tensorflow_probability/examples/latent_dirichlet_allocation_edward2.py
def newsgroups_dataset(directory, split_name, num_words, shuffle_and_repeat):
  """20 newsgroups as a tf.data.Dataset."""
  data = np.load(download(directory, FILE_TEMPLATE.format(split=split_name)))
  # The last row is empty in both train and test.
  data = data[:-1]

  # Each row is a list of word ids in the document. We first convert this to
  # sparse COO matrix (which automatically sums the repeating words). Then,
  # we convert this COO matrix to CSR format which allows for fast querying of
  # documents.
  num_documents = data.shape[0]
  indices = np.array([(row_idx, column_idx)
                      for row_idx, row in enumerate(data)
                      for column_idx in row])
  sparse_matrix = scipy.sparse.coo_matrix(
      (np.ones(indices.shape[0]), (indices[:, 0], indices[:, 1])),
      shape=(num_documents, num_words),
      dtype=np.float32)
  sparse_matrix = sparse_matrix.tocsr()

  dataset = tf.data.Dataset.range(num_documents)

  # For training, we shuffle each epoch and repeat the epochs.
  if shuffle_and_repeat:
    dataset = dataset.shuffle(num_documents).repeat()

  # Returns a single document as a dense TensorFlow tensor. The dataset is
  # stored as a sparse matrix outside of the graph.
  def get_row_py_func(idx):
    def get_row_python(idx_py):
      return np.squeeze(np.array(sparse_matrix[idx_py].todense()), axis=0)

    py_func = tf.compat.v1.py_func(
        get_row_python, [idx], tf.float32, stateful=False)
    py_func.set_shape((num_words,))
    return py_func

  dataset = dataset.map(get_row_py_func)
  return dataset
def newsgroups_dataset(directory, split_name, num_words, shuffle_and_repeat):
  data = np.load(download(directory, FILE_TEMPLATE.format(split=split_name)))
  # The last row is empty in both train and test.
  data = data[:-1]

  # Each row is a list of word ids in the document. We first convert this to
  # sparse COO matrix (which automatically sums the repeating words). Then,
  # we convert this COO matrix to CSR format which allows for fast querying of
  # documents.
  num_documents = data.shape[0]
  indices = np.array([(row_idx, column_idx)
                      for row_idx, row in enumerate(data)
                      for column_idx in row])
  sparse_matrix = scipy.sparse.coo_matrix(
      (np.ones(indices.shape[0]), (indices[:, 0], indices[:, 1])),
      shape=(num_documents, num_words),
      dtype=np.float32)
  sparse_matrix = sparse_matrix.tocsr()

  dataset = tf.data.Dataset.range(num_documents)

  # For training, we shuffle each epoch and repeat the epochs.
  if shuffle_and_repeat:
    dataset = dataset.shuffle(num_documents).repeat()

  # Returns a single document as a dense TensorFlow tensor. The dataset is
  # stored as a sparse matrix outside of the graph.
  def get_row_py_func(idx):
    def get_row_python(idx_py):
      return np.squeeze(np.array(sparse_matrix[idx_py].todense()), axis=0)

    py_func = tf.compat.v1.py_func(
        get_row_python, [idx], tf.float32, stateful=False)
    py_func.set_shape((num_words,))
    return py_func

  dataset = dataset.map(get_row_py_func)
  return dataset
def newsgroups_dataset ( directory , split_name , num_words , shuffle_and_repeat ) : data = np . load ( download ( directory , FILE_TEMPLATE . format ( split = split_name ) ) ) # The last row is empty in both train and test. data = data [ : - 1 ] # Each row is a list of word ids in the document. We first convert this to # sparse COO matrix (which automatically sums the repeating words). Then, # we convert this COO matrix to CSR format which allows for fast querying of # documents. num_documents = data . shape [ 0 ] indices = np . array ( [ ( row_idx , column_idx ) for row_idx , row in enumerate ( data ) for column_idx in row ] ) sparse_matrix = scipy . sparse . coo_matrix ( ( np . ones ( indices . shape [ 0 ] ) , ( indices [ : , 0 ] , indices [ : , 1 ] ) ) , shape = ( num_documents , num_words ) , dtype = np . float32 ) sparse_matrix = sparse_matrix . tocsr ( ) dataset = tf . data . Dataset . range ( num_documents ) # For training, we shuffle each epoch and repeat the epochs. if shuffle_and_repeat : dataset = dataset . shuffle ( num_documents ) . repeat ( ) # Returns a single document as a dense TensorFlow tensor. The dataset is # stored as a sparse matrix outside of the graph. def get_row_py_func ( idx ) : def get_row_python ( idx_py ) : return np . squeeze ( np . array ( sparse_matrix [ idx_py ] . todense ( ) ) , axis = 0 ) py_func = tf . compat . v1 . py_func ( get_row_python , [ idx ] , tf . float32 , stateful = False ) py_func . set_shape ( ( num_words , ) ) return py_func dataset = dataset . map ( get_row_py_func ) return dataset
20 newsgroups as a tf.data.Dataset.
20 newsgroups as a tf . data . Dataset .