Python tensorflow.int64() Examples

def ones_matrix_band_part(rows, cols, num_lower, num_upper, out_shape=None):
  """Matrix band part of ones."""
  if all([isinstance(el, int) for el in [rows, cols, num_lower, num_upper]]):
    # Needed info is constant, so we construct in numpy
    if num_lower < 0:
      num_lower = rows - 1
    if num_upper < 0:
      num_upper = cols - 1
    lower_mask = np.tri(cols, rows, num_lower).T
    upper_mask = np.tri(rows, cols, num_upper)
    band = np.ones((rows, cols)) * lower_mask * upper_mask
    if out_shape:
      band = band.reshape(out_shape)
    band = tf.constant(band, tf.float32)
  else:
    band = tf.matrix_band_part(
        tf.ones([rows, cols]), tf.cast(num_lower, tf.int64),
        tf.cast(num_upper, tf.int64))
    if out_shape:
      band = tf.reshape(band, out_shape)

  return band 

def test_indices_to_dense_vector_int(self):
    size = 500
    num_indices = 25
    rand_indices = np.random.permutation(np.arange(size))[0:num_indices]

    expected_output = np.zeros(size, dtype=np.int64)
    expected_output[rand_indices] = 1

    tf_rand_indices = tf.constant(rand_indices)
    indicator = ops.indices_to_dense_vector(
        tf_rand_indices, size, 1, dtype=tf.int64)

    with self.test_session() as sess:
      output = sess.run(indicator)
      self.assertAllEqual(output, expected_output)
      self.assertEqual(output.dtype, expected_output.dtype) 

def add_task_id(self, task, example):
    """Convert example to code switching mode by adding a task id."""
    if hasattr(task, "class_labels"):
      # TODO(urvashik): handle the case where num_labels > 9
      example["targets"] = tf.cast(discretization.int_to_bit(
          example["targets"], 1, base=10) + 50, tf.int64)
      example["targets"] = tf.squeeze(example["targets"], axis=[-1])

    if task.has_inputs:
      inputs = example.pop("inputs")
      concat_list = [inputs, [task.task_id], example["targets"]]
    else:
      concat_list = [[task.task_id], example["targets"]]

    example["targets"] = tf.concat(concat_list, 0)
    return example 

def build_cross_entropy_loss(logits, gold):
  """Constructs a cross entropy from logits and one-hot encoded gold labels.

  Supports skipping rows where the gold label is the magic -1 value.

  Args:
    logits: float Tensor of scores.
    gold: int Tensor of one-hot labels.

  Returns:
    cost, correct, total: the total cost, the total number of correctly
        predicted labels, and the total number of valid labels.
  """
  valid = tf.reshape(tf.where(tf.greater(gold, -1)), [-1])
  gold = tf.gather(gold, valid)
  logits = tf.gather(logits, valid)
  correct = tf.reduce_sum(tf.to_int32(tf.nn.in_top_k(logits, gold, 1)))
  total = tf.size(gold)
  cost = tf.reduce_sum(
      tf.contrib.nn.deprecated_flipped_sparse_softmax_cross_entropy_with_logits(
          logits, tf.cast(gold, tf.int64))) / tf.cast(total, tf.float32)
  return cost, correct, total 

def serving_input_fn(self, hparams):
    """Input fn for serving export, starting from serialized example."""
    mode = tf.estimator.ModeKeys.PREDICT
    serialized_example = tf.placeholder(
        dtype=tf.string, shape=[None], name="serialized_example")
    dataset = tf.data.Dataset.from_tensor_slices(serialized_example)
    dataset = dataset.map(self.decode_example)
    dataset = dataset.map(lambda ex: self.preprocess_example(ex, mode, hparams))
    dataset = dataset.map(self.maybe_reverse_and_copy)
    dataset = dataset.map(data_reader.cast_ints_to_int32)
    dataset = dataset.padded_batch(
        tf.shape(serialized_example, out_type=tf.int64)[0],
        dataset.output_shapes)
    dataset = dataset.map(standardize_shapes)
    features = tf.contrib.data.get_single_element(dataset)

    if self.has_inputs:
      features.pop("targets", None)

    return tf.estimator.export.ServingInputReceiver(
        features=features, receiver_tensors=serialized_example) 

def decode_example(self, serialized_example):
    """Return a dict of Tensors from a serialized tensorflow.Example."""
    data_fields, data_items_to_decoders = self.example_reading_spec()
    # Necessary to rejoin examples in the correct order with the Cloud ML Engine
    # batch prediction API.
    data_fields["batch_prediction_key"] = tf.FixedLenFeature([1], tf.int64, 0)
    if data_items_to_decoders is None:
      data_items_to_decoders = {
          field: tf.contrib.slim.tfexample_decoder.Tensor(field)
          for field in data_fields
      }

    decoder = tf.contrib.slim.tfexample_decoder.TFExampleDecoder(
        data_fields, data_items_to_decoders)

    decode_items = list(sorted(data_items_to_decoders))
    decoded = decoder.decode(serialized_example, items=decode_items)
    return dict(zip(decode_items, decoded)) 

def __init__(self, pc: _Network3D, config, centers, sess, freqs_resolution=1e9):
        """
        :param sess: Must be set at the latest before using get_pr or get_freqs
        """
        self.pc_class = pc.__class__
        self.config = config
        self.input_ctx_shape = self.pc_class.get_context_shape(config)
        self.input_ctx = tf.placeholder(tf.int64, self.input_ctx_shape)  # symbols!
        input_ctx_batched = tf.expand_dims(self.input_ctx, 0)  # add batch dimension, 1DHW
        input_ctx_batched = tf.expand_dims(input_ctx_batched, -1)  # add T dimension for 3d conv, now 1CHW1
        # Here, in contrast to pc.bitcost(...), q does not need to be padded, as it is part of some context.
        # Logits will be a 1111L vector, i.e., prediction of the next pixel
        q = tf.gather(centers, input_ctx_batched)
        logits = pc.logits(q, is_training=False)
        self.pr = tf.nn.softmax(logits)
        self.freqs = tf.squeeze(tf.cast(self.pr * freqs_resolution, tf.int64))
        self.sess = sess

        self._get_freqs = None 

def build_inputs(self):
    if self.mode == "encode":
      # Encode mode doesn't read from disk, so defer to parent.
      return super(SkipThoughtsModel, self).build_inputs()
    else:
      # Replace disk I/O with random Tensors.
      self.encode_ids = tf.random_uniform(
          [self.config.batch_size, 15],
          minval=0,
          maxval=self.config.vocab_size,
          dtype=tf.int64)
      self.decode_pre_ids = tf.random_uniform(
          [self.config.batch_size, 15],
          minval=0,
          maxval=self.config.vocab_size,
          dtype=tf.int64)
      self.decode_post_ids = tf.random_uniform(
          [self.config.batch_size, 15],
          minval=0,
          maxval=self.config.vocab_size,
          dtype=tf.int64)
      self.encode_mask = tf.ones_like(self.encode_ids)
      self.decode_pre_mask = tf.ones_like(self.decode_pre_ids)
      self.decode_post_mask = tf.ones_like(self.decode_post_ids) 

def build_inputs(self):
    if self.mode == "inference":
      # Inference mode doesn't read from disk, so defer to parent.
      return super(ShowAndTellModel, self).build_inputs()
    else:
      # Replace disk I/O with random Tensors.
      self.images = tf.random_uniform(
          shape=[self.config.batch_size, self.config.image_height,
                 self.config.image_width, 3],
          minval=-1,
          maxval=1)
      self.input_seqs = tf.random_uniform(
          [self.config.batch_size, 15],
          minval=0,
          maxval=self.config.vocab_size,
          dtype=tf.int64)
      self.target_seqs = tf.random_uniform(
          [self.config.batch_size, 15],
          minval=0,
          maxval=self.config.vocab_size,
          dtype=tf.int64)
      self.input_mask = tf.ones_like(self.input_seqs) 

def test_indices_to_dense_vector_int(self):
    size = 500
    num_indices = 25
    rand_indices = np.random.permutation(np.arange(size))[0:num_indices]

    expected_output = np.zeros(size, dtype=np.int64)
    expected_output[rand_indices] = 1

    tf_rand_indices = tf.constant(rand_indices)
    indicator = ops.indices_to_dense_vector(
        tf_rand_indices, size, 1, dtype=tf.int64)

    with self.test_session() as sess:
      output = sess.run(indicator)
      self.assertAllEqual(output, expected_output)
      self.assertEqual(output.dtype, expected_output.dtype) 

def _read_single_sequence_example(file_list, tokens_shape=None):
  """Reads and parses SequenceExamples from TFRecord-encoded file_list."""
  tf.logging.info('Constructing TFRecordReader from files: %s', file_list)
  file_queue = tf.train.string_input_producer(file_list)
  reader = tf.TFRecordReader()
  seq_key, serialized_record = reader.read(file_queue)
  ctx, sequence = tf.parse_single_sequence_example(
      serialized_record,
      sequence_features={
          data_utils.SequenceWrapper.F_TOKEN_ID:
              tf.FixedLenSequenceFeature(tokens_shape or [], dtype=tf.int64),
          data_utils.SequenceWrapper.F_LABEL:
              tf.FixedLenSequenceFeature([], dtype=tf.int64),
          data_utils.SequenceWrapper.F_WEIGHT:
              tf.FixedLenSequenceFeature([], dtype=tf.float32),
      })
  return seq_key, ctx, sequence 

def extra_reading_spec(self):
    """Additional data fields to store on disk and their decoders."""

    # TODO(piotrmilos): shouldn't done be included here?
    data_fields = {
        "frame_number": tf.FixedLenFeature([1], tf.int64),
        "action": tf.FixedLenFeature([1], tf.int64),
        "reward": tf.FixedLenFeature([1], tf.int64)
    }
    decoders = {
        "frame_number":
            tf.contrib.slim.tfexample_decoder.Tensor(tensor_key="frame_number"),
        "action":
            tf.contrib.slim.tfexample_decoder.Tensor(tensor_key="action"),
        "reward":
            tf.contrib.slim.tfexample_decoder.Tensor(tensor_key="reward"),
    }
    return data_fields, decoders 

def loss(logits, labels):
  """Add L2Loss to all the trainable variables.

  Add summary for "Loss" and "Loss/avg".
  Args:
    logits: Logits from inference().
    labels: Labels from distorted_inputs or inputs(). 1-D tensor
            of shape [batch_size]

  Returns:
    Loss tensor of type float.
  """
  # Calculate the average cross entropy loss across the batch.
  labels = tf.cast(labels, tf.int64)
  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
      labels=labels, logits=logits, name='cross_entropy_per_example')
  cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
  tf.add_to_collection('losses', cross_entropy_mean)

  # The total loss is defined as the cross entropy loss plus all of the weight
  # decay terms (L2 loss).
  return tf.add_n(tf.get_collection('losses'), name='total_loss') 

def global_step(device=''):
  """Returns the global step variable.

  Args:
    device: Optional device to place the variable. It can be an string or a
      function that is called to get the device for the variable.

  Returns:
    the tensor representing the global step variable.
  """
  global_step_ref = tf.get_collection(tf.GraphKeys.GLOBAL_STEP)
  if global_step_ref:
    return global_step_ref[0]
  else:
    collections = [
        VARIABLES_TO_RESTORE,
        tf.GraphKeys.GLOBAL_VARIABLES,
        tf.GraphKeys.GLOBAL_STEP,
    ]
    # Get the device for the variable.
    with tf.device(variable_device(device, 'global_step')):
      return tf.get_variable('global_step', shape=[], dtype=tf.int64,
                             initializer=tf.zeros_initializer(),
                             trainable=False, collections=collections) 

def example_reading_spec(self):
    data_fields, data_items_to_decoders = (super(QuestionAndContext2TextProblem,
                                                 self)
                                           .example_reading_spec())
    data_fields["context"] = tf.VarLenFeature(tf.int64)
    return (data_fields, data_items_to_decoders) 

def extra_reading_spec(self):
    """Additional data fields to store on disk and their decoders."""
    data_fields = {
        "frame_number": tf.FixedLenFeature([1], tf.int64),
    }
    decoders = {
        "frame_number": tf.contrib.slim.tfexample_decoder.Tensor(
            tensor_key="frame_number"),
    }
    return data_fields, decoders 

def precision(labels, predictions, num_classes, pos_indices=None,
              weights=None, average='micro'):
    """Multi-class precision metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    pr, _, _ = metrics_from_confusion_matrix(
        cm, pos_indices, average=average)
    op, _, _ = metrics_from_confusion_matrix(
        op, pos_indices, average=average)
    return (pr, op) 

def read_tfrecord(tf_filename, size):
    queue = tf.train.string_input_producer([tf_filename])
    reader = tf.TFRecordReader()
    __, serialized_example = reader.read(queue)
    feature = {
          'image_raw': tf.FixedLenFeature([size[0]*size[1]*size[2]], tf.float32),
          'height': tf.FixedLenFeature([], tf.int64),
          'width': tf.FixedLenFeature([], tf.int64)
    }
    features = tf.parse_single_example(serialized_example, features=feature)
    image = features['image_raw']
    image = tf.reshape(image, size)
    return image
# end change
############################################################################## 

def _parse_function(example_proto):
  feature = {
        'image_raw': tf.FixedLenFeature([180*180*1], tf.float32),
        'height': tf.FixedLenFeature([], tf.int64),
        'width': tf.FixedLenFeature([], tf.int64)
  }
  parsed_features = tf.parse_single_example(example_proto, feature)
  image = parsed_features['image_raw']
  image = tf.reshape(image, [180,180,1])
  return image 

def read_tfrecord(tf_filename, size):
    queue = tf.train.string_input_producer([tf_filename])
    reader = tf.TFRecordReader()
    __, serialized_example = reader.read(queue)
    feature = {
          'image_raw': tf.FixedLenFeature([], tf.string),
          'height': tf.FixedLenFeature([], tf.int64),
          'width': tf.FixedLenFeature([], tf.int64)
    }
    features = tf.parse_single_example(serialized_example, features=feature)
    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image = tf.reshape(image, size)
    return image 

def fbeta(labels, predictions, num_classes, pos_indices=None, weights=None,
          average='micro', beta=1):
    """Multi-class fbeta metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    beta : int, optional
        Weight of precision in harmonic mean
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    _, _, fbeta = metrics_from_confusion_matrix(
        cm, pos_indices, average=average, beta=beta)
    _, _, op = metrics_from_confusion_matrix(
        op, pos_indices, average=average, beta=beta)
    return (fbeta, op) 

def accuracy_instance(predictions, targets, n=[1, 2, 3, 4, 5, 10], nb_classes=5, nb_samples_per_class=10, batch_size=1):
    targets = tf.cast(targets, predictions.dtype)

    accuracy = tf.constant(value=0, shape=(batch_size, nb_samples_per_class), dtype=tf.float32)
    indices = tf.constant(value=0, shape=(batch_size, nb_classes+1), dtype=tf.float32)

    def step_((accuracy, indices), (p, t)):
        """with tf.variable_scope("Metric_step_var", reuse=True):
            accuracy = tf.get_variable(name="accuracy", shape=(batch_size, nb_samples_per_class),
                                       initializer=tf.constant_initializer(0), dtype=tf.float32)
            indices = tf.get_variable(name="indices", shape=(batch_size, nb_classes + 1),
                                      initializer=tf.constant_initializer(0), dtype=tf.float32)"""

        p = tf.cast(p, tf.int32)
        t = tf.cast(t, tf.int32)
        ##Accuracy Update
        batch_range = tf.cast(tf.range(0, batch_size), dtype=tf.int32)
        gather = tf.cast(tf.gather_nd(indices,tf.stack([tf.range(0,p.get_shape().as_list()[0]), t], axis=1)), tf.int32)
        index = tf.cast(tf.stack([batch_range, gather], axis=1), dtype=tf.int64)
        val = tf.cast(tf.equal(p, t), tf.float32)
        delta = tf.SparseTensor(indices=index, values=val, dense_shape=tf.cast(accuracy.get_shape().as_list(), tf.int64))
        accuracy = accuracy + tf.sparse_tensor_to_dense(delta)
        ##Index Update
        index = tf.cast(tf.stack([batch_range, t], axis=1), dtype=tf.int64)
        val = tf.constant(1.0, shape=[batch_size])
        delta = tf.SparseTensor(indices=index, values=val, dense_shape=tf.cast(indices.get_shape().as_list(), dtype=tf.int64))
        indices = indices + tf.sparse_tensor_to_dense(delta)
        return [accuracy, indices] 

def example_reading_spec(self):
    data_fields = {"targets": tf.VarLenFeature(tf.int64)}
    if self.has_inputs:
      data_fields["inputs"] = tf.VarLenFeature(tf.int64)

    if self.packed_length:
      if self.has_inputs:
        data_fields["inputs_segmentation"] = tf.VarLenFeature(tf.int64)
        data_fields["inputs_position"] = tf.VarLenFeature(tf.int64)
      data_fields["targets_segmentation"] = tf.VarLenFeature(tf.int64)
      data_fields["targets_position"] = tf.VarLenFeature(tf.int64)

    data_items_to_decoders = None
    return (data_fields, data_items_to_decoders) 

def compute_accuracy(predictions, labels):
  return tf.reduce_sum(
      tf.cast(
          tf.equal(
              tf.argmax(predictions, axis=1,
                        output_type=tf.int64),
              tf.argmax(labels, axis=1,
                        output_type=tf.int64)),
          dtype=tf.float32)) / float(predictions.shape[0].value) 

def example_reading_spec(self):
    label_key = "image/class/label"
    data_fields, data_items_to_decoders = (
        super(Image2TextProblem, self).example_reading_spec())
    data_fields[label_key] = tf.VarLenFeature(tf.int64)
    data_items_to_decoders[
        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
    return data_fields, data_items_to_decoders 

def example_reading_spec(self):
    label_key = "image/class/label"
    data_fields, data_items_to_decoders = (
        super(Image2ClassProblem, self).example_reading_spec())
    data_fields[label_key] = tf.FixedLenFeature((1,), tf.int64)

    data_items_to_decoders[
        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
    return data_fields, data_items_to_decoders 

def example_reading_spec(self):
    data_fields = {
        "inputs": tf.VarLenFeature(tf.int64),
        "audio/sample_count": tf.FixedLenFeature((), tf.int64),
        "audio/sample_width": tf.FixedLenFeature((), tf.int64),
        "targets": tf.VarLenFeature(tf.int64),
    }
    return data_fields, None 

def example_reading_spec(self):
    data_fields, data_items_to_decoders = (
        super(BabiQa, self).example_reading_spec())
    data_fields['targets'] = tf.FixedLenFeature([1], tf.int64)
    return (data_fields, data_items_to_decoders) 

def example_reading_spec(self):
    label_key = "image/class/label"
    data_fields, data_items_to_decoders = (
        super(Video2ClassProblem, self).example_reading_spec())
    data_fields[label_key] = tf.FixedLenFeature((1,), tf.int64)
    data_items_to_decoders[
        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
    return data_fields, data_items_to_decoders 

def embedding(x,
              vocab_size,
              dense_size,
              name=None,
              reuse=None,
              multiplier=1.0,
              symbol_dropout_rate=0.0,
              embedding_var=None,
              dtype=tf.float32):
  """Embed x of type int64 into dense vectors, reducing to max 4 dimensions."""
  with tf.variable_scope(
      name, default_name="embedding", values=[x], reuse=reuse, dtype=dtype):
    if embedding_var is None:
      embedding_var = tf.get_variable("kernel", [vocab_size, dense_size])
    # On the backwards pass, we want to convert the gradient from
    # an indexed-slices to a regular tensor before sending it back to the
    # parameter server. This avoids excess computation on the parameter server.
    if not tf.contrib.eager.in_eager_mode():
      embedding_var = convert_gradient_to_tensor(embedding_var)
    x = dropout_no_scaling(x, 1.0 - symbol_dropout_rate)
    emb_x = gather(embedding_var, x, dtype)
    if multiplier != 1.0:
      emb_x *= multiplier
    static_shape = emb_x.shape.as_list()
    if len(static_shape) < 5:
      return emb_x
    assert len(static_shape) == 5
    # If we had an extra channel dimension, assume it's 1, i.e. shape[3] == 1.
    return tf.squeeze(emb_x, 3)