Python tensorflow.python.ops.array_ops.sparse_placeholder() Examples

def make_place_holder_tensors_for_base_features(feature_columns):
  """Returns placeholder tensors for inference.

  Args:
    feature_columns: An iterable containing all the feature columns. All items
      should be instances of classes derived from _FeatureColumn.
  Returns:
    A dict mapping feature keys to SparseTensors (sparse columns) or
    placeholder Tensors (dense columns).
  """
  # Get dict mapping features to FixedLenFeature or VarLenFeature values.
  dict_for_parse_example = create_feature_spec_for_parsing(feature_columns)
  placeholders = {}
  for column_name, column_type in dict_for_parse_example.items():
    if isinstance(column_type, parsing_ops.VarLenFeature):
      # Sparse placeholder for sparse tensors.
      placeholders[column_name] = array_ops.sparse_placeholder(
          column_type.dtype, name="Placeholder_{}".format(column_name))
    else:
      # Simple placeholder for dense tensors.
      placeholders[column_name] = array_ops.placeholder(
          column_type.dtype,
          shape=(None, column_type.shape[0]),
          name="Placeholder_{}".format(column_name))
  return placeholders 

def make_place_holder_tensors_for_base_features(feature_columns):
  """Returns placeholder tensors for inference.

  Args:
    feature_columns: An iterable containing all the feature columns. All items
      should be instances of classes derived from _FeatureColumn.
  Returns:
    A dict mapping feature keys to SparseTensors (sparse columns) or
    placeholder Tensors (dense columns).
  """
  # Get dict mapping features to FixedLenFeature or VarLenFeature values.
  dict_for_parse_example = create_feature_spec_for_parsing(feature_columns)
  placeholders = {}
  for column_name, column_type in dict_for_parse_example.items():
    if isinstance(column_type, parsing_ops.VarLenFeature):
      # Sparse placeholder for sparse tensors.
      placeholders[column_name] = array_ops.sparse_placeholder(
          column_type.dtype, name="Placeholder_{}".format(column_name))
    else:
      # Simple placeholder for dense tensors.
      placeholders[column_name] = array_ops.placeholder(
          column_type.dtype,
          shape=(None, column_type.shape[0]),
          name="Placeholder_{}".format(column_name))
  return placeholders 

def make_place_holder_tensors_for_base_features(feature_columns):
  """Returns placeholder tensors for inference.

  Args:
    feature_columns: An iterable containing all the feature columns. All items
      should be instances of classes derived from _FeatureColumn.
  Returns:
    A dict mapping feature keys to SparseTensors (sparse columns) or
    placeholder Tensors (dense columns).
  """
  # Get dict mapping features to FixedLenFeature or VarLenFeature values.
  dict_for_parse_example = create_feature_spec_for_parsing(feature_columns)
  placeholders = {}
  for column_name, column_type in dict_for_parse_example.items():
    if isinstance(column_type, parsing_ops.VarLenFeature):
      # Sparse placeholder for sparse tensors.
      placeholders[column_name] = array_ops.sparse_placeholder(
          column_type.dtype, name="Placeholder_{}".format(column_name))
    else:
      # Simple placeholder for dense tensors.
      placeholders[column_name] = array_ops.placeholder(
          column_type.dtype,
          shape=(None, column_type.shape[0]),
          name="Placeholder_{}".format(column_name))
  return placeholders 

def test_sparse_multi_rank(self):
    wire_cast = fc.categorical_column_with_hash_bucket('wire_cast', 4)
    with ops.Graph().as_default():
      wire_tensor = array_ops.sparse_placeholder(dtypes.string)
      wire_value = sparse_tensor.SparseTensorValue(
          values=['omar', 'stringer', 'marlo', 'omar'],  # hashed = [2, 0, 3, 2]
          indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 0, 1]],
          dense_shape=[2, 2, 2])
      features = {'wire_cast': wire_tensor}
      model = linear.LinearModel([wire_cast])
      predictions = model(features)
      wire_cast_var, _ = model.variables
      with _initialized_session() as sess:
        self.assertAllClose(np.zeros((4, 1)), self.evaluate(wire_cast_var))
        self.assertAllClose(
            np.zeros((2, 1)),
            predictions.eval(feed_dict={wire_tensor: wire_value}))
        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
        self.assertAllClose(
            [[1010.], [11000.]],
            predictions.eval(feed_dict={wire_tensor: wire_value})) 

def make_place_holder_tensors_for_base_features(feature_columns):
  """Returns placeholder tensors for inference.

  Args:
    feature_columns: An iterable containing all the feature columns. All items
      should be instances of classes derived from _FeatureColumn.
  Returns:
    A dict mapping feature keys to SparseTensors (sparse columns) or
    placeholder Tensors (dense columns).
  """
  # Get dict mapping features to FixedLenFeature or VarLenFeature values.
  dict_for_parse_example = create_feature_spec_for_parsing(feature_columns)
  placeholders = {}
  for column_name, column_type in dict_for_parse_example.items():
    if isinstance(column_type, parsing_ops.VarLenFeature):
      # Sparse placeholder for sparse tensors.
      placeholders[column_name] = array_ops.sparse_placeholder(
          column_type.dtype, name="Placeholder_{}".format(column_name))
    else:
      # Simple placeholder for dense tensors.
      placeholders[column_name] = array_ops.placeholder(
          column_type.dtype,
          shape=(None, column_type.shape[0]),
          name="Placeholder_{}".format(column_name))
  return placeholders 

def testFeedSparsePlaceholderConstantShape(self):
    with session.Session() as s:
      indices = np.array([[3, 2, 0], [4, 5, 1]]).astype(np.int64)
      values = np.array([1.0, 2.0]).astype(np.float32)
      shape = np.array([7, 9, 2]).astype(np.int64)
      sp = array_ops.sparse_placeholder(dtype=np.float32,
                                        shape=shape,
                                        name='placeholder1')
      self.assertAllEqual(sp.shape.eval(session=s), shape)
      self.assertAllEqual(tensor_util.constant_value(sp.shape), shape)
      sp_indices = array_ops.identity(sp.indices)
      sp_values = array_ops.identity(sp.values)
      sp_shape = array_ops.identity(sp.shape)
      # Feed with tuple
      indices_out, values_out, shape_out = s.run(
          [sp_indices, sp_values, sp_shape], {sp: (indices, values)})
      self.assertAllEqual(indices_out, indices)
      self.assertAllEqual(values_out, values)
      self.assertAllEqual(shape_out, shape) 

def testFeedSparsePlaceholderPartialShape(self):
    with session.Session() as s:
      indices = np.array([[3, 2, 0], [4, 5, 1]]).astype(np.int64)
      values = np.array([1.0, 2.0]).astype(np.float32)
      shape = np.array([7, 9, 2]).astype(np.int64)
      sp = array_ops.sparse_placeholder(
          shape=[None, 9, 2], dtype=np.float32, name='placeholder1')
      sp_indices = array_ops.identity(sp.indices)
      sp_values = array_ops.identity(sp.values)
      sp_shape = array_ops.identity(sp.shape)
      sp2 = sparse_tensor.SparseTensor(sp_indices, sp_values, sp_shape)
      # Feed with tuple
      indices_out, values_out, shape_out = s.run(
          [sp_indices, sp_values, sp_shape], {sp: (indices, values, shape)})
      self.assertAllEqual(indices_out, indices)
      self.assertAllEqual(values_out, values)
      self.assertAllEqual(shape_out, shape)
      # Feed with SparseTensorValue
      indices_out, values_out, shape_out = s.run(
          [sp_indices, sp_values, sp_shape],
          {sp: sparse_tensor.SparseTensorValue(indices, values, shape)})
      self.assertAllEqual(indices_out, indices)
      self.assertAllEqual(values_out, values)
      self.assertAllEqual(shape_out, shape)
      # Feed with SparseTensorValue, fetch SparseTensorValue
      sp2_out = s.run(
          sp2, {sp: sparse_tensor.SparseTensorValue(indices, values, shape)})
      self.assertAllEqual(sp2_out.indices, indices)
      self.assertAllEqual(sp2_out.values, values)
      self.assertAllEqual(sp2_out.shape, shape) 

def get_placeholder(self):
    if self.is_sparse:
      return array_ops.sparse_placeholder(dtype=self.dtype)
    return array_ops.placeholder(dtype=self.dtype,
                                 shape=[None] + list(self.shape[1:])) 

def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
  """Instantiates a placeholder tensor and returns it.

  Arguments:
      shape: Shape of the placeholder
          (integer tuple, may include `None` entries).
      ndim: Number of axes of the tensor.
          At least one of {`shape`, `ndim`} must be specified.
          If both are specified, `shape` is used.
      dtype: Placeholder type.
      sparse: Boolean, whether the placeholder should have a sparse type.
      name: Optional name string for the placeholder.

  Returns:
      Tensor instance (with Keras metadata included).

  Examples:
  ```python
      >>> from keras import backend as K
      >>> input_ph = K.placeholder(shape=(2, 4, 5))
      >>> input_ph
      <tf.Tensor 'Placeholder_4:0' shape=(2, 4, 5) dtype=float32>
  ```
  """
  if dtype is None:
    dtype = floatx()
  if not shape:
    if ndim:
      shape = tuple([None for _ in range(ndim)])
  if sparse:
    x = array_ops.sparse_placeholder(dtype, shape=shape, name=name)
  else:
    x = array_ops.placeholder(dtype, shape=shape, name=name)
  x._uses_learning_phase = False
  return x 

def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
  """Instantiates a placeholder tensor and returns it.

  Arguments:
      shape: Shape of the placeholder
          (integer tuple, may include `None` entries).
      ndim: Number of axes of the tensor.
          At least one of {`shape`, `ndim`} must be specified.
          If both are specified, `shape` is used.
      dtype: Placeholder type.
      sparse: Boolean, whether the placeholder should have a sparse type.
      name: Optional name string for the placeholder.

  Returns:
      Tensor instance (with Keras metadata included).

  Examples:
  ```python
      >>> from keras import backend as K
      >>> input_ph = K.placeholder(shape=(2, 4, 5))
      >>> input_ph
      <tf.Tensor 'Placeholder_4:0' shape=(2, 4, 5) dtype=float32>
  ```
  """
  if dtype is None:
    dtype = floatx()
  if not shape:
    if ndim:
      shape = tuple([None for _ in range(ndim)])
  if sparse:
    x = array_ops.sparse_placeholder(dtype, shape=shape, name=name)
  else:
    x = array_ops.placeholder(dtype, shape=shape, name=name)
  x._uses_learning_phase = False
  return x 

def testFeedSparsePlaceholder(self):
    with session.Session() as s:
      indices = np.array([[3, 2, 0], [4, 5, 1]]).astype(np.int64)
      values = np.array([1.0, 2.0]).astype(np.float32)
      shape = np.array([7, 9, 2]).astype(np.int64)
      sp = array_ops.sparse_placeholder(dtype=np.float32, name='placeholder1')
      sp_indices = array_ops.identity(sp.indices)
      sp_values = array_ops.identity(sp.values)
      sp_shape = array_ops.identity(sp.shape)
      sp2 = sparse_tensor.SparseTensor(sp_indices, sp_values, sp_shape)
      # Feed with tuple
      indices_out, values_out, shape_out = s.run(
          [sp_indices, sp_values, sp_shape], {sp: (indices, values, shape)})
      self.assertAllEqual(indices_out, indices)
      self.assertAllEqual(values_out, values)
      self.assertAllEqual(shape_out, shape)
      # Feed with SparseTensorValue
      indices_out, values_out, shape_out = s.run(
          [sp_indices, sp_values, sp_shape],
          {sp: sparse_tensor.SparseTensorValue(indices, values, shape)})
      self.assertAllEqual(indices_out, indices)
      self.assertAllEqual(values_out, values)
      self.assertAllEqual(shape_out, shape)
      # Feed with SparseTensorValue, fetch SparseTensorValue
      sp2_out = s.run(
          sp2, {sp: sparse_tensor.SparseTensorValue(indices, values, shape)})
      self.assertAllEqual(sp2_out.indices, indices)
      self.assertAllEqual(sp2_out.values, values)
      self.assertAllEqual(sp2_out.shape, shape) 

def testInvalidDimensionSizeInputUnavailableInGraphConstruction(self):
    sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
    with self.test_session(use_gpu=False) as sess:
      new_shape = np.array([3, 7, 5], dtype=np.int64)
      out = sparse_ops.sparse_reset_shape(sp_input, new_shape)

      with self.assertRaisesOpError("x <= y did not hold element-wise"):
        sess.run(out, feed_dict={sp_input: self._SparseTensorValue_2x5x6()}) 

def testFeedInputUnavailableInGraphConstructionOk(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
      new_shape = np.array([3, 6, 7], dtype=np.int64)
      sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)

      output = sess.run(sp_output,
                        feed_dict={sp_input: self._SparseTensorValue_2x5x6()})

      self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0],
                                           [0, 1, 3], [1, 1, 4],
                                           [1, 3, 2], [1, 3, 3]])
      self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
      self.assertAllEqual(output.shape, [3, 6, 7]) 

def get_placeholder(self):
    if self.is_sparse:
      return array_ops.sparse_placeholder(dtype=self.dtype)
    return array_ops.placeholder(dtype=self.dtype,
                                 shape=[None] + list(self.shape[1:])) 

def get_placeholder(self):
    if self.is_sparse:
      return array_ops.sparse_placeholder(dtype=self.dtype)
    return array_ops.placeholder(dtype=self.dtype,
                                 shape=[None] + list(self.shape[1:])) 

def test_with_1d_unknown_shape_sparse_tensor(self):
    price = fc.numeric_column('price')
    price_buckets = fc.bucketized_column(
        price, boundaries=[
            0.,
            10.,
            100.,
        ])
    body_style = fc.categorical_column_with_vocabulary_list(
        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
    country = fc.categorical_column_with_vocabulary_list(
        'country', vocabulary_list=['US', 'JP', 'CA'])

    # Provides 1-dim tensor and dense tensor.
    features = {
        'price': array_ops.placeholder(dtypes.float32),
        'body-style': array_ops.sparse_placeholder(dtypes.string),
        'country': array_ops.placeholder(dtypes.string),
    }
    self.assertIsNone(features['price'].shape.ndims)
    self.assertIsNone(features['body-style'].get_shape().ndims)

    price_data = np.array([-1., 12.])
    body_style_data = sparse_tensor.SparseTensorValue(
        indices=((0,), (1,)), values=('sedan', 'hardtop'), dense_shape=(2,))
    country_data = np.array(['US', 'CA'])

    model = linear.LinearModel([price_buckets, body_style, country])
    net = model(features)
    body_style_var, _, price_buckets_var, bias = model.variables
    with _initialized_session() as sess:
      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
      sess.run(bias.assign([5.]))

      self.assertAllClose([[10 - 1000 + 5.], [1000 - 10 + 5.]],
                          sess.run(
                              net,
                              feed_dict={
                                  features['price']: price_data,
                                  features['body-style']: body_style_data,
                                  features['country']: country_data
                              })) 

def __init__(self,
               input_shape=None,
               batch_size=None,
               dtype=dtypes.float32,
               input_tensor=None,
               sparse=False,
               name=None):
    if context.in_eager_mode():
      raise RuntimeError('InputLayer not supported in Eager mode.')
    super(InputLayer, self).__init__(dtype=dtype, name=name)
    self.built = True
    self.sparse = sparse
    self.batch_size = batch_size

    if isinstance(input_shape, tensor_shape.TensorShape):
      input_shape = tuple(input_shape.as_list())

    if input_tensor is None:
      if input_shape is not None:
        batch_input_shape = (batch_size,) + tuple(input_shape)
      else:
        batch_input_shape = None

      if sparse:
        input_tensor = array_ops.sparse_placeholder(
            shape=batch_input_shape,
            dtype=dtype,
            name=self.name)
      else:
        input_tensor = array_ops.placeholder(
            shape=batch_input_shape,
            dtype=dtype,
            name=self.name)

      # For compatibility with Keras API.
      self.is_placeholder = True
      self._batch_input_shape = batch_input_shape
    else:
      # For compatibility with Keras API.
      self.is_placeholder = False
      self._batch_input_shape = tuple(input_tensor.get_shape().as_list())

    # Create an input node to add to self.outbound_node
    # and set output_tensors' _keras_history.
    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
    Node(
        self,
        inbound_layers=[],
        node_indices=[],
        tensor_indices=[],
        input_tensors=[input_tensor],
        output_tensors=[input_tensor])