Python array_ops.unpack函数代码示例

def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None):
  """Processes inputs for Sequence to Sequence models.

  Args:
    x: Input Tensor [batch_size, input_length, embed_dim].
    y: Output Tensor [batch_size, output_length, embed_dim].
    input_length: length of input x.
    output_length: length of output y.
    sentinel: optional first input to decoder and final output expected.
      If sentinel is not provided, zeros are used. Due to fact that y is not
      available in sampling time, shape of sentinel will be inferred from x.
    name: Operation name.

  Returns:
    Encoder input from x, and decoder inputs and outputs from y.
  """
  with ops.name_scope(name, "seq2seq_inputs", [x, y]):
    in_x = array_ops_.unpack(x, axis=1)
    y = array_ops_.unpack(y, axis=1)
    if not sentinel:
      # Set to zeros of shape of y[0], using x for batch size.
      sentinel_shape = array_ops_.pack(
          [array_ops_.shape(x)[0], y[0].get_shape()[1]])
      sentinel = array_ops_.zeros(sentinel_shape)
      sentinel.set_shape(y[0].get_shape())
    in_y = [sentinel] + y
    out_y = y + [sentinel]
    return in_x, in_y, out_y

  def __call__(self,
               inputs,
               initial_state=None,
               dtype=None,
               sequence_length=None,
               scope=None):
    is_list = isinstance(inputs, list)
    if self._use_dynamic_rnn:
      if is_list:
        inputs = array_ops.pack(inputs)
      outputs, state = rnn.dynamic_rnn(
          self._cell,
          inputs,
          sequence_length=sequence_length,
          initial_state=initial_state,
          dtype=dtype,
          time_major=True,
          scope=scope)
      if is_list:
        # Convert outputs back to list
        outputs = array_ops.unpack(outputs)
    else:  # non-dynamic rnn
      if not is_list:
        inputs = array_ops.unpack(inputs)
      outputs, state = rnn.rnn(self._cell,
                               inputs,
                               initial_state=initial_state,
                               dtype=dtype,
                               sequence_length=sequence_length,
                               scope=scope)
      if not is_list:
        # Convert outputs back to tensor
        outputs = array_ops.pack(outputs)

    return outputs, state

 def testCannotInferNumFromUnknownShape(self):
   x = array_ops.placeholder(np.float32)
   with self.assertRaisesRegexp(ValueError,
                                r'Cannot infer num from shape <unknown>'):
     array_ops.unpack(x)
   with self.assertRaisesRegexp(ValueError,
                                r'Cannot infer num from shape <unknown>'):
     array_ops.unstack(x)

 def testCannotInferNumFromNoneShape(self):
   x = array_ops.placeholder(np.float32, shape=(None,))
   with self.assertRaisesRegexp(ValueError,
                                r'Cannot infer num from shape \(\?,\)'):
     array_ops.unpack(x)
   with self.assertRaisesRegexp(ValueError,
                                r'Cannot infer num from shape \(\?,\)'):
     array_ops.unstack(x)

 def testSimple(self):
   np.random.seed(7)
   with self.test_session(use_gpu=True):
     for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
       data = np.random.randn(*shape)
       # Convert data to a single tensorflow tensor
       x = constant_op.constant(data)
       # Unpack into a list of tensors
       cs_unpacked = array_ops.unpack(x, num=shape[0])
       cs_unstacked = array_ops.unpack(x, num=shape[0])
       for cs in (cs_unpacked, cs_unstacked):
         self.assertEqual(type(cs), list)
         self.assertEqual(len(cs), shape[0])
         cs = [c.eval() for c in cs]
         self.assertAllEqual(cs, data)

 def _cat_probs(self, log_probs):
   """Get a list of num_components batchwise probabilities."""
   which_softmax = nn_ops.log_softmax if log_probs else nn_ops.softmax
   cat_probs = which_softmax(self.cat.logits)
   cat_probs = array_ops.unpack(
       cat_probs, num=self.num_components, axis=-1)
   return cat_probs

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply
               reverses the list.

  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  for input_ in input_seq:
    input_.set_shape(input_.get_shape().with_rank(2))

  # Join into (time, batch_size, depth)
  s_joined = array_ops_.pack(input_seq)

  # Reverse along dimension 0
  s_reversed = array_ops_.reverse_sequence(s_joined, lengths, 0, 1)
  # Split again into list
  result = array_ops_.unpack(s_reversed)
  return result

def _reverse_seq(input_seq, lengths):
    """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply reverses
               the list.

  Returns:
    time-reversed sequence
  """
    if lengths is None:
        return list(reversed(input_seq))

    input_shape = tensor_shape.unknown_shape(ndims=input_seq[0].get_shape().ndims)
    for input_ in input_seq:
        input_shape.merge_with(input_.get_shape())
        input_.set_shape(input_shape)

    # Join into (time, batch_size, depth)
    s_joined = array_ops.pack(input_seq)

    # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
    if lengths is not None:
        lengths = math_ops.to_int64(lengths)

    # Reverse along dimension 0
    s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
    # Split again into list
    result = array_ops.unpack(s_reversed)
    for r in result:
        r.set_shape(input_shape)
    return result

 def __call__(self, inputs, state, scope=None):
   """Run this multi-layer cell on inputs, starting from state."""
   with vs.variable_scope(scope or type(self).__name__):  # "MultiRNNCell"
     cur_state_pos = 0
     cur_inp = inputs
     new_states = []
     for i, cell in enumerate(self._cells):
       with vs.variable_scope("Cell%d" % i):
         if self._state_is_tuple:
           if not nest.is_sequence(state):
             raise ValueError(
                 "Expected state to be a tuple of length %d, but received: %s"
                 % (len(self.state_size), state))
           cur_state = state[i]
         else:
           # print("STATE",state)
           """
           cur_state = array_ops.slice(
               state, [0, cur_state_pos], [-1, cell.state_size])
           """
           cur_state = array_ops.unpack(state)[i]
           # cur_state_pos += cell.state_size
         cur_inp, new_state = cell(cur_inp, cur_state)
         new_states.append(new_state)
   """
   new_states = (tuple(new_states) if self._state_is_tuple
                 else array_ops.concat(1, new_states))
   """
   new_states = array_ops.pack(new_states)
   return cur_inp, new_states

  def testZeroLengthDim(self):
    with self.test_session():
      x = array_ops.zeros(shape=(0, 1, 2))
      y = array_ops.unpack(x, axis=1)[0].eval()
      self.assertEqual(y.shape, (0, 2))

      y = array_ops.unstack(x, axis=1)[0].eval()
      self.assertEqual(y.shape, (0, 2))

  def testInferNum(self):
    with self.test_session():
      for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
        x = array_ops.placeholder(np.float32, shape=shape)
        cs = array_ops.unpack(x)
        self.assertEqual(type(cs), list)
        self.assertEqual(len(cs), shape[0])

        cs = array_ops.unstack(x)
        self.assertEqual(type(cs), list)
        self.assertEqual(len(cs), shape[0])

  def testAxis0Default(self):
    with self.test_session() as sess:
      a = constant_op.constant([[1, 2, 3], [4, 5, 6]], name='a')

      unpacked = sess.run(array_ops.unpack(a))
      unstacked = sess.run(array_ops.unstack(a))

    self.assertEqual(len(unpacked), 2)
    self.assertAllEqual(unpacked[0], [1, 2, 3])
    self.assertAllEqual(unpacked[1], [4, 5, 6])
    self.assertEqual(len(unstacked), 2)
    self.assertAllEqual(unstacked[0], [1, 2, 3])
    self.assertAllEqual(unstacked[1], [4, 5, 6])

 def _sample_n(self, n, seed=None):
     # We use 2 uniform random floats to generate polar random variates.
     # http://dl.acm.org/citation.cfm?id=179631
     # Theorem 2. Let G, H be iid variates, uniformly distributed on [0,1].
     # Let theta = 2*pi*H, let R = sqrt(df*(G^(-2/df) - 1)) for df > 0.
     # Let X = R*cos(theta), and let Y = R*sin(theta).
     # Then X ~ t_df and Y ~ t_df.
     # The variates X and Y are not independent.
     shape = array_ops.concat(0, ([2, n], self.batch_shape()))
     uniform = random_ops.random_uniform(shape=shape, dtype=self.dtype, seed=seed)
     samples_g, samples_h = array_ops.unpack(uniform, num=2)
     theta = (2.0 * math.pi) * samples_h
     r = math_ops.sqrt(self.df * (math_ops.pow(samples_g, -2 / self.df) - 1))
     samples = r * math_ops.cos(theta)
     return samples * self.sigma + self.mu

  def testAgainstNumpy(self):
    # For 1 to 5 dimensions.
    for i in range(1, 6):
      a = np.random.random(np.random.permutation(i) + 1)

      # For all the possible axis to split it, including negative indices.
      for j in range(-i, i):
        expected = np_split_squeeze(a, j)

        with self.test_session() as sess:
          actual_unpack = sess.run(array_ops.unpack(a, axis=j))
          actual_unstack = sess.run(array_ops.unstack(a, axis=j))

        self.assertAllEqual(expected, actual_unpack)
        self.assertAllEqual(expected, actual_unstack)

  def testGradientsAxis0(self):
    for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
      data = np.random.randn(*shape)
      shapes = [shape[1:]] * shape[0]
      for i in xrange(shape[0]):
        with self.test_session(use_gpu=True):
          x = constant_op.constant(data)
          cs = array_ops.unpack(x, num=shape[0])
          err = gradient_checker.compute_gradient_error(x, shape, cs[i],
                                                        shapes[i])
          self.assertLess(err, 1e-6)

          cs = array_ops.unstack(x, num=shape[0])
          err = gradient_checker.compute_gradient_error(x, shape, cs[i],
                                                        shapes[i])
          self.assertLess(err, 1e-6)