Python tensorflow.python.ops.math_ops._as_indexed_slices() Examples
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code examples of tensorflow.python.ops.math_ops._as_indexed_slices().
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Example #1
| Source File: control_flow_ops.py From lambda-packs with MIT License | 5 votes |
|
def _AddNextAndBackEdge(m, v):
"""Add NextIteration and back edge from v to m."""
if isinstance(m, ops.Tensor):
v = ops.convert_to_tensor(v)
v = _NextIteration(v)
m.op._update_input(1, v) # pylint: disable=protected-access
elif isinstance(m, ops.IndexedSlices):
# pylint: disable=protected-access
v = math_ops._as_indexed_slices(v, optimize=False)
v = _NextIteration(v)
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
# pylint: enable=protected-access
if m.dense_shape is not None:
if v.dense_shape is None:
raise ValueError("Must have dense shape: %s" % v.name)
m.dense_shape.op._update_input(1, v.dense_shape)
elif isinstance(m, sparse_tensor.SparseTensor):
if not isinstance(v, sparse_tensor.SparseTensor):
raise ValueError("Must be a sparse tensor: %s" % v.name)
v = _NextIteration(v)
# pylint: disable=protected-access
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
m.dense_shape.op._update_input(1, v.dense_shape)
# pylint: enable=protected-access
else:
raise TypeError("Type %s not supported" % type(m))
return v
Example #2
| Source File: control_flow_ops.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _AddNextAndBackEdge(m, v):
"""Add NextIteration and back edge from v to m."""
if isinstance(m, ops.Tensor):
v = ops.convert_to_tensor(v)
v = _NextIteration(v)
m.op._update_input(1, v) # pylint: disable=protected-access
elif isinstance(m, ops.IndexedSlices):
# pylint: disable=protected-access
v = math_ops._as_indexed_slices(v, optimize=False)
v = _NextIteration(v)
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
# pylint: enable=protected-access
if m.dense_shape is not None:
if v.dense_shape is None:
raise ValueError("Must have dense shape: %s" % v.name)
m.dense_shape.op._update_input(1, v.dense_shape)
elif isinstance(m, sparse_tensor.SparseTensor):
if not isinstance(v, sparse_tensor.SparseTensor):
raise ValueError("Must be a sparse tensor: %s" % v.name)
v = _NextIteration(v)
# pylint: disable=protected-access
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
m.dense_shape.op._update_input(1, v.dense_shape)
# pylint: enable=protected-access
else:
raise TypeError("Type %s not supported" % type(m))
return v
Example #3
| Source File: control_flow_ops.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def _AddNextAndBackEdge(m, v):
"""Add NextIteration and back edge from v to m."""
if isinstance(m, ops.Tensor):
v = ops.convert_to_tensor(v)
v = _NextIteration(v)
m.op._update_input(1, v) # pylint: disable=protected-access
elif isinstance(m, ops.IndexedSlices):
# pylint: disable=protected-access
v = math_ops._as_indexed_slices(v, optimize=False)
v = _NextIteration(v)
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
# pylint: enable=protected-access
if m.dense_shape is not None:
if v.dense_shape is None:
raise ValueError("Must have dense shape: %s" % v.name)
m.dense_shape.op._update_input(1, v.dense_shape)
elif isinstance(m, sparse_tensor.SparseTensor):
if not isinstance(v, sparse_tensor.SparseTensor):
raise ValueError("Must be a sparse tensor: %s" % v.name)
v = _NextIteration(v)
# pylint: disable=protected-access
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
m.shape.op._update_input(1, v.shape)
# pylint: enable=protected-access
else:
raise TypeError("Type %s not supported" % type(m))
return v
Example #4
| Source File: gradients_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testIndexedSlicesToTensor(self):
with self.test_session():
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
self.assertAllEqual(np_val.shape, c_sparse.dense_shape.eval())
c_dense = math_ops.mul(c_sparse, 1.0)
self.assertAllClose(np_val, c_dense.eval())
Example #5
| Source File: gradients_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testIndexedSlicesToTensorList(self):
with self.test_session():
numpy_list = []
dense_list = []
sparse_list = []
for _ in range(3):
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
numpy_list.append(np_val)
dense_list.append(c)
sparse_list.append(c_sparse)
packed_dense = array_ops.pack(dense_list)
packed_sparse = array_ops.pack(sparse_list)
self.assertAllClose(packed_dense.eval(), packed_sparse.eval())
Example #6
| Source File: gradients_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testInt64Indices(self):
with self.test_session():
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
c_sparse = ops.IndexedSlices(
c_sparse.values, math_ops.cast(c_sparse.indices, dtypes.int64),
c_sparse.dense_shape)
self.assertAllEqual(np_val.shape, c_sparse.dense_shape.eval())
c_dense = math_ops.mul(c_sparse, 1.0)
self.assertAllClose(np_val, c_dense.eval())
Example #7
| Source File: control_flow_ops.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _AddNextAndBackEdge(m, v):
"""Add NextIteration and back edge from v to m."""
if isinstance(m, ops.Tensor):
v = ops.convert_to_tensor(v)
v = _NextIteration(v)
m.op._update_input(1, v) # pylint: disable=protected-access
elif isinstance(m, ops.IndexedSlices):
# pylint: disable=protected-access
v = math_ops._as_indexed_slices(v, optimize=False)
v = _NextIteration(v)
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
# pylint: enable=protected-access
if m.dense_shape is not None:
if v.dense_shape is None:
raise ValueError("Must have dense shape: %s" % v.name)
m.dense_shape.op._update_input(1, v.dense_shape)
elif isinstance(m, sparse_tensor.SparseTensor):
if not isinstance(v, sparse_tensor.SparseTensor):
raise ValueError("Must be a sparse tensor: %s" % v.name)
v = _NextIteration(v)
# pylint: disable=protected-access
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
m.dense_shape.op._update_input(1, v.dense_shape)
# pylint: enable=protected-access
else:
raise TypeError("Type %s not supported" % type(m))
return v
Example #8
| Source File: control_flow_ops.py From keras-lambda with MIT License | 5 votes |
|
def _AddNextAndBackEdge(m, v):
"""Add NextIteration and back edge from v to m."""
if isinstance(m, ops.Tensor):
v = ops.convert_to_tensor(v)
v = _NextIteration(v)
m.op._update_input(1, v) # pylint: disable=protected-access
elif isinstance(m, ops.IndexedSlices):
# pylint: disable=protected-access
v = math_ops._as_indexed_slices(v, optimize=False)
v = _NextIteration(v)
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
# pylint: enable=protected-access
if m.dense_shape is not None:
if v.dense_shape is None:
raise ValueError("Must have dense shape: %s" % v.name)
m.dense_shape.op._update_input(1, v.dense_shape)
elif isinstance(m, sparse_tensor.SparseTensor):
if not isinstance(v, sparse_tensor.SparseTensor):
raise ValueError("Must be a sparse tensor: %s" % v.name)
v = _NextIteration(v)
# pylint: disable=protected-access
m.values.op._update_input(1, v.values)
m.indices.op._update_input(1, v.indices)
m.dense_shape.op._update_input(1, v.dense_shape)
# pylint: enable=protected-access
else:
raise TypeError("Type %s not supported" % type(m))
return v
