本文整理汇总了Python中tensorflow.compat.v1.SparseTensor方法的典型用法代码示例。如果您正苦于以下问题:Python v1.SparseTensor方法的具体用法?Python v1.SparseTensor怎么用?Python v1.SparseTensor使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.compat.v1
的用法示例。
在下文中一共展示了v1.SparseTensor方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _build
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def _build(self):
dataset = tfds.load(name=self._dataset_name, split=self._mode)
minibatch = dataset.map(parse).repeat()
if self._shuffle:
minibatch = minibatch.shuffle(self._batch_size*100)
minibatch = minibatch.batch(
self._batch_size).make_one_shot_iterator().get_next()
minibatch['sentiment'].set_shape([self._batch_size])
minibatch['sentence'] = tf.SparseTensor(
indices=minibatch['sentence'].indices,
values=minibatch['sentence'].values,
dense_shape=[self._batch_size, minibatch['sentence'].dense_shape[1]])
# minibatch.sentence sparse tensor with dense shape
# [batch_size x seq_length], length: [batch_size]
return Dataset(
tokens=minibatch['sentence'],
num_tokens=self.get_row_lengths(minibatch['sentence']),
sentiment=minibatch['sentiment'],
)
示例2: parse
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def parse(data_dict):
"""Parse dataset from _data_gen into the same format as sst_binary."""
sentiment = data_dict['label']
sentence = data_dict['sentence']
dense_chars = tf.decode_raw(sentence, tf.uint8)
dense_chars.set_shape((None,))
chars = tfp.math.dense_to_sparse(dense_chars)
if six.PY3:
safe_chr = lambda c: '?' if c >= 128 else chr(c)
else:
safe_chr = chr
to_char = np.vectorize(safe_chr)
chars = tf.SparseTensor(indices=chars.indices,
values=tf.py_func(to_char, [chars.values], tf.string),
dense_shape=chars.dense_shape)
return {'sentiment': sentiment,
'sentence': chars}
示例3: _sparse_tensor_to_numpy_dict
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def _sparse_tensor_to_numpy_dict(
sparse_tensor):
"""Convert a tf.SparseTensor into a dictionary of numpy arrays.
Args:
sparse_tensor: A SparseTensor of the trained relation.
Returns:
A dictionary representing the data.
"""
return {
'shape': sparse_tensor.dense_shape,
'rows': numpy.array(sparse_tensor.indices[:, 0]),
'cols': numpy.array(sparse_tensor.indices[:, 1]),
'values': numpy.array(sparse_tensor.values)
}
示例4: declare_relation
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def declare_relation(self,
rel_name,
domain_type,
range_type,
trainable = False,
dense = False,
gpus = 0):
"""Declare the domain and range types for a relation.
Args:
rel_name: string naming a relation
domain_type: string naming the type of subject entities for the relation
range_type: string naming the type of object entities for the relation
trainable: boolean, true if the weights for this relation will be trained
dense: if true, store data as a dense tensor instead of a SparseTensor
gpus: number of gpus available for computation
Raises:
RelationNameError: If a relation with this name already exists.
"""
super(DistributedNeuralQueryContext,
self).declare_relation(rel_name, domain_type, range_type, trainable,
dense)
if gpus <= 0:
self._declaration[rel_name].underlying_parameters = [None] * gpus
示例5: sparse_dense_mul
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def sparse_dense_mul(sp_mat, dense_mat):
"""Element-wise multiplication between sparse and dense tensors.
Returns a sparse tensor. Limited broadcasting of dense_mat is supported.
If rank(dense_mat) < rank(sparse_mat), then dense_mat is broadcasted on the
rightmost dimensions to match sparse_mat.
Args:
sp_mat: SparseTensor.
dense_mat: DenseTensor with rank <= sp_mat.
Returns:
SparseTensor.
"""
rank = dense_mat.get_shape().ndims
indices = sp_mat.indices[:, :rank]
dense_values = tf.gather_nd(dense_mat, indices)
return tf.SparseTensor(sp_mat.indices, sp_mat.values * dense_values,
sp_mat.dense_shape)
示例6: sparse_reduce
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def sparse_reduce(sp_tensor, rank, agg_fn="sum", axis=-1):
"""Reduce SparseTensor along the given axis.
Args:
sp_tensor: SparseTensor of arbitrary rank.
rank: Integer rank of the sparse tensor.
agg_fn: Reduce function for aggregation.
axis: Integer specifying axis to sum over.
Returns:
sp_tensor: SparseTensor of one less rank.
"""
if axis < 0:
axis = rank + axis
axes_to_keep = tf.one_hot(
axis, rank, on_value=False, off_value=True, dtype=tf.bool)
indices_to_keep = tf.boolean_mask(sp_tensor.indices, axes_to_keep, axis=1)
new_shape = tf.boolean_mask(sp_tensor.dense_shape, axes_to_keep)
indices_to_keep.set_shape([None, rank - 1])
indices, values = aggregate_sparse_indices(
indices_to_keep, sp_tensor.values, new_shape, agg_fn=agg_fn)
return tf.sparse.reorder(tf.SparseTensor(indices, values, new_shape))
示例7: remove_from_sparse
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def remove_from_sparse(sp_tensor, remove_indices):
"""Remove indices from SparseTensor if present."""
# 1. create 1d index maps
scaling_vector = tf.cumprod(
tf.cast(tf.shape(sp_tensor), sp_tensor.indices.dtype))
a1s = tf.linalg.matvec(sp_tensor.indices, scaling_vector)
b1s = tf.linalg.matvec(remove_indices, scaling_vector)
# 2. get relevant indices of sp_a
int_idx = tf.setdiff1d(a1s, b1s)[1]
to_retain = tf.sparse_to_dense(
sparse_indices=int_idx,
output_shape=tf.shape(a1s),
default_value=0.0,
sparse_values=1.0) > 0.5
return tf.sparse.retain(sp_tensor, to_retain)
示例8: load_sparse_matrix
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def load_sparse_matrix(var_name, checkpoint_path):
"""Load sparse matrix from checkpoint."""
with tf.gfile.Open(checkpoint_path + ".info") as f:
num_nnz = int(f.read())
tf_data = tf.get_local_variable(
var_name + "_data", shape=[num_nnz], dtype=tf.float32, use_resource=True)
tf_indices = tf.get_local_variable(
var_name + "_indices",
shape=[num_nnz, 2],
dtype=tf.int64,
use_resource=True)
tf_shape = tf.get_local_variable(
var_name + "_shape", shape=[2], dtype=tf.int64, use_resource=True)
init_from_checkpoint(
checkpoint_path, target_variables=[tf_data, tf_indices, tf_shape])
tf_sp = tf.SparseTensor(tf_indices, tf_data, tf_shape)
return tf_sp
示例9: _reshape_context_features
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def _reshape_context_features(self, keys_to_tensors):
"""Reshape context features.
The instance context_features are reshaped to
[num_context_features, context_feature_length]
Args:
keys_to_tensors: a dictionary from keys to tensors.
Returns:
A 2-D float tensor of shape [num_context_features, context_feature_length]
"""
context_feature_length = keys_to_tensors['image/context_feature_length']
to_shape = tf.cast(tf.stack([-1, context_feature_length]), tf.int32)
context_features = keys_to_tensors['image/context_features']
if isinstance(context_features, tf.SparseTensor):
context_features = tf.sparse_tensor_to_dense(context_features)
context_features = tf.reshape(context_features, to_shape)
return context_features
示例10: _reshape_keypoints
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def _reshape_keypoints(self, keys_to_tensors):
"""Reshape keypoints.
The keypoints are reshaped to [num_instances, num_keypoints, 2].
Args:
keys_to_tensors: a dictionary from keys to tensors. Expected keys are:
'image/object/keypoint/x'
'image/object/keypoint/y'
Returns:
A 3-D float tensor of shape [num_instances, num_keypoints, 2] with values
in [0, 1].
"""
y = keys_to_tensors['image/object/keypoint/y']
if isinstance(y, tf.SparseTensor):
y = tf.sparse_tensor_to_dense(y)
y = tf.expand_dims(y, 1)
x = keys_to_tensors['image/object/keypoint/x']
if isinstance(x, tf.SparseTensor):
x = tf.sparse_tensor_to_dense(x)
x = tf.expand_dims(x, 1)
keypoints = tf.concat([y, x], 1)
keypoints = tf.reshape(keypoints, [-1, self._num_keypoints, 2])
return keypoints
示例11: _reshape_instance_masks
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def _reshape_instance_masks(self, keys_to_tensors):
"""Reshape instance segmentation masks.
The instance segmentation masks are reshaped to [num_instances, height,
width].
Args:
keys_to_tensors: a dictionary from keys to tensors.
Returns:
A 3-D float tensor of shape [num_instances, height, width] with values
in {0, 1}.
"""
height = keys_to_tensors['image/height']
width = keys_to_tensors['image/width']
to_shape = tf.cast(tf.stack([-1, height, width]), tf.int32)
masks = keys_to_tensors['image/object/mask']
if isinstance(masks, tf.SparseTensor):
masks = tf.sparse_tensor_to_dense(masks)
masks = tf.reshape(
tf.cast(tf.greater(masks, 0.0), dtype=tf.float32), to_shape)
return tf.cast(masks, tf.float32)
示例12: get_sparse_variable
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def get_sparse_variable(name, indices, shape, dtype=None, trainable=True,
initializer=None, partitioner=None, regularizer=None):
n = len(indices)
values = tf.get_variable(name, [n], dtype=dtype,
initializer=initializer, partitioner=partitioner,
regularizer=regularizer, trainable=trainable)
return tf.sparse_reorder(
tf.SparseTensor(indices=indices, values=values, dense_shape=shape))
示例13: sparse_equals_constant
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def sparse_equals_constant(constant, tensor):
return tf.SparseTensor(
indices=tensor.indices,
dense_shape=tensor.dense_shape,
values=tf.equal(tensor.values, constant))
示例14: sparse_expand_dims
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def sparse_expand_dims(tensor, current_num_dims, axis=0):
if axis == -1:
axis = current_num_dims
new_col = tf.zeros([tf.shape(tensor.indices)[0]], dtype=tf.int64)
cols = tf.unstack(tensor.indices, axis=1, num=current_num_dims)
shape = tf.unstack(tensor.dense_shape, num=current_num_dims)
new_indices = tf.stack(cols[:axis] + [new_col] + cols[axis:], axis=1)
return tf.SparseTensor(
indices=new_indices,
values=tensor.values,
dense_shape=tf.stack(shape[:axis] + [1] + shape[axis:]))
示例15: sparse_add_constant
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import SparseTensor [as 别名]
def sparse_add_constant(constant, tensor):
return tf.SparseTensor(
indices=tensor.indices,
values=constant + tensor.values,
dense_shape=tensor.dense_shape)