Python tensorflow.foldl() Examples

def neural_gpu_body(inputs, hparams, name=None):
  """The core Neural GPU."""
  with tf.variable_scope(name, "neural_gpu"):

    def step(state, inp):  # pylint: disable=missing-docstring
      x = tf.nn.dropout(state, 1.0 - hparams.dropout)
      for layer in range(hparams.num_hidden_layers):
        x = common_layers.conv_gru(
            x, (hparams.kernel_height, hparams.kernel_width),
            hparams.hidden_size,
            name="cgru_%d" % layer)
      # Padding input is zeroed-out in the modality, we check this by summing.
      padding_inp = tf.less(tf.reduce_sum(tf.abs(inp), axis=[1, 2]), 0.00001)
      new_state = tf.where(padding_inp, state, x)  # No-op where inp is padding.
      return new_state

    return tf.foldl(
        step,
        tf.transpose(inputs, [1, 0, 2, 3]),
        initializer=inputs,
        parallel_iterations=1,
        swap_memory=True) 

def neural_gpu_body(inputs, hparams, name=None):
  """The core Neural GPU."""
  with tf.variable_scope(name, "neural_gpu"):

    def step(state, inp):  # pylint: disable=missing-docstring
      x = tf.nn.dropout(state, 1.0 - hparams.dropout)
      for layer in range(hparams.num_hidden_layers):
        x = common_layers.conv_gru(
            x, (hparams.kernel_height, hparams.kernel_width),
            hparams.hidden_size,
            name="cgru_%d" % layer)
      # Padding input is zeroed-out in the modality, we check this by summing.
      padding_inp = tf.less(tf.reduce_sum(tf.abs(inp), axis=[1, 2]), 0.00001)
      new_state = tf.where(padding_inp, state, x)  # No-op where inp is padding.
      return new_state

    return tf.foldl(
        step,
        tf.transpose(inputs, [1, 0, 2, 3]),
        initializer=inputs,
        parallel_iterations=1,
        swap_memory=True) 

def neural_gpu_body(inputs, hparams, name=None):
  """The core Neural GPU."""
  with tf.variable_scope(name, "neural_gpu"):

    def step(state, inp):  # pylint: disable=missing-docstring
      x = tf.nn.dropout(state, 1.0 - hparams.dropout)
      for layer in range(hparams.num_hidden_layers):
        x = common_layers.conv_gru(
            x, (hparams.kernel_height, hparams.kernel_width),
            hparams.hidden_size,
            name="cgru_%d" % layer)
      # Padding input is zeroed-out in the modality, we check this by summing.
      padding_inp = tf.less(tf.reduce_sum(tf.abs(inp), axis=[1, 2]), 0.00001)
      new_state = tf.where(padding_inp, state, x)  # No-op where inp is padding.
      return new_state

    return tf.foldl(
        step,
        tf.transpose(inputs, [1, 0, 2, 3]),
        initializer=inputs,
        parallel_iterations=1,
        swap_memory=True) 

def testFoldl_Scoped(self):
    with self.test_session() as sess:
      with tf.variable_scope("root") as varscope:
        elems = tf.constant([1, 2, 3, 4, 5, 6], name="data")

        r = tf.foldl(simple_scoped_fn, elems)
        # Check that we have the one variable we asked for here.
        self.assertEqual(len(tf.trainable_variables()), 1)
        self.assertEqual(tf.trainable_variables()[0].name, "root/body/two:0")
        sess.run([tf.global_variables_initializer()])
        self.assertAllEqual(208, r.eval())

        # Now let's reuse our single variable.
        varscope.reuse_variables()
        r = tf.foldl(simple_scoped_fn, elems, initializer=10)
        self.assertEqual(len(tf.trainable_variables()), 1)
        self.assertAllEqual(880, r.eval()) 

def testScanFoldl_Nested(self):
    with self.test_session():
      elems = tf.constant([1.0, 2.0, 3.0, 4.0], name="data")
      inner_elems = tf.constant([0.5, 0.5], name="data")

      def r_inner(a, x):
        return tf.foldl(lambda b, y: b * y * x, inner_elems, initializer=a)

      r = tf.scan(r_inner, elems)

      # t == 0 (returns 1)
      # t == 1, a == 1, x == 2 (returns 1)
      #   t_0 == 0, b == a == 1, y == 0.5, returns b * y * x = 1
      #   t_1 == 1, b == 1,      y == 0.5, returns b * y * x = 1
      # t == 2, a == 1, x == 3 (returns 1.5*1.5 == 2.25)
      #   t_0 == 0, b == a == 1, y == 0.5, returns b * y * x = 1.5
      #   t_1 == 1, b == 1.5,    y == 0.5, returns b * y * x = 1.5*1.5
      # t == 3, a == 2.25, x == 4 (returns 9)
      #   t_0 == 0, b == a == 2.25, y == 0.5, returns b * y * x = 4.5
      #   t_1 == 1, b == 4.5,       y == 0.5, returns b * y * x = 9
      self.assertAllClose([1., 1., 2.25, 9.], r.eval()) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def nested_control_flow_module_fn():
  """Compute the sum of elements greater than 'a' with nested control flow."""
  elems = tf_v1.placeholder(
      dtype=tf.float32, name="elems", shape=[None])
  a = tf_v1.placeholder(dtype=tf.float32, name="a")

  def sum_above_a(acc, x):
    return acc + tf.cond(x > a, lambda: x, lambda: 0.0)

  hub.add_signature(
      inputs={"elems": elems, "a": a},
      outputs=tf.foldl(sum_above_a, elems, initializer=tf.constant(0.0))) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def perform_filter_operation(Y, filter_matrix_conj, taps, delay):
    """

    >>> D, T, taps, delay = 1, 10, 2, 1
    >>> tf.enable_eager_execution()
    >>> Y = tf.ones([D, T])
    >>> filter_matrix_conj = tf.ones([taps, D, D])
    >>> X = perform_filter_operation_v2(Y, filter_matrix_conj, taps, delay)
    >>> X.shape
    TensorShape([Dimension(1), Dimension(10)])
    >>> X.numpy()
    array([[ 1.,  0., -1., -1., -1., -1., -1., -1., -1., -1.]], dtype=float32)
    """
    dyn_shape = tf.shape(Y)
    T = dyn_shape[1]

    def add_tap(accumulated, tau_minus_delay):
        new = tf.einsum(
            'de,dt',
            filter_matrix_conj[tau_minus_delay, :, :],
            Y[:, :(T - delay - tau_minus_delay)]
        )
        paddings = tf.convert_to_tensor([[0, 0], [delay + tau_minus_delay, 0]])
        new = tf.pad(new, paddings, "CONSTANT")
        return accumulated + new

    reverb_tail = tf.foldl(
        add_tap, tf.range(0, taps),
        initializer=tf.zeros_like(Y)
    )
    return Y - reverb_tail 

def _sample_n(self, n, seed=None):
        all_counts = tf.to_float(tf.range(self._total_count + 1))
        for batch_dim in range(self.batch_shape.ndims):
            all_counts = tf.expand_dims(all_counts, axis=-1)
        all_cdfs = tf.map_fn(self.cdf, all_counts)
        shape = tf.concat([[n], self.batch_shape_tensor()], 0)
        uniform = tf.random_uniform(shape, seed=seed)
        return tf.foldl(
            lambda acc, cdfs: tf.where(uniform > cdfs, acc + 1, acc),
            all_cdfs,
            initializer=tf.zeros(shape, dtype=tf.int32)) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def testFoldShape(self):
    with self.test_session():
      x = tf.constant([[1, 2, 3], [4, 5, 6]])
      def fn(_, current_input):
        return current_input
      initializer = tf.constant([0, 0, 0])
      y = tf.foldl(fn, x, initializer=initializer)
      self.assertAllEqual(y.get_shape(), y.eval().shape) 

def testFold_Grad(self):
    with self.test_session():
      elems = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name="data")
      v = tf.constant(2.0, name="v")

      r = tf.foldl(
          lambda a, x: tf.mul(a, x), elems, initializer=v)
      r = tf.gradients(r, v)[0]
      self.assertAllEqual(720.0, r.eval())

      r = tf.foldr(
          lambda a, x: tf.mul(a, x), elems, initializer=v)
      r = tf.gradients(r, v)[0]
      self.assertAllEqual(720.0, r.eval()) 

def testFoldl_Simple(self):
    with self.test_session():
      elems = tf.constant([1, 2, 3, 4, 5, 6], name="data")

      r = tf.foldl(lambda a, x: tf.mul(tf.add(a, x), 2), elems)
      self.assertAllEqual(208, r.eval())

      r = tf.foldl(
          lambda a, x: tf.mul(tf.add(a, x), 2), elems, initializer=10)
      self.assertAllEqual(880, r.eval()) 

def foldl(fn, elems, initializer=None, name=None):
    """Reduce elems using fn to combine them from left to right.

    # Arguments
        fn: Callable that will be called upon each element in elems and an
            accumulator, for instance `lambda acc, x: acc + x`
        elems: tensor
        initializer: The first value used (`elems[0]` in case of None)
        name: A string name for the foldl node in the graph

    # Returns
        Tensor with same type and shape as `initializer`.
    """
    return tf.foldl(fn, elems, initializer=initializer, name=name) 

def top_kth_iterative(x, k):
  """Compute the k-th top element of x on the last axis iteratively.

  This assumes values in x are non-negative, rescale if needed.
  It is often faster than tf.nn.top_k for small k, especially if k < 30.
  Note: this does not support back-propagation, it stops gradients!

  Args:
    x: a Tensor of non-negative numbers of type float.
    k: a python integer.

  Returns:
    a float tensor of the same shape as x but with 1 on the last axis
    that contains the k-th largest number in x.
  """
  # The iterative computation is as follows:
  #
  # cur_x = x
  # for _ in range(k):
  #   top_x = maximum of elements of cur_x on the last axis
  #   cur_x = cur_x where cur_x < top_x and 0 everywhere else (top elements)
  #
  # We encode this computation in a TF graph using tf.foldl, so the inner
  # part of the above loop is called "next_x" and tf.foldl does the loop.
  def next_x(cur_x, _):
    top_x = tf.reduce_max(cur_x, axis=-1, keep_dims=True)
    return cur_x * to_float(cur_x < top_x)
  # We only do k-1 steps of the loop and compute the final max separately.
  fin_x = tf.foldl(next_x, tf.range(k - 1), initializer=tf.stop_gradient(x),
                   parallel_iterations=2, back_prop=False)
  return tf.stop_gradient(tf.reduce_max(fin_x, axis=-1, keep_dims=True))