Python tensorflow.python.ops.data_flow_ops.dynamic_stitch() Examples

The following are 9 code examples of tensorflow.python.ops.data_flow_ops.dynamic_stitch(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.data_flow_ops , or try the search function .
Example #1
Source File: data_flow_grad.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _DynamicPartitionGrads(op, *grads):
  """Gradients for DynamicPartition."""
  data = op.inputs[0]
  indices = op.inputs[1]
  num_partitions = op.get_attr("num_partitions")

  prefix_shape = array_ops.shape(indices)
  original_indices = array_ops.reshape(
      math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
  partitioned_indices = data_flow_ops.dynamic_partition(
      original_indices, indices, num_partitions)
  reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)
  reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
  return [reconstructed, None]
Example #2
Source File: sharded_mutable_dense_hashtable.py    From lambda-packs with MIT License 5 votes vote down vote up 
def lookup(self, keys, name=None):
    if keys.dtype != self._key_dtype:
      raise TypeError('Signature mismatch. Keys must be dtype %s, got %s.' %
                      (self._key_dtype, keys.dtype))
    self._check_keys(keys)
    num_shards = self._num_shards
    if num_shards == 1:
      return self._table_shards[0].lookup(keys, name=name)

    shard_indices = self._shard_indices(keys)
    # TODO(andreasst): support 'keys' that are not vectors
    key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
                                                 num_shards)
    value_shards = [
        self._table_shards[i].lookup(key_shards[i], name=name)
        for i in range(num_shards)
    ]

    num_keys = keys.get_shape().dims[0]
    original_indices = math_ops.range(num_keys)
    partitioned_indices = data_flow_ops.dynamic_partition(original_indices,
                                                          shard_indices,
                                                          num_shards)
    result = data_flow_ops.dynamic_stitch(partitioned_indices, value_shards)
    result.set_shape(
        tensor_shape.TensorShape([num_keys]).concatenate(self._value_shape))
    return result
Example #3
Source File: data_flow_grad.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _DynamicPartitionGrads(op, *grads):
  """Gradients for DynamicPartition."""
  data = op.inputs[0]
  indices = op.inputs[1]
  num_partitions = op.get_attr("num_partitions")

  prefix_shape = array_ops.shape(indices)
  original_indices = array_ops.reshape(
      math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
  partitioned_indices = data_flow_ops.dynamic_partition(
      original_indices, indices, num_partitions)
  reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)
  reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
  return [reconstructed, None]
Example #4
Source File: sharded_mutable_dense_hashtable.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def lookup(self, keys, name=None):
    if keys.dtype != self._key_dtype:
      raise TypeError('Signature mismatch. Keys must be dtype %s, got %s.' %
                      (self._key_dtype, keys.dtype))
    self._check_keys(keys)
    num_shards = self._num_shards
    if num_shards == 1:
      return self._table_shards[0].lookup(keys, name=name)

    shard_indices = self._shard_indices(keys)
    # TODO(andreasst): support 'keys' that are not vectors
    key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
                                                 num_shards)
    value_shards = [
        self._table_shards[i].lookup(key_shards[i], name=name)
        for i in range(num_shards)
    ]

    num_keys = keys.get_shape().dims[0]
    original_indices = math_ops.range(num_keys)
    partitioned_indices = data_flow_ops.dynamic_partition(original_indices,
                                                          shard_indices,
                                                          num_shards)
    result = data_flow_ops.dynamic_stitch(partitioned_indices, value_shards)
    result.set_shape(
        tensor_shape.TensorShape([num_keys]).concatenate(self._value_shape))
    return result
Example #5
Source File: data_flow_grad.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _DynamicPartitionGrads(op, *grads):
  """Gradients for DynamicPartition."""
  data = op.inputs[0]
  indices = op.inputs[1]
  num_partitions = op.get_attr("num_partitions")

  prefix_shape = array_ops.shape(indices)
  original_indices = array_ops.reshape(
      math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
  partitioned_indices = data_flow_ops.dynamic_partition(
      original_indices, indices, num_partitions)
  reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)
  reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
  return [reconstructed, None]
Example #6
Source File: sdca_ops.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def lookup(self, keys, name=None):
    if keys.dtype != self._key_dtype:
      raise TypeError('Signature mismatch. Keys must be dtype %s, got %s.' %
                      (self._key_dtype, keys.dtype))
    self._check_keys(keys)
    num_shards = self._num_shards
    if num_shards == 1:
      return self._table_shards[0].lookup(keys, name=name)

    shard_indices = self._shard_indices(keys)
    # TODO(andreasst): support 'keys' that are not vectors
    key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
                                                 num_shards)
    value_shards = [
        self._table_shards[i].lookup(key_shards[i], name=name)
        for i in range(num_shards)
    ]

    num_keys = keys.get_shape().dims[0]
    original_indices = math_ops.range(num_keys)
    partitioned_indices = data_flow_ops.dynamic_partition(original_indices,
                                                          shard_indices,
                                                          num_shards)
    result = data_flow_ops.dynamic_stitch(partitioned_indices, value_shards)
    result.set_shape(
        tensor_shape.TensorShape([num_keys]).concatenate(self._value_shape))
    return result
Example #7
Source File: data_flow_grad.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def _DynamicPartitionGrads(op, *grads):
  """Gradients for DynamicPartition."""
  data = op.inputs[0]
  indices = op.inputs[1]
  num_partitions = op.get_attr("num_partitions")

  prefix_shape = array_ops.shape(indices)
  original_indices = array_ops.reshape(
      math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
  partitioned_indices = data_flow_ops.dynamic_partition(
      original_indices, indices, num_partitions)
  reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)
  reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
  return [reconstructed, None]
Example #8
Source File: data_flow_grad.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _DynamicPartitionGrads(op, *grads):
  """Gradients for DynamicPartition."""
  data = op.inputs[0]
  indices = op.inputs[1]
  num_partitions = op.get_attr("num_partitions")

  prefix_shape = array_ops.shape(indices)
  original_indices = array_ops.reshape(
      math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
  partitioned_indices = data_flow_ops.dynamic_partition(
      original_indices, indices, num_partitions)
  reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)
  reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
  return [reconstructed, None]
Example #9
Source File: sharded_mutable_dense_hashtable.py    From keras-lambda with MIT License 5 votes vote down vote up 
def lookup(self, keys, name=None):
    if keys.dtype != self._key_dtype:
      raise TypeError('Signature mismatch. Keys must be dtype %s, got %s.' %
                      (self._key_dtype, keys.dtype))
    self._check_keys(keys)
    num_shards = self._num_shards
    if num_shards == 1:
      return self._table_shards[0].lookup(keys, name=name)

    shard_indices = self._shard_indices(keys)
    # TODO(andreasst): support 'keys' that are not vectors
    key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
                                                 num_shards)
    value_shards = [
        self._table_shards[i].lookup(key_shards[i], name=name)
        for i in range(num_shards)
    ]

    num_keys = keys.get_shape().dims[0]
    original_indices = math_ops.range(num_keys)
    partitioned_indices = data_flow_ops.dynamic_partition(original_indices,
                                                          shard_indices,
                                                          num_shards)
    result = data_flow_ops.dynamic_stitch(partitioned_indices, value_shards)
    result.set_shape(
        tensor_shape.TensorShape([num_keys]).concatenate(self._value_shape))
    return result