of type `string` which contains `Summary` protobufs. Raises: ValueError: If tensor has the wrong type. """ 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): with tf.control_dependencies([tf.assert_type(data, tf.string)]): return tf.summary.tensor_summary(name='text_summary', tensor=data, collections=collections, summary_metadata=summary_metadata)
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)
