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tensorflow/python/ops
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# limitations under the License.
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# ==============================================================================
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"""## Control Flow Operations
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TensorFlow provides several operations and classes that you can use to control
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the execution of operations and add conditional dependencies to your graph.
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"""Control Flow Operations. See the @{python/control_flow_ops} guide.
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@@identity
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@@tuple
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@ -26,22 +23,10 @@ the execution of operations and add conditional dependencies to your graph.
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@@cond
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@@case
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@@while_loop
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## Logical Operators
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TensorFlow provides several operations that you can use to add logical operators
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to your graph.
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@@logical_and
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@@logical_not
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@@logical_or
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@@logical_xor
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## Comparison Operators
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TensorFlow provides several operations that you can use to add comparison
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operators to your graph.
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@@equal
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@@not_equal
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@@less
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@ -49,12 +34,6 @@ operators to your graph.
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@@greater
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@@greater_equal
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@@where
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## Debugging Operations
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TensorFlow provides several operations that you can use to validate values and
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debug your graph.
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@@is_finite
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@@is_inf
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@@is_nan
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@ -12,13 +12,10 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""## Script Language Operators.
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TensorFlow provides allows you to wrap python/numpy functions as
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TensorFlow operators.
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"""Script Language Operators. See the @{python/script_ops} guide.
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@@py_func
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"""
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# pylint: disable=g-bad-name
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@ -12,10 +12,8 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""## Tensor Handle Operations.
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TensorFlow provides several operators that allows the user to keep tensors
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"in-place" across run calls.
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"""Tensor Handle Operations. See the @{python/session_ops} guide.
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@@get_session_handle
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@@get_session_tensor
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@ -14,25 +14,14 @@
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# ==============================================================================
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# pylint: disable=g-short-docstring-punctuation
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"""## Sparse Tensor Representation
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TensorFlow supports a `SparseTensor` representation for data that is sparse
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in multiple dimensions. Contrast this representation with `IndexedSlices`,
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which is efficient for representing tensors that are sparse in their first
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dimension, and dense along all other dimensions.
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"""Sparse Tensor Representation. See the @{python/sparse_ops} guide.
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@@SparseTensor
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@@SparseTensorValue
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## Conversion
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@@sparse_to_dense
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@@sparse_tensor_to_dense
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@@sparse_to_indicator
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@@sparse_merge
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## Manipulation
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@@sparse_concat
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@@sparse_reorder
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@@sparse_reshape
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@ -41,18 +30,15 @@ dimension, and dense along all other dimensions.
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@@sparse_reset_shape
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@@sparse_fill_empty_rows
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@@sparse_transpose
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## Reduction
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@@sparse_reduce_sum
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@@sparse_reduce_sum_sparse
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## Math Operations
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@@sparse_add
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@@sparse_softmax
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@@sparse_tensor_dense_matmul
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@@sparse_maximum
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@@sparse_minimum
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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@ -13,46 +13,27 @@
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# limitations under the License.
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# ==============================================================================
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"""## Variables
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"""Variables. See the @{python/state_ops} guide.
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@@Variable
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## Variable helper functions
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TensorFlow provides a set of functions to help manage the set of variables
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collected in the graph.
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@@global_variables
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@@local_variables
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@@model_variables
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@@trainable_variables
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@@moving_average_variables
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@@global_variables_initializer
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@@local_variables_initializer
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@@variables_initializer
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@@is_variable_initialized
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@@report_uninitialized_variables
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@@assert_variables_initialized
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@@assign
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@@assign_add
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@@assign_sub
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## Saving and Restoring Variables
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@@Saver
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@@latest_checkpoint
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@@get_checkpoint_state
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@@update_checkpoint_state
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## Sharing Variables
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TensorFlow provides several classes and operations that you can use to
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create variables contingent on certain conditions.
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@@get_variable
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@@get_local_variable
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@@VariableScope
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@ -60,9 +41,7 @@ create variables contingent on certain conditions.
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@@variable_op_scope
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@@get_variable_scope
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@@make_template
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@@no_regularizer
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@@constant_initializer
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@@random_normal_initializer
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@@truncated_normal_initializer
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@ -71,27 +50,9 @@ create variables contingent on certain conditions.
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@@zeros_initializer
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@@ones_initializer
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@@orthogonal_initializer
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## Variable Partitioners for Sharding
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@@fixed_size_partitioner
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@@variable_axis_size_partitioner
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@@min_max_variable_partitioner
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## Sparse Variable Updates
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The sparse update ops modify a subset of the entries in a dense `Variable`,
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either overwriting the entries or adding / subtracting a delta. These are
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useful for training embedding models and similar lookup-based networks, since
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only a small subset of embedding vectors change in any given step.
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Since a sparse update of a large tensor may be generated automatically during
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gradient computation (as in the gradient of
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[`tf.gather`](../../api_docs/python/array_ops.md#gather)),
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an [`IndexedSlices`](#IndexedSlices) class is provided that encapsulates a set
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of sparse indices and values. `IndexedSlices` objects are detected and handled
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automatically by the optimizers in most cases.
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@@scatter_update
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@@scatter_add
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@@scatter_sub
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@ -102,25 +63,14 @@ automatically by the optimizers in most cases.
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@@scatter_nd_sub
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@@sparse_mask
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@@IndexedSlices
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### Read-only Lookup Tables
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@@initialize_all_tables
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@@tables_initializer
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## Exporting and Importing Meta Graphs
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@@export_meta_graph
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@@import_meta_graph
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# Deprecated functions (removed after 2017-03-02). Please don't use them.
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@@all_variables
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@@initialize_all_variables
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@@initialize_local_variables
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@@initialize_variables
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"""
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from __future__ import absolute_import
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