Python tensorflow.TensorShape() Examples

def main():
    dataset = tf.data.Dataset.from_generator(gen, (tf.int32, tf.int32),
                                             (tf.TensorShape([BATCH_SIZE]),
                                              tf.TensorShape([BATCH_SIZE, 1])))
    optimizer = tf.compat.v1.train.GradientDescentOptimizer(LEARNING_RATE)
    model = Word2Vec(vocab_size=VOCAB_SIZE, embed_size=EMBED_SIZE)
    grad_fn = tfe.implicit_value_and_gradients(model.compute_loss)
    total_loss = 0.0
    num_train_steps = 0
    while num_train_steps < NUM_TRAIN_STEPS:
        for center_words, target_words in tfe.Iterator(dataset):
            if num_train_steps >= NUM_TRAIN_STEPS:
                break
            loss_batch, grads = grad_fn(center_words, target_words)
            total_loss += loss_batch
            optimizer.apply_gradients(grads)
            if (num_train_steps + 1) % SKIP_STEP == 0:
                print('Average loss at step {}: {:5.1f}'.format(
                    num_train_steps, total_loss / SKIP_STEP
                ))
                total_loss = 0.0
            num_train_steps += 1 

def reshape_by_blocks(x, x_shape, memory_block_size):
  """Reshapes input by splitting its length over blocks of memory_block_size.

  Args:
    x: a Tensor with shape [batch, heads, length, depth]
    x_shape: tf.TensorShape of x.
    memory_block_size: Integer which divides length.

  Returns:
    Tensor with shape
    [batch, heads, length // memory_block_size, memory_block_size, depth].
  """
  x = tf.reshape(x, [
      x_shape[0], x_shape[1], x_shape[2] // memory_block_size,
      memory_block_size, x_shape[3]
  ])
  return x 

def nn_distance(xyz1,xyz2):
	'''
Computes the distance of nearest neighbors for a pair of point clouds
input: xyz1: (batch_size,#points_1,3)  the first point cloud
input: xyz2: (batch_size,#points_2,3)  the second point cloud
output: dist1: (batch_size,#point_1)   distance from first to second
output: idx1:  (batch_size,#point_1)   nearest neighbor from first to second
output: dist2: (batch_size,#point_2)   distance from second to first
output: idx2:  (batch_size,#point_2)   nearest neighbor from second to first
	'''
	return nn_distance_module.nn_distance(xyz1,xyz2)
#@tf.RegisterShape('NnDistance')
#def _nn_distance_shape(op):
	#shape1=op.inputs[0].get_shape().with_rank(3)
	#shape2=op.inputs[1].get_shape().with_rank(3)
	#return [tf.TensorShape([shape1.dims[0],shape1.dims[1]]),tf.TensorShape([shape1.dims[0],shape1.dims[1]]),
		#tf.TensorShape([shape2.dims[0],shape2.dims[1]]),tf.TensorShape([shape2.dims[0],shape2.dims[1]])] 

def _assert_same_size(outputs, output_size):
    """Check if outputs match output_size

    Args:
        outputs: A Tensor or a (nested) tuple of tensors
        output_size: Can be an Integer, a TensorShape, or a (nested) tuple of
            Integers or TensorShape.
    """
    nest.assert_same_structure(outputs, output_size)
    flat_output_size = nest.flatten(output_size)
    flat_output = nest.flatten(outputs)

    for (output, size) in zip(flat_output, flat_output_size):
        if output[0].shape != tf.TensorShape(size):
            raise ValueError(
                "The output size does not match the the required output_size") 

def _compute_concat_output_shape(input_shape, axis):
    """Infers the output shape of concat given the input shape.

    The code is adapted from the ConcatLayer of lasagne
    (https://github.com/Lasagne/Lasagne/blob/master/lasagne/layers/merge.py)

    Args:
        input_shape (list): A list of shapes, each of which is in turn a
            list or TensorShape.
        axis (int): Axis of the concat operation.

    Returns:
        list: Output shape of concat.
    """
    # The size of each axis of the output shape equals the first
    # input size of respective axis that is not `None`
    input_shape = [tf.TensorShape(s).as_list() for s in input_shape]
    output_shape = [next((s for s in sizes if s is not None), None)
                    for sizes in zip(*input_shape)]
    axis_sizes = [s[axis] for s in input_shape]
    concat_axis_size = None if any(s is None for s in axis_sizes) \
            else sum(axis_sizes)
    output_shape[axis] = concat_axis_size
    return output_shape 

def _make_shapes_consistent(output, labels):
  """Try to make inputs have the same shape by adding dimensions of size 1."""
  shape1 = output.shape
  shape2 = labels.shape
  len1 = len(shape1)
  len2 = len(shape2)
  if len1 == len2:
    return (output, labels)
  if isinstance(shape1, tf.TensorShape):
    shape1 = tuple(shape1.as_list())
  if isinstance(shape2, tf.TensorShape):
    shape2 = tuple(shape2.as_list())
  if len1 > len2 and all(i == 1 for i in shape1[len2:]):
    for i in range(len1 - len2):
      labels = tf.expand_dims(labels, -1)
    return (output, labels)
  if len2 > len1 and all(i == 1 for i in shape2[len1:]):
    for i in range(len2 - len1):
      output = tf.expand_dims(output, -1)
    return (output, labels)
  raise ValueError("Incompatible shapes for outputs and labels: %s versus %s" %
                   (str(shape1), str(shape2))) 

def __init__(self,
                 dtype,
                 batch_shape=None,
                 value_shape=None,
                 group_ndims=0,
                 is_continuous=None,
                 **kwargs):
        dtype = tf.float32 if dtype is None else tf.as_dtype(dtype).base_dtype

        self.explicit_batch_shape = tf.TensorShape(batch_shape)

        self.explicit_value_shape = tf.TensorShape(value_shape)

        if is_continuous is None:
            is_continuous = dtype.is_floating

        super(Empirical, self).__init__(
            dtype=dtype,
            param_dtype=None,
            is_continuous=is_continuous,
            is_reparameterized=False,
            use_path_derivative=False,
            group_ndims=group_ndims,
            **kwargs) 

def __init__(self,
                 samples,
                 value_shape=None,
                 group_ndims=0,
                 **kwargs):
        self.samples = tf.convert_to_tensor(samples)

        self.explicit_value_shape = tf.TensorShape(value_shape)

        super(Implicit, self).__init__(
            dtype=samples.dtype,
            param_dtype=samples.dtype,
            is_continuous=samples.dtype.is_floating,
            is_reparameterized=False,
            use_path_derivative=False,
            group_ndims=group_ndims,
            **kwargs) 

def _sample(self, n_samples):
        if self.n_experiments is None:
            raise ValueError('Cannot sample when `n_experiments` is None')

        if self.logits.get_shape().ndims == 2:
            logits_flat = self.logits
        else:
            logits_flat = tf.reshape(self.logits, [-1, self.n_categories])
        samples_flat = tf.transpose(
            tf.random.categorical(logits_flat, n_samples * self.n_experiments))
        shape = tf.concat([[n_samples, self.n_experiments],
                           self.batch_shape], 0)
        samples = tf.reshape(samples_flat, shape)
        static_n_samples = n_samples if isinstance(n_samples,
                                                   int) else None
        static_n_exps = self.n_experiments \
            if isinstance(self.n_experiments, int) else None
        samples.set_shape(
            tf.TensorShape([static_n_samples, static_n_exps]).
            concatenate(self.get_batch_shape()))
        samples = tf.reduce_sum(
            tf.one_hot(samples, self.n_categories, dtype=self.dtype),
            axis=1)
        return samples 

def _sample(self, n_samples):
        if self.logits.get_shape().ndims == 2:
            logits_flat = self.logits
        else:
            logits_flat = tf.reshape(self.logits, [-1, self.n_categories])
        samples_flat = tf.transpose(
            tf.random.categorical(logits_flat, n_samples))
        if self.logits.get_shape().ndims == 2:
            samples = samples_flat
        else:
            shape = tf.concat([[n_samples], self.batch_shape], 0)
            samples = tf.reshape(samples_flat, shape)
            static_n_samples = n_samples if isinstance(n_samples,
                                                       int) else None
            samples.set_shape(
                tf.TensorShape([static_n_samples]).
                concatenate(self.get_batch_shape()))
        samples = tf.one_hot(samples, self.n_categories, dtype=self.dtype)
        return samples 

def _sample(self, n_samples):
        logits, temperature = self.logits, self.temperature
        if not self.is_reparameterized:
            logits = tf.stop_gradient(logits)
            temperature = tf.stop_gradient(temperature)
        shape = tf.concat([[n_samples], tf.shape(self.logits)], 0)

        uniform = open_interval_standard_uniform(shape, self.dtype)
        # TODO: Add Gumbel distribution
        gumbel = -tf.log(-tf.log(uniform))
        samples = tf.nn.softmax((logits + gumbel) / temperature)

        static_n_samples = n_samples if isinstance(n_samples, int) else None
        samples.set_shape(
            tf.TensorShape([static_n_samples]).concatenate(logits.get_shape()))
        return samples 

def _sample(self, n_samples):
        mean, u_tril, v_tril = self.mean, self.u_tril, self.v_tril
        if not self.is_reparameterized:
            mean = tf.stop_gradient(mean)
            u_tril = tf.stop_gradient(u_tril)
            v_tril = tf.stop_gradient(v_tril)

        def tile(t):
            new_shape = tf.concat([[n_samples], tf.ones_like(tf.shape(t))], 0)
            return tf.tile(tf.expand_dims(t, 0), new_shape)

        batch_u_tril = tile(u_tril)
        batch_v_tril = tile(v_tril)
        noise = tf.random_normal(
            tf.concat([[n_samples], tf.shape(mean)], axis=0), dtype=self.dtype)
        samples = mean + \
            tf.matmul(tf.matmul(batch_u_tril, noise),
                      tf.matrix_transpose(batch_v_tril))
        # Update static shape
        static_n_samples = n_samples if isinstance(n_samples, int) else None
        samples.set_shape(tf.TensorShape([static_n_samples])
                          .concatenate(self.get_batch_shape())
                          .concatenate(self.get_value_shape()))
        return samples 

def main():
    dataset = tf.data.Dataset.from_generator(gen,
                                             (tf.int32, tf.int32),
                                             (tf.TensorShape([BATCH_SIZE]), tf.TensorShape([BATCH_SIZE, 1])))
    word2vec(dataset) 

def main():
    dataset = tf.data.Dataset.from_generator(gen,
                                             (tf.int32, tf.int32),
                                             (tf.TensorShape([BATCH_SIZE]), tf.TensorShape([BATCH_SIZE, 1])))
    model = SkipGramModel(dataset, VOCAB_SIZE, EMBED_SIZE, BATCH_SIZE, NUM_SAMPLED, LEARNING_RATE)
    model.build_graph()
    model.train(NUM_TRAIN_STEPS)
    model.visualize(VISUAL_FLD, NUM_VISUALIZE) 

def test_net_slice_char_logits_with_correct_shape(self):
    batch_size = 2
    seq_length = 4
    num_char_classes = 3

    layer = create_layer(sequence_layers.NetSlice, batch_size, seq_length,
                         num_char_classes)
    char_logits = layer.create_logits()

    self.assertEqual(
        tf.TensorShape([batch_size, seq_length, num_char_classes]),
        char_logits.get_shape()) 

def test_net_slice_with_autoregression_char_logits_with_correct_shape(self):
    batch_size = 2
    seq_length = 4
    num_char_classes = 3

    layer = create_layer(sequence_layers.NetSliceWithAutoregression,
                         batch_size, seq_length, num_char_classes)
    char_logits = layer.create_logits()

    self.assertEqual(
        tf.TensorShape([batch_size, seq_length, num_char_classes]),
        char_logits.get_shape()) 

def test_attention_char_logits_with_correct_shape(self):
    batch_size = 2
    seq_length = 4
    num_char_classes = 3

    layer = create_layer(sequence_layers.Attention, batch_size, seq_length,
                         num_char_classes)
    char_logits = layer.create_logits()

    self.assertEqual(
        tf.TensorShape([batch_size, seq_length, num_char_classes]),
        char_logits.get_shape()) 

def test_attention_with_autoregression_char_logits_with_correct_shape(self):
    batch_size = 2
    seq_length = 4
    num_char_classes = 3

    layer = create_layer(sequence_layers.AttentionWithAutoregression,
                         batch_size, seq_length, num_char_classes)
    char_logits = layer.create_logits()

    self.assertEqual(
        tf.TensorShape([batch_size, seq_length, num_char_classes]),
        char_logits.get_shape()) 

def testParsingReaderOpWhileLoop(self):
    feature_size = 3
    batch_size = 5

    def ParserEndpoints():
      return gen_parser_ops.gold_parse_reader(self._task_context,
                                              feature_size,
                                              batch_size,
                                              corpus_name='training-corpus')

    with self.test_session() as sess:
      # The 'condition' and 'body' functions expect as many arguments as there
      # are loop variables. 'condition' depends on the 'epoch' loop variable
      # only, so we disregard the remaining unused function arguments. 'body'
      # returns a list of updated loop variables.
      def Condition(epoch, *unused_args):
        return tf.less(epoch, 2)

      def Body(epoch, num_actions, *feature_args):
        # By adding one of the outputs of the reader op ('epoch') as a control
        # dependency to the reader op we force the repeated evaluation of the
        # reader op.
        with epoch.graph.control_dependencies([epoch]):
          features, epoch, gold_actions = ParserEndpoints()
        num_actions = tf.maximum(num_actions,
                                 tf.reduce_max(gold_actions, [0], False) + 1)
        feature_ids = []
        for i in range(len(feature_args)):
          feature_ids.append(features[i])
        return [epoch, num_actions] + feature_ids

      epoch = ParserEndpoints()[-2]
      num_actions = tf.constant(0)
      loop_vars = [epoch, num_actions]

      res = sess.run(
          tf.while_loop(Condition, Body, loop_vars,
                        shape_invariants=[tf.TensorShape(None)] * 2,
                        parallel_iterations=1))
      logging.info('Result: %s', res)
      self.assertEqual(res[0], 2) 

def _two_element_tuple(int_or_tuple):
  """Converts `int_or_tuple` to height, width.

  Several of the functions that follow accept arguments as either
  a tuple of 2 integers or a single integer.  A single integer
  indicates that the 2 values of the tuple are the same.

  This functions normalizes the input value by always returning a tuple.

  Args:
    int_or_tuple: A list of 2 ints, a single int or a tf.TensorShape.

  Returns:
    A tuple with 2 values.

  Raises:
    ValueError: If `int_or_tuple` it not well formed.
  """
  if isinstance(int_or_tuple, (list, tuple)):
    if len(int_or_tuple) != 2:
      raise ValueError('Must be a list with 2 elements: %s' % int_or_tuple)
    return int(int_or_tuple[0]), int(int_or_tuple[1])
  if isinstance(int_or_tuple, int):
    return int(int_or_tuple), int(int_or_tuple)
  if isinstance(int_or_tuple, tf.TensorShape):
    if len(int_or_tuple) == 2:
      return int_or_tuple[0], int_or_tuple[1]
  raise ValueError('Must be an int, a list with 2 elements or a TensorShape of '
                   'length 2') 

def test_return_correct_batchSize(self):
    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
    self.assertEqual(32, static_shape.get_batch_size(tensor_shape)) 

def test_return_correct_height(self):
    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
    self.assertEqual(299, static_shape.get_height(tensor_shape)) 

def test_return_correct_width(self):
    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
    self.assertEqual(384, static_shape.get_width(tensor_shape)) 

def test_return_correct_depth(self):
    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
    self.assertEqual(3, static_shape.get_depth(tensor_shape)) 

def output_size(self):
    return (self._action_size, self._action_size, tf.TensorShape([])) 

def output_size(self):
    return (self._action_size, self._action_size, tf.TensorShape([])) 

def output_size(self):
    return (self._action_size, self._action_size, tf.TensorShape([])) 

def _init_inception():
    global softmax
    if not os.path.exists(MODEL_DIR):
        os.makedirs(MODEL_DIR)
    filename = DATA_URL.split('/')[-1]
    filepath = os.path.join(MODEL_DIR, filename)
    if not os.path.exists(filepath):
        def _progress(count, block_size, total_size):
            sys.stdout.write('\r>> Downloading %s %.1f%%' % (
                filename, float(count * block_size) / float(total_size) * 100.0))
            sys.stdout.flush()

        filepath, _ = urllib.urlretrieve(DATA_URL, filepath, _progress)
        print()
        statinfo = os.stat(filepath)
        print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
    with tf.gfile.FastGFile(os.path.join(
            MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')
    # Works with an arbitrary minibatch size.
    with tf.Session() as sess:
        pool3 = sess.graph.get_tensor_by_name('pool_3:0')
        ops = pool3.graph.get_operations()
        for op_idx, op in enumerate(ops):
            for o in op.outputs:
                shape = o.get_shape()
                shape = [s.value for s in shape]
                new_shape = []
                for j, s in enumerate(shape):
                    if s == 1 and j == 0:
                        new_shape.append(None)
                    else:
                        new_shape.append(s)
                o._shape = tf.TensorShape(new_shape)
        w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
        logits = tf.matmul(tf.squeeze(pool3), w)
        softmax = tf.nn.softmax(logits) 

def get_state_shape_invariants(tensor):
  """Returns the shape of the tensor but sets middle dims to None."""
  shape = tensor.shape.as_list()
  for i in range(1, len(shape) - 1):
    shape[i] = None
  return tf.TensorShape(shape) 

def input_fn(self):
    types = {"inputs": tf.int32}
    shapes = {"inputs": tf.TensorShape([None])}
    dataset = tf.data.Dataset.from_generator(self.input_generator, types,
                                             shapes)
    dataset = dataset.padded_batch(self.BATCH_SIZE, shapes)
    dataset = dataset.map(problem.standardize_shapes)
    features = dataset.make_one_shot_iterator().get_next()
    return features