Python tensorflow.assert_less_equal() Examples
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Example #1
Source File: memory.py From soccer-matlab with BSD 2-Clause "Simplified" License | 6 votes |
def replace(self, episodes, length, rows=None): """Replace full episodes. Args: episodes: Tuple of transition quantities with batch and time dimensions. length: Batch of sequence lengths. rows: Episodes to replace, defaults to all. Returns: Operation. """ rows = tf.range(self._capacity) if rows is None else rows assert rows.shape.ndims == 1 assert_capacity = tf.assert_less( rows, self._capacity, message='capacity exceeded') with tf.control_dependencies([assert_capacity]): assert_max_length = tf.assert_less_equal( length, self._max_length, message='max length exceeded') replace_ops = [] with tf.control_dependencies([assert_max_length]): for buffer_, elements in zip(self._buffers, episodes): replace_op = tf.scatter_update(buffer_, rows, elements) replace_ops.append(replace_op) with tf.control_dependencies(replace_ops): return tf.scatter_update(self._length, rows, length)
Example #2
Source File: GAN.py From VideoSuperResolution with MIT License | 6 votes |
def _preprocess_for_inception(images): """Preprocess images for inception. Args: images: images minibatch. Shape [batch size, width, height, channels]. Values are in [0..255]. Returns: preprocessed_images """ images = tf.cast(images, tf.float32) # tfgan_eval.preprocess_image function takes values in [0, 255] with tf.control_dependencies([tf.assert_greater_equal(images, 0.0), tf.assert_less_equal(images, 255.0)]): images = tf.identity(images) preprocessed_images = tf.map_fn( fn=_TFGAN.preprocess_image, elems=images, back_prop=False) return preprocessed_images
Example #3
Source File: memory.py From batch-ppo with Apache License 2.0 | 6 votes |
def replace(self, episodes, length, rows=None): """Replace full episodes. Args: episodes: Tuple of transition quantities with batch and time dimensions. length: Batch of sequence lengths. rows: Episodes to replace, defaults to all. Returns: Operation. """ rows = tf.range(self._capacity) if rows is None else rows assert rows.shape.ndims == 1 assert_capacity = tf.assert_less( rows, self._capacity, message='capacity exceeded') with tf.control_dependencies([assert_capacity]): assert_max_length = tf.assert_less_equal( length, self._max_length, message='max length exceeded') with tf.control_dependencies([assert_max_length]): replace_ops = tools.nested.map( lambda var, val: tf.scatter_update(var, rows, val), self._buffers, episodes, flatten=True) with tf.control_dependencies(replace_ops): return tf.scatter_update(self._length, rows, length)
Example #4
Source File: gmm_ops.py From deep_image_model with Apache License 2.0 | 6 votes |
def _init_clusters_random(data, num_clusters, random_seed): """Does random initialization of clusters. Args: data: a list of Tensors with a matrix of data, each row is an example. num_clusters: an integer with the number of clusters. random_seed: Seed for PRNG used to initialize seeds. Returns: A Tensor with num_clusters random rows of data. """ assert isinstance(data, list) num_data = tf.add_n([tf.shape(inp)[0] for inp in data]) with tf.control_dependencies([tf.assert_less_equal(num_clusters, num_data)]): indices = tf.random_uniform([num_clusters], minval=0, maxval=tf.cast(num_data, tf.int64), seed=random_seed, dtype=tf.int64) indices = tf.cast(indices, tf.int32) % num_data clusters_init = embedding_lookup(data, indices, partition_strategy='div') return clusters_init
Example #5
Source File: clustering_ops.py From deep_image_model with Apache License 2.0 | 6 votes |
def _init_clusters_random(self): """Does random initialization of clusters. Returns: Tensor of randomly initialized clusters. """ num_data = tf.add_n([tf.shape(inp)[0] for inp in self._inputs]) # Note that for mini-batch k-means, we should ensure that the batch size of # data used during initialization is sufficiently large to avoid duplicated # clusters. with tf.control_dependencies( [tf.assert_less_equal(self._num_clusters, num_data)]): indices = tf.random_uniform(tf.reshape(self._num_clusters, [-1]), minval=0, maxval=tf.cast(num_data, tf.int64), seed=self._random_seed, dtype=tf.int64) clusters_init = embedding_lookup(self._inputs, indices, partition_strategy='div') return clusters_init
Example #6
Source File: data_reader.py From kfac with Apache License 2.0 | 6 votes |
def __call__(self, batch_size): """Reads `batch_size` data. Args: batch_size: Tensor of type `int32`, batch size of the data to be retrieved from the dataset. `batch_size` should be less than or equal to `max_batch_size`. Returns: Read data, An iterable of tensors with batch size equal to `batch_size`. """ check_size = tf.assert_less_equal( batch_size, tf.convert_to_tensor(self._max_batch_size, dtype=tf.int32), message='Data set read failure, Batch size greater than max allowed.' ) with tf.control_dependencies([check_size]): return _slice_data(self._dataset, batch_size)
Example #7
Source File: data_reader_alt.py From kfac with Apache License 2.0 | 6 votes |
def __call__(self, batch_size): """Reads `batch_size` data. Args: batch_size: Tensor of type `int32`. Batch size of the data to be retrieved from the dataset. `batch_size` should be less than or equal to the number of examples in the dataset. Returns: Read data, a list of Tensors with batch size equal to `batch_size`. """ check_size = tf.assert_less_equal( batch_size, tf.convert_to_tensor(self._num_examples, dtype=tf.int32), message='Data set read failure, batch_size > num_examples.' ) with tf.control_dependencies([check_size]): self._indices = tf.random.shuffle( tf.range(self._num_examples, dtype=tf.int32)) return _extract_data(self._dataset, self._indices[:batch_size])
Example #8
Source File: memory.py From soccer-matlab with BSD 2-Clause "Simplified" License | 6 votes |
def replace(self, episodes, length, rows=None): """Replace full episodes. Args: episodes: Tuple of transition quantities with batch and time dimensions. length: Batch of sequence lengths. rows: Episodes to replace, defaults to all. Returns: Operation. """ rows = tf.range(self._capacity) if rows is None else rows assert rows.shape.ndims == 1 assert_capacity = tf.assert_less( rows, self._capacity, message='capacity exceeded') with tf.control_dependencies([assert_capacity]): assert_max_length = tf.assert_less_equal( length, self._max_length, message='max length exceeded') replace_ops = [] with tf.control_dependencies([assert_max_length]): for buffer_, elements in zip(self._buffers, episodes): replace_op = tf.scatter_update(buffer_, rows, elements) replace_ops.append(replace_op) with tf.control_dependencies(replace_ops): return tf.scatter_update(self._length, rows, length)
Example #9
Source File: attacks_tf.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _project_perturbation(perturbation, epsilon, input_image): """Project `perturbation` onto L-infinity ball of radius `epsilon`.""" # Ensure inputs are in the correct range with tf.control_dependencies([ tf.assert_less_equal(input_image, 1.0), tf.assert_greater_equal(input_image, 0.0) ]): clipped_perturbation = tf.clip_by_value( perturbation, -epsilon, epsilon) new_image = tf.clip_by_value( input_image + clipped_perturbation, 0., 1.) return new_image - input_image
Example #10
Source File: preprocessing.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def scale_to_inception_range(image): """Scales an image in the range [0,1] to [-1,1] as expected by inception.""" # Assert that incoming images have been properly scaled to [0,1]. with tf.control_dependencies( [tf.assert_less_equal(tf.reduce_max(image), 1.), tf.assert_greater_equal(tf.reduce_min(image), 0.)]): image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image
Example #11
Source File: utils_tf.py From cleverhans with MIT License | 5 votes |
def assert_less_equal(*args, **kwargs): """ Wrapper for tf.assert_less_equal Overrides tf.device so that the assert always goes on CPU. The unwrapped version raises an exception if used with tf.device("/GPU:x"). """ with tf.device("/CPU:0"): return tf.assert_less_equal(*args, **kwargs)
Example #12
Source File: utils.py From models with Apache License 2.0 | 5 votes |
def new_mean_squared(grad_vec, decay, ms): """Calculates the new accumulated mean squared of the gradient. Args: grad_vec: the vector for the current gradient decay: the decay term ms: the previous mean_squared value Returns: the new mean_squared value """ decay_size = decay.get_shape().num_elements() decay_check_ops = [ tf.assert_less_equal(decay, 1., summarize=decay_size), tf.assert_greater_equal(decay, 0., summarize=decay_size)] with tf.control_dependencies(decay_check_ops): grad_squared = tf.square(grad_vec) # If the previous mean_squared is the 0 vector, don't use the decay and just # return the full grad_squared. This should only happen on the first timestep. decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)), lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay) # Update the running average of squared gradients. epsilon = 1e-12 return (1. - decay) * (grad_squared + epsilon) + decay * ms
Example #13
Source File: preprocessing.py From models with Apache License 2.0 | 5 votes |
def scale_to_inception_range(image): """Scales an image in the range [0,1] to [-1,1] as expected by inception.""" # Assert that incoming images have been properly scaled to [0,1]. with tf.control_dependencies( [tf.assert_less_equal(tf.reduce_max(image), 1.), tf.assert_greater_equal(tf.reduce_min(image), 0.)]): image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image
Example #14
Source File: utils.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def new_mean_squared(grad_vec, decay, ms): """Calculates the new accumulated mean squared of the gradient. Args: grad_vec: the vector for the current gradient decay: the decay term ms: the previous mean_squared value Returns: the new mean_squared value """ decay_size = decay.get_shape().num_elements() decay_check_ops = [ tf.assert_less_equal(decay, 1., summarize=decay_size), tf.assert_greater_equal(decay, 0., summarize=decay_size)] with tf.control_dependencies(decay_check_ops): grad_squared = tf.square(grad_vec) # If the previous mean_squared is the 0 vector, don't use the decay and just # return the full grad_squared. This should only happen on the first timestep. decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)), lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay) # Update the running average of squared gradients. epsilon = 1e-12 return (1. - decay) * (grad_squared + epsilon) + decay * ms
Example #15
Source File: preprocessing.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def scale_to_inception_range(image): """Scales an image in the range [0,1] to [-1,1] as expected by inception.""" # Assert that incoming images have been properly scaled to [0,1]. with tf.control_dependencies( [tf.assert_less_equal(tf.reduce_max(image), 1.), tf.assert_greater_equal(tf.reduce_min(image), 0.)]): image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image
Example #16
Source File: inception_network.py From precision-recall-distributions with Apache License 2.0 | 5 votes |
def preprocess_for_inception(images): """Preprocess images for inception. Args: images: images minibatch. Shape [batch size, width, height, channels]. Values are in [0..255]. Returns: preprocessed_images """ # Images should have 3 channels. assert images.shape[3].value == 3 # tf.contrib.gan.eval.preprocess_image function takes values in [0, 255] with tf.control_dependencies([tf.assert_greater_equal(images, 0.0), tf.assert_less_equal(images, 255.0)]): images = tf.identity(images) preprocessed_images = tf.map_fn( fn=tf.contrib.gan.eval.preprocess_image, elems=images, back_prop=False ) return preprocessed_images
Example #17
Source File: eval_utils.py From compare_gan with Apache License 2.0 | 5 votes |
def inception_transform(inputs): with tf.control_dependencies([ tf.assert_greater_equal(inputs, 0.0), tf.assert_less_equal(inputs, 255.0)]): inputs = tf.identity(inputs) preprocessed_inputs = tf.map_fn( fn=tfgan.eval.preprocess_image, elems=inputs, back_prop=False) return tfgan.eval.run_inception( preprocessed_inputs, graph_def=get_inception_graph_def(), output_tensor=["pool_3:0", "logits:0"])
Example #18
Source File: utils.py From object_detection_with_tensorflow with MIT License | 5 votes |
def new_mean_squared(grad_vec, decay, ms): """Calculates the new accumulated mean squared of the gradient. Args: grad_vec: the vector for the current gradient decay: the decay term ms: the previous mean_squared value Returns: the new mean_squared value """ decay_size = decay.get_shape().num_elements() decay_check_ops = [ tf.assert_less_equal(decay, 1., summarize=decay_size), tf.assert_greater_equal(decay, 0., summarize=decay_size)] with tf.control_dependencies(decay_check_ops): grad_squared = tf.square(grad_vec) # If the previous mean_squared is the 0 vector, don't use the decay and just # return the full grad_squared. This should only happen on the first timestep. decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)), lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay) # Update the running average of squared gradients. epsilon = 1e-12 return (1. - decay) * (grad_squared + epsilon) + decay * ms
Example #19
Source File: preprocessing.py From object_detection_with_tensorflow with MIT License | 5 votes |
def scale_to_inception_range(image): """Scales an image in the range [0,1] to [-1,1] as expected by inception.""" # Assert that incoming images have been properly scaled to [0,1]. with tf.control_dependencies( [tf.assert_less_equal(tf.reduce_max(image), 1.), tf.assert_greater_equal(tf.reduce_min(image), 0.)]): image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image
Example #20
Source File: utils.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def new_mean_squared(grad_vec, decay, ms): """Calculates the new accumulated mean squared of the gradient. Args: grad_vec: the vector for the current gradient decay: the decay term ms: the previous mean_squared value Returns: the new mean_squared value """ decay_size = decay.get_shape().num_elements() decay_check_ops = [ tf.assert_less_equal(decay, 1., summarize=decay_size), tf.assert_greater_equal(decay, 0., summarize=decay_size)] with tf.control_dependencies(decay_check_ops): grad_squared = tf.square(grad_vec) # If the previous mean_squared is the 0 vector, don't use the decay and just # return the full grad_squared. This should only happen on the first timestep. decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)), lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay) # Update the running average of squared gradients. epsilon = 1e-12 return (1. - decay) * (grad_squared + epsilon) + decay * ms
Example #21
Source File: utils.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def new_mean_squared(grad_vec, decay, ms): """Calculates the new accumulated mean squared of the gradient. Args: grad_vec: the vector for the current gradient decay: the decay term ms: the previous mean_squared value Returns: the new mean_squared value """ decay_size = decay.get_shape().num_elements() decay_check_ops = [ tf.assert_less_equal(decay, 1., summarize=decay_size), tf.assert_greater_equal(decay, 0., summarize=decay_size)] with tf.control_dependencies(decay_check_ops): grad_squared = tf.square(grad_vec) # If the previous mean_squared is the 0 vector, don't use the decay and just # return the full grad_squared. This should only happen on the first timestep. decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)), lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay) # Update the running average of squared gradients. epsilon = 1e-12 return (1. - decay) * (grad_squared + epsilon) + decay * ms
Example #22
Source File: dataset_utils.py From TwinGAN with Apache License 2.0 | 5 votes |
def __init__(self, preprocess_fn = None): # Create a single Session to run all image coding calls. # All images must either be in the same format, or have their encoding method recorded. # Initializes function that converts PNG to JPEG data. self._png_data = tf.placeholder(dtype=tf.string) self._decode_png = tf.image.decode_png(self._png_data) self._png_to_jpeg = tf.image.encode_jpeg(self._decode_png, format='rgb', quality=100) # Initializes function that decodes RGB JPEG data. self._decode_jpeg_data = tf.placeholder(dtype=tf.string) self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3) # self._decode_image_data = tf.placeholder(dtype=tf.string) image = tf.image.decode_image(self._decode_image_data, channels=3) self._image_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100) self._array_image = tf.placeholder(shape=[None, None, None], dtype=tf.uint8) self._encode_array_to_jpeg = tf.image.encode_jpeg(self._array_image, format='rgb', quality=100) if preprocess_fn: image.set_shape([None, None, 3]) self._decode_preprocessed_image = preprocess_fn(image) assert self._decode_preprocessed_image.dtype == tf.uint8 le_255 = tf.assert_less_equal(self._decode_preprocessed_image, tf.constant(255, tf.uint8)) ge_0 = tf.assert_non_negative(self._decode_preprocessed_image) with tf.control_dependencies([le_255, ge_0]): format = 'grayscale' if self._decode_preprocessed_image.shape[-1] == 1 else 'rgb' self._image_to_preprocessed_jpeg = tf.image.encode_jpeg(self._decode_preprocessed_image, format=format, quality=100) self._image_preprocessed_shape = tf.shape(self._decode_preprocessed_image) else: self._image_to_preprocessed_jpeg = None self._image_preprocessed_shape = None
Example #23
Source File: preprocessing.py From yolo_v2 with Apache License 2.0 | 5 votes |
def scale_to_inception_range(image): """Scales an image in the range [0,1] to [-1,1] as expected by inception.""" # Assert that incoming images have been properly scaled to [0,1]. with tf.control_dependencies( [tf.assert_less_equal(tf.reduce_max(image), 1.), tf.assert_greater_equal(tf.reduce_min(image), 0.)]): image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image
Example #24
Source File: utils.py From yolo_v2 with Apache License 2.0 | 5 votes |
def new_mean_squared(grad_vec, decay, ms): """Calculates the new accumulated mean squared of the gradient. Args: grad_vec: the vector for the current gradient decay: the decay term ms: the previous mean_squared value Returns: the new mean_squared value """ decay_size = decay.get_shape().num_elements() decay_check_ops = [ tf.assert_less_equal(decay, 1., summarize=decay_size), tf.assert_greater_equal(decay, 0., summarize=decay_size)] with tf.control_dependencies(decay_check_ops): grad_squared = tf.square(grad_vec) # If the previous mean_squared is the 0 vector, don't use the decay and just # return the full grad_squared. This should only happen on the first timestep. decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)), lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay) # Update the running average of squared gradients. epsilon = 1e-12 return (1. - decay) * (grad_squared + epsilon) + decay * ms
Example #25
Source File: preprocessing.py From Gun-Detector with Apache License 2.0 | 5 votes |
def scale_to_inception_range(image): """Scales an image in the range [0,1] to [-1,1] as expected by inception.""" # Assert that incoming images have been properly scaled to [0,1]. with tf.control_dependencies( [tf.assert_less_equal(tf.reduce_max(image), 1.), tf.assert_greater_equal(tf.reduce_min(image), 0.)]): image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image
Example #26
Source File: utils.py From Gun-Detector with Apache License 2.0 | 5 votes |
def new_mean_squared(grad_vec, decay, ms): """Calculates the new accumulated mean squared of the gradient. Args: grad_vec: the vector for the current gradient decay: the decay term ms: the previous mean_squared value Returns: the new mean_squared value """ decay_size = decay.get_shape().num_elements() decay_check_ops = [ tf.assert_less_equal(decay, 1., summarize=decay_size), tf.assert_greater_equal(decay, 0., summarize=decay_size)] with tf.control_dependencies(decay_check_ops): grad_squared = tf.square(grad_vec) # If the previous mean_squared is the 0 vector, don't use the decay and just # return the full grad_squared. This should only happen on the first timestep. decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)), lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay) # Update the running average of squared gradients. epsilon = 1e-12 return (1. - decay) * (grad_squared + epsilon) + decay * ms
Example #27
Source File: check_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def test_doesnt_raise_when_equal(self): with self.test_session(): small = tf.constant([1, 2], name="small") with tf.control_dependencies([tf.assert_less_equal(small, small)]): out = tf.identity(small) out.eval()
Example #28
Source File: check_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def test_raises_when_greater(self): with self.test_session(): small = tf.constant([1, 2], name="small") big = tf.constant([3, 4], name="big") with tf.control_dependencies( [tf.assert_less_equal(big, small, message="fail")]): out = tf.identity(small) with self.assertRaisesOpError("fail.*big.*small"): out.eval()
Example #29
Source File: check_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def test_doesnt_raise_when_less_equal(self): with self.test_session(): small = tf.constant([1, 2], name="small") big = tf.constant([3, 2], name="big") with tf.control_dependencies([tf.assert_less_equal(small, big)]): out = tf.identity(small) out.eval()
Example #30
Source File: check_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def test_doesnt_raise_when_less_equal_and_broadcastable_shapes(self): with self.test_session(): small = tf.constant([1], name="small") big = tf.constant([3, 1], name="big") with tf.control_dependencies([tf.assert_less_equal(small, big)]): out = tf.identity(small) out.eval()