Python tensorflow.SparseTensorValue方法代碼示例

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def _run_graph(self, sess, qq, hh, tt, mdb, to_fetch):
        feed = {}
        if not self.query_is_language:
            feed[self.queries] = [[q] * (self.num_step-1) + [self.num_query]
                                  for q in qq]
        else:
            feed[self.queries] = [[q] * (self.num_step-1) 
                                  + [[self.num_vocab] * self.num_word]
                                  for q in qq]

        feed[self.heads] = hh 
        feed[self.tails] = tt 
        for r in xrange(self.num_operator / 2):
            feed[self.database[r]] = tf.SparseTensorValue(*mdb[r]) 
        fetches = to_fetch
        graph_output = sess.run(fetches, feed)
        return graph_output 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the shape as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.

  """
  with ops.name_scope(name, "Shape", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops.cast(input.dense_shape, out_type)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.as_list(), out_type, name=name)
      return gen_array_ops.shape(input, name=name, out_type=out_type) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.dense_shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def _get_labels_feed_item(label_list, max_time):
    """
    Generate the tensor from 'label_list' to feed as labels into the network

    Args:
      label_list: a list of encoded labels (ints)
      max_time: the maximum time length of `label_list`

    Returns: the SparseTensorValue to feed into the network

    """

    label_shape = np.array([len(label_list), max_time], dtype=np.int)
    label_indices = []
    label_values = []
    for labelIdx, label in enumerate(label_list):
      for idIdx, identifier in enumerate(label):
        label_indices.append([labelIdx, idIdx])
        label_values.append(identifier)
    label_indices = np.array(label_indices, dtype=np.int)
    label_values = np.array(label_values, dtype=np.int)
    return tf.SparseTensorValue(label_indices, label_values, label_shape) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def output_s(self):
        batch_num = int(self.test_data_amt / self.batch_size)
        output = np.ones([self.batch_size, 300])
        for i in range(batch_num):
            x_batch_field, b_batch, z_batch, y_batch, all_prices = self.util_test.get_batch_data_sorted_dwpp(i)
            feed_dict = {}
            for j in range(len(self.X)):
                feed_dict[self.X[j]] = tf.SparseTensorValue(x_batch_field[j], [1] * len(x_batch_field[j]),
                                                                  [self.batch_size, self.field_sizes[j]])
            feed_dict[self.b] = b_batch
            feed_dict[self.z] = z_batch
            feed_dict[self.y] = y_batch
            feed_dict[self.all_prices] = all_prices
            output = np.vstack([output, self.sess.run(self.w_all, feed_dict)])
        print(output.shape)
        np.savetxt(self.output_dir + 's.txt', 1 - output[self.batch_size:,], delimiter='\t', fmt='%.4f') 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def convert2SparseTensorValue(list_labels):
    #
    # list_labels: batch_major
    #
   
    #
    num_samples = len(list_labels)
    num_maxlen = max(map(lambda x: len(x), list_labels))
    #
    indices = []
    values = []
    shape = [num_samples, num_maxlen]
    #
    for idx in range(num_samples):
        #
        item = list_labels[idx]
        #
        values.extend(item)
        indices.extend([[idx, posi] for posi in range(len(item))])    
        #
    #
    return tf.SparseTensorValue(indices = indices, values = values, dense_shape = shape)
    #
# 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def to_sparse_tensor(M, value=False):
    """Convert a scipy sparse matrix to a tf SparseTensor or SparseTensorValue.

    Parameters
    ----------
    M : scipy.sparse.sparse
        Matrix in Scipy sparse format.
    value : bool, default False
        Convert to tf.SparseTensorValue if True, else to tf.SparseTensor.

    Returns
    -------
    S : tf.SparseTensor or tf.SparseTensorValue
        Matrix as a sparse tensor.

    Author: Oleksandr Shchur
    """
    M = sp.coo_matrix(M)
    if value:
        return tf.SparseTensorValue(np.vstack((M.row, M.col)).T, M.data, M.shape)
    else:
        return tf.SparseTensor(np.vstack((M.row, M.col)).T, M.data, M.shape) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def _SparseTensorValue_3x50(self, indices_dtype, values_dtype):
    # NOTE: This input is intentionally not sorted to validate the
    # already_sorted flag below.
    ind = np.array([
        [0, 0],
        [1, 0], [1, 2],
        [2, 0], [2, 1],
        [1, 1]])
    # NB: these are not sorted
    indices = np.array([0, 13, 10, 33, 32, 14])
    values = np.array([-3, 4, 1, 9, 5, 1])
    shape = np.array([3, 3])
    indices = tf.SparseTensorValue(
        np.array(ind, np.int64),
        np.array(indices, indices_dtype),
        np.array(shape, np.int64))
    values = tf.SparseTensorValue(
        np.array(ind, np.int64),
        np.array(values, values_dtype),
        np.array(shape, np.int64))
    return indices, values 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the shape as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.

  """
  with ops.name_scope(name, "Shape", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops.cast(input.shape, out_type)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.as_list(), out_type, name=name)
      return gen_array_ops.shape(input, name=name, out_type=out_type) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def clip_last_batch(self, last_batch, true_size):
    """This method performs last batch clipping.
    Used in cases when dataset is not divisible by the batch size and model
    does not support dynamic batch sizes. In those cases, the last batch will
    contain some data from the "next epoch" and this method can be used
    to remove that data. This method works for both
    dense and sparse tensors. In most cases you will not need to overwrite this
    method.

    Args:
      last_batch (list): list with elements that could be either ``np.array``
          or ``tf.SparseTensorValue`` containing data for last batch. The
          assumption is that the first axis of all data tensors will correspond
          to the current batch size.
      true_size (int): true size that the last batch should be cut to.
    """
    return clip_last_batch(last_batch, true_size) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def make_sparse(sequences, domain_length, peptide_length, n_amino_acids):
    def _sparsify(arr, ncols):
        nrows = arr.shape[0]

        ridxes, compressed_cidxes = np.where(arr >= 0)
        cidxes = arr[ridxes, compressed_cidxes]
        
        vals = np.ones(ridxes.size)
        
        idxes = np.vstack([ridxes, cidxes]).T
         
        shape = [nrows, ncols]
        
        return tf.SparseTensorValue(
                    indices = idxes, 
                    values = vals,
                    dense_shape = shape
                )
    
    col_sizes = [domain_length * n_amino_acids, peptide_length * n_amino_acids, domain_length * peptide_length * n_amino_acids * n_amino_acids]
    return [_sparsify(m, ncols) for m, ncols in zip(sequences, col_sizes)] 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(input, (sparse_tensor.SparseTensor,
                          sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import SparseTensorValue [as 別名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(input, (sparse_tensor.SparseTensor,
                          sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.dense_shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name)