Python tensorflow.python.ops.nn_ops 模块，conv2d() 实例源码

def _attention(self, query, attn_states):
    conv2d = nn_ops.conv2d
    reduce_sum = math_ops.reduce_sum
    softmax = nn_ops.softmax
    tanh = math_ops.tanh

    with vs.variable_scope("Attention"):
      k = vs.get_variable("AttnW", [1, 1, self._attn_size, self._attn_vec_size])
      v = vs.get_variable("AttnV", [self._attn_vec_size])
      hidden = array_ops.reshape(attn_states,
                                 [-1, self._attn_length, 1, self._attn_size])
      hidden_features = conv2d(hidden, k, [1, 1, 1, 1], "SAME")
      y = _linear(query, self._attn_vec_size, True)
      y = array_ops.reshape(y, [-1, 1, 1, self._attn_vec_size])
      s = reduce_sum(v * tanh(hidden_features + y), [2, 3])
      a = softmax(s)
      d = reduce_sum(
          array_ops.reshape(a, [-1, self._attn_length, 1, 1]) * hidden, [1, 2])
      new_attns = array_ops.reshape(d, [-1, self._attn_size])
      new_attn_states = array_ops.slice(attn_states, [0, 1, 0], [-1, -1, -1])
      return new_attns, new_attn_states

def _attention(self, query, attn_states):
    conv2d = nn_ops.conv2d
    reduce_sum = math_ops.reduce_sum
    softmax = nn_ops.softmax
    tanh = math_ops.tanh

    with vs.variable_scope("Attention"):
      k = vs.get_variable("AttnW", [1, 1, self._attn_size, self._attn_vec_size])
      v = vs.get_variable("AttnV", [self._attn_vec_size])
      hidden = array_ops.reshape(attn_states,
                                 [-1, self._attn_length, 1, self._attn_size])
      hidden_features = conv2d(hidden, k, [1, 1, 1, 1], "SAME")
      y = _linear(query, self._attn_vec_size, True)
      y = array_ops.reshape(y, [-1, 1, 1, self._attn_vec_size])
      s = reduce_sum(v * tanh(hidden_features + y), [2, 3])
      a = softmax(s)
      d = reduce_sum(
          array_ops.reshape(a, [-1, self._attn_length, 1, 1]) * hidden, [1, 2])
      new_attns = array_ops.reshape(d, [-1, self._attn_size])
      new_attn_states = array_ops.slice(attn_states, [0, 1, 0], [-1, -1, -1])
      return new_attns, new_attn_states

def _attention(self, query, attn_states):
    conv2d = nn_ops.conv2d
    reduce_sum = math_ops.reduce_sum
    softmax = nn_ops.softmax
    tanh = math_ops.tanh

    with tf.variable_scope("attention"):
      k = tf.get_variable(
          "attn_w", [1, 1, self._attn_size, self._attn_vec_size])
      v = tf.get_variable("attn_v", [self._attn_vec_size])
      hidden = array_ops.reshape(attn_states,
                                 [-1, self._attn_length, 1, self._attn_size])
      hidden_features = conv2d(hidden, k, [1, 1, 1, 1], "SAME")
      y = _linear(query, self._attn_vec_size, True)
      y = array_ops.reshape(y, [-1, 1, 1, self._attn_vec_size])
      s = reduce_sum(v * tanh(hidden_features + y), [2, 3])
      a = softmax(s)
      d = reduce_sum(
          array_ops.reshape(a, [-1, self._attn_length, 1, 1]) * hidden, [1, 2])
      new_attns = array_ops.reshape(d, [-1, self._attn_size])
      new_attn_states = array_ops.slice(attn_states, [0, 1, 0], [-1, -1, -1])
      return new_attns, new_attn_states

def _strict_conv1d(x, h):
  """Return x * h for rank 1 tensors x and h."""
  with ops.name_scope('strict_conv1d', values=[x, h]):
    x = array_ops.reshape(x, (1, -1, 1, 1))
    h = array_ops.reshape(h, (-1, 1, 1, 1))
    result = nn_ops.conv2d(x, h, [1, 1, 1, 1], 'SAME')
    return array_ops.reshape(result, [-1])

def _strict_conv1d(x, h):
  """Return x * h for rank 1 tensors x and h."""
  with ops.name_scope('strict_conv1d', values=[x, h]):
    x = array_ops.reshape(x, (1, -1, 1, 1))
    h = array_ops.reshape(h, (-1, 1, 1, 1))
    result = nn_ops.conv2d(x, h, [1, 1, 1, 1], 'SAME')
    return array_ops.reshape(result, [-1])

def _strict_conv1d(x, h):
  """Return x * h for rank 1 tensors x and h."""
  with ops.name_scope('strict_conv1d', values=[x, h]):
    x = array_ops.reshape(x, (1, -1, 1, 1))
    h = array_ops.reshape(h, (-1, 1, 1, 1))
    result = nn_ops.conv2d(x, h, [1, 1, 1, 1], 'SAME')
    return array_ops.reshape(result, [-1])

def _VerifyValues(self, input_sizes, filter_sizes, stride, padding, expected):
    """Tests that tf.nn.conv2d produces the expected value.

    Args:
      input_sizes: Input tensor dimensions in
        [batch, input_rows, input_cols, input_depth].
      filter_sizes: Filter tensor dimensions in
        [kernel_rows, kernel_cols, input_depth, output_depth].
      stride: Stride.
      padding: Padding type.
      expected: Expected output.
    """
    total_size_1 = np.prod(input_sizes)
    total_size_2 = np.prod(filter_sizes)
    x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes)
    x2 = np.arange(1, total_size_2 + 1, dtype=np.float32).reshape(filter_sizes)
    strides = [1, stride, stride, 1]

    with self.test_session() as sess:
      with self.test_scope():
        t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes)
        t2 = array_ops.placeholder(dtypes.float32, shape=filter_sizes)
        out = nn_ops.conv2d(
            t1, t2, strides=strides, padding=padding, data_format="NHWC")
      value = sess.run(out, {t1: x1, t2: x2})
      self.assertArrayNear(expected, np.ravel(value), 1e-3)

def get_vinyals_attention_function(attention_inputs, output_size, num_heads, scope=None):
  """
  This is the attention approach as defined by Vinyals and Kaiser on
  the paer 'Grammar as a Foreign Language'

  :param attention_inputs:
  :param output_size:
  :param num_heads:
  :return:
  """
  with tf.variable_scope(scope or "vinyals_attention", reuse=None) as attention_scope:

    top_states = [array_ops.reshape(e, [-1, 1, output_size])
                  for e in attention_inputs]
    attention_states = array_ops.concat(top_states, 1)
    if not attention_states.get_shape()[1:2].is_fully_defined():
      raise ValueError("Shape[1] and [2] of attention_states must be known: %s" % attention_states.get_shape())

    attn_length = attention_states.get_shape()[1].value
    attn_size = attention_states.get_shape()[2].value

    # To calculate W1 * h_t we use a 1-by-1 convolution, need to reshape before.
    hidden = array_ops.reshape(attention_states, [-1, attn_length, 1, attn_size])
    hidden_features = []
    v = []
    attention_vec_size = attn_size  # Size of query vectors for attention.
    for a in xrange(num_heads):
      k = tf.get_variable("AttnW_%d" % a, [1, 1, attn_size, attention_vec_size])
      hidden_features.append(nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME"))
      v.append(tf.get_variable("AttnV_%d" % a, [attention_vec_size]))

    def attention(query):
      """Put attention masks on hidden using hidden_features and query."""
      attn_weights = []
      ds = []  # Results of attention reads will be stored here.
      for i in xrange(num_heads):
        with tf.variable_scope("Attention_%d" % i):
          y = _linear(query, attention_vec_size, True)
          y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size])
          # Attention mask is a softmax of v^T * tanh(...).
          s = math_ops.reduce_sum(v[i] * math_ops.tanh(hidden_features[i] + y), [2, 3])
          a = nn_ops.softmax(s)
          attn_weights.append(a)
          # Now calculate the attention-weighted vector d.
          d = math_ops.reduce_sum(
            array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden, [1, 2])
          ds.append(array_ops.reshape(d, [-1, attn_size]))
      return attn_weights, ds

    return attention

def __init__(self, cell, attention_states, batch_size, embedding, initializer=None, num_heads=1, scope=None):
        if not isinstance(cell, tf.nn.rnn_cell.RNNCell):
            raise TypeError("The parameter cell is not RNNCell.")
        self._cell = cell
        self._attention_states = attention_states
        self.embedding = embedding

        with variable_scope.variable_scope(scope or "attention_decoder"):
            # batch_size = attention_states.get_shape()[0].value
            attn_length = attention_states.get_shape()[1].value
            attn_size = attention_states.get_shape()[2].value

            hidden = array_ops.reshape(
                attention_states, [-1, attn_length, 1, attn_size])
            hidden_features = []
            v = []
            attention_vec_size = attn_size  # Size of query vectors for attention.
            for a in xrange(num_heads):
                k = variable_scope.get_variable("AttnW_%d" % a,
                                                [1, 1, attn_size, attention_vec_size])
                hidden_features.append(nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME"))
                v.append(variable_scope.get_variable("AttnV_%d" % a,
                                                     [attention_vec_size]))

            batch_attn_size = array_ops.pack([batch_size, attn_size])
            self.attns = [array_ops.zeros(batch_attn_size, dtype=dtypes.float32)
                          for _ in xrange(num_heads)]

            def attention(query):
                """Put attention masks on hidden using hidden_features and query."""
                ds = []  # Results of attention reads will be stored here.
                for a in xrange(num_heads):
                    with variable_scope.variable_scope("Attention_%d" % a):
                        y = linear(query, attention_vec_size, True)
                        y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size])
                        # Attention mask is a softmax of v^T * tanh(...).
                        s = math_ops.reduce_sum(
                            v[a] * math_ops.tanh(hidden_features[a] + y), [2, 3])
                        a = nn_ops.softmax(s)
                        # Now calculate the attention-weighted vector d.
                        d = math_ops.reduce_sum(
                            array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden,
                            [1, 2])
                        ds.append(array_ops.reshape(d, [-1, attn_size]))
                return ds

            self.attention = attention

def get_vinyals_attention_function(attention_inputs, output_size, num_heads, scope=None):
  """
  This is the attention approach as defined by Vinyals and Kaiser on
  the paer 'Grammar as a Foreign Language'

  :param attention_inputs:
  :param output_size:
  :param num_heads:
  :return:
  """
  with tf.variable_scope(scope or "vinyals_attention", reuse=None) as attention_scope:

    top_states = [array_ops.reshape(e, [-1, 1, output_size])
                  for e in attention_inputs]
    attention_states = array_ops.concat(top_states, 1)
    if not attention_states.get_shape()[1:2].is_fully_defined():
      raise ValueError("Shape[1] and [2] of attention_states must be known: %s" % attention_states.get_shape())

    attn_length = attention_states.get_shape()[1].value
    attn_size = attention_states.get_shape()[2].value

    # To calculate W1 * h_t we use a 1-by-1 convolution, need to reshape before.
    hidden = array_ops.reshape(attention_states, [-1, attn_length, 1, attn_size])
    hidden_features = []
    v = []
    attention_vec_size = attn_size  # Size of query vectors for attention.
    for a in xrange(num_heads):
      k = tf.get_variable("AttnW_%d" % a, [1, 1, attn_size, attention_vec_size])
      hidden_features.append(nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME"))
      v.append(tf.get_variable("AttnV_%d" % a, [attention_vec_size]))

    def attention(query):
      """Put attention masks on hidden using hidden_features and query."""
      attn_weights = []
      ds = []  # Results of attention reads will be stored here.
      for i in xrange(num_heads):
        with tf.variable_scope("Attention_%d" % i):
          y = _linear(query, attention_vec_size, True)
          y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size])
          # Attention mask is a softmax of v^T * tanh(...).
          s = math_ops.reduce_sum(v[i] * math_ops.tanh(hidden_features[i] + y), [2, 3])
          a = nn_ops.softmax(s)
          attn_weights.append(a)
          # Now calculate the attention-weighted vector d.
          d = math_ops.reduce_sum(
            array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden, [1, 2])
          ds.append(array_ops.reshape(d, [-1, attn_size]))
      return attn_weights, ds

    return attention