Python tensorflow.tensordot函数代码示例

  def testBayesianLinearModel(self):
    """Tests that model makes reasonable predictions."""
    np.random.seed(42)
    train_batch_size = 5
    test_batch_size = 2
    num_features = 3
    noise_variance = 0.01
    coeffs = tf.range(num_features, dtype=tf.float32)
    features = tf.to_float(np.random.randn(train_batch_size, num_features))
    labels = (tf.tensordot(features, coeffs, [[-1], [0]])
              + noise_variance * tf.to_float(np.random.randn(train_batch_size)))

    model = bayes.BayesianLinearModel(noise_variance=noise_variance)
    model.fit(features, labels)

    test_features = tf.to_float(np.random.randn(test_batch_size, num_features))
    test_labels = tf.tensordot(test_features, coeffs, [[-1], [0]])
    outputs = model(test_features)
    test_predictions = outputs.distribution.mean()
    test_predictions_variance = outputs.distribution.variance()

    [
        test_labels_val, test_predictions_val, test_predictions_variance_val,
    ] = self.evaluate(
        [test_labels, test_predictions, test_predictions_variance])
    self.assertEqual(test_predictions_val.shape, (test_batch_size,))
    self.assertEqual(test_predictions_variance_val.shape, (test_batch_size,))
    self.assertAllClose(test_predictions_val, test_labels_val, atol=0.1)
    self.assertAllLessEqual(test_predictions_variance_val, noise_variance)

  def melspecgrams_to_specgrams(self, melspecgrams):
    """Converts melspecgrams to specgrams.

    Args:
      melspecgrams: Tensor of log magnitudes and instantaneous frequencies,
        shape [batch, time, freq, 2], mel scaling of frequencies.

    Returns:
      specgrams: Tensor of log magnitudes and instantaneous frequencies,
        shape [batch, time, freq, 2].
    """
    if self._mel_downscale is None:
      return melspecgrams

    logmelmag2 = melspecgrams[:, :, :, 0]
    mel_p = melspecgrams[:, :, :, 1]

    mel2l = tf.to_float(self._mel_to_linear_matrix())
    mag2 = tf.tensordot(tf.exp(logmelmag2), mel2l, 1)
    logmag = 0.5 * self._safe_log(mag2)
    mel_phase_angle = tf.cumsum(mel_p * np.pi, axis=-2)
    phase_angle = tf.tensordot(mel_phase_angle, mel2l, 1)
    p = spectral_ops.instantaneous_frequency(phase_angle)

    return tf.concat(
        [logmag[:, :, :, tf.newaxis], p[:, :, :, tf.newaxis]], axis=-1)

  def call(self, x):
    """Execute this layer on input tensors.

    Parameters
    ----------
    x: list of Tensor 
      should be [atom_features(batch_size*max_n_atoms*n_embedding), 
                 distance_matrix(batch_size*max_n_atoms*max_n_atoms*n_distance), 
                 distance_matrix_mask(batch_size*max_n_atoms*max_n_atoms)]

    Returns
    -------
    tf.Tensor
      new embeddings for atoms, same shape as x[0]
    """
    self.build()
    atom_features = x[0]
    distance_matrix = x[1]
    distance_matrix_mask = x[2]
    outputs = tf.multiply(
        (tf.tensordot(distance_matrix, self.W_df, [[3], [0]]) + self.b_df),
        tf.expand_dims(
            tf.tensordot(atom_features, self.W_cf, [[2], [0]]) + self.b_cf,
            axis=1))
    # for atom i in a molecule m, this step multiplies together distance info of atom pair(i,j)
    # and embeddings of atom j(both gone through a hidden layer)
    outputs = tf.tensordot(outputs, self.W_fc, [[3], [0]])
    outputs = tf.multiply(outputs, tf.expand_dims(distance_matrix_mask, axis=3))
    # masking the outputs tensor for pair(i,i) and all paddings
    outputs = self.activation(outputs)
    outputs = tf.reduce_sum(outputs, axis=2) + atom_features
    # for atom i, sum the influence from all other atom j in the molecule

    return outputs

  def specgrams_to_melspecgrams(self, specgrams):
    """Converts specgrams to melspecgrams.

    Args:
      specgrams: Tensor of log magnitudes and instantaneous frequencies,
        shape [batch, time, freq, 2].

    Returns:
      melspecgrams: Tensor of log magnitudes and instantaneous frequencies,
        shape [batch, time, freq, 2], mel scaling of frequencies.
    """
    if self._mel_downscale is None:
      return specgrams

    logmag = specgrams[:, :, :, 0]
    p = specgrams[:, :, :, 1]

    mag2 = tf.exp(2.0 * logmag)
    phase_angle = tf.cumsum(p * np.pi, axis=-2)

    l2mel = tf.to_float(self._linear_to_mel_matrix())
    logmelmag2 = self._safe_log(tf.tensordot(mag2, l2mel, 1))
    mel_phase_angle = tf.tensordot(phase_angle, l2mel, 1)
    mel_p = spectral_ops.instantaneous_frequency(mel_phase_angle)

    return tf.concat(
        [logmelmag2[:, :, :, tf.newaxis], mel_p[:, :, :, tf.newaxis]], axis=-1)

def ising_hamiltonian(N, dtype):
    X = tf.convert_to_tensor([[0.0, 1.0], [1.0, 0.0]], dtype=dtype)
    Z = tf.convert_to_tensor([[1.0, 0.0], [0.0, -1.0]], dtype=dtype)
    I = tf.eye(2, dtype=dtype)
    h = -tf.tensordot(X, X, axes=0) - tf.tensordot(Z, I, axes=0)
    h_last = h - tf.tensordot(I, Z, axes=0)
    h = tf.transpose(h, (0,2,1,3))
    h_last = tf.transpose(h_last, (0,2,1,3))
    H = [h]*(N-2) + [h_last]
    return H

def attention(query, facts, attention_size, mask, stag='null', mode='LIST', softmax_stag=1, time_major=False, return_alphas=False):
    if isinstance(facts, tuple):
        # In case of Bi-RNN, concatenate the forward and the backward RNN
        # outputs.
        facts = tf.concat(facts, 2)

    if time_major:
        # (T,B,D) => (B,T,D)
        facts = tf.array_ops.transpose(facts, [1, 0, 2])

    mask = tf.equal(mask, tf.ones_like(mask))
    # D value - hidden size of the RNN layer
    hidden_size = facts.get_shape().as_list()[-1]
    input_size = query.get_shape().as_list()[-1]

    # Trainable parameters
    w1 = tf.Variable(tf.random_normal(
        [hidden_size, attention_size], stddev=0.1))
    w2 = tf.Variable(tf.random_normal(
        [input_size, attention_size], stddev=0.1))
    b = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
    v = tf.Variable(tf.random_normal([attention_size], stddev=0.1))

    with tf.name_scope('v'):
        # Applying fully connected layer with non-linear activation to each of the B*T timestamps;
        #  the shape of `tmp` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
        tmp1 = tf.tensordot(facts, w1, axes=1)
        tmp2 = tf.tensordot(query, w2, axes=1)
        tmp2 = tf.reshape(tmp2, [-1, 1, tf.shape(tmp2)[-1]])
        tmp = tf.tanh((tmp1 + tmp2) + b)

    # For each of the timestamps its vector of size A from `tmp` is reduced
    # with `v` vector
    v_dot_tmp = tf.tensordot(tmp, v, axes=1, name='v_dot_tmp')  # (B,T) shape
    key_masks = mask  # [B, 1, T]
    # key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
    paddings = tf.ones_like(v_dot_tmp) * (-2 ** 32 + 1)
    v_dot_tmp = tf.where(key_masks, v_dot_tmp, paddings)  # [B, 1, T]
    alphas = tf.nn.softmax(v_dot_tmp, name='alphas')         # (B,T) shape

    # Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
    #output = tf.reduce_sum(facts * tf.expand_dims(alphas, -1), 1)
    output = facts * tf.expand_dims(alphas, -1)
    output = tf.reshape(output, tf.shape(facts))
    # output = output / (facts.get_shape().as_list()[-1] ** 0.5)
    if not return_alphas:
        return output
    else:
        return output, alphas

def lovasz_softmax_flat(probas, labels, classes='all'):
    """
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
    """
    C = probas.shape[1]
    losses = []
    present = []
    class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
    for c in class_to_sum:
        fg = tf.cast(tf.equal(labels, c), probas.dtype)  # foreground for class c
        if classes == 'present':
            present.append(tf.reduce_sum(fg) > 0)
        errors = tf.abs(fg - probas[:, c])
        errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], name="descending_sort_{}".format(c))
        fg_sorted = tf.gather(fg, perm)
        grad = lovasz_grad(fg_sorted)
        losses.append(
            tf.tensordot(errors_sorted, tf.stop_gradient(grad), 1, name="loss_class_{}".format(c))
                      )
    if len(class_to_sum) == 1:  # short-circuit mean when only one class
        return losses[0]
    losses_tensor = tf.stack(losses)
    if classes == 'present':
        present = tf.stack(present)
        losses_tensor = tf.boolean_mask(losses_tensor, present)
    loss = tf.reduce_mean(losses_tensor)
    return loss

  def outer_product(self, node1: Node, node2: Node,
                    name: Optional[Text] = None) -> Node:
    """Calcuates an outer product of the two nodes.

    This causes the nodes to combine their edges and axes, so the shapes are
    combined. For example, if `a` had a shape (2, 3) and `b` had a shape
    (4, 5, 6), then the node `net.outer_product(a, b) will have shape
    (2, 3, 4, 5, 6).

    Args:
      node1: The first node. The axes on this node will be on the left side of
        the new node.
      node2: The second node. The axes on this node will be on the right side of
        the new node.
      name: Optional name to give the new node created.

    Returns:
      new_node: A new node. It's shape will be node1.shape + node2.shape
    """
    new_tensor = tf.tensordot(node1.tensor, node2.tensor, 0)
    new_node = self.add_node(new_tensor, name)
    additional_axes = len(node1.tensor.shape)
    for i, edge in enumerate(node1.edges):
      edge.update_axis(i, node1, i, new_node)
    for i, edge in enumerate(node2.edges):
      edge.update_axis(i, node2, i + additional_axes, new_node)
    # Remove the nodes from the set.
    self.nodes_set.remove(node1)
    self.nodes_set.remove(node2)
    for i, edge in enumerate(node1.edges + node2.edges):
      new_node.add_edge(edge, i)
    return new_node

  def contract(self, edge: Edge, name: Optional[Text] = None) -> Node:
    """Contract an edge connecting two nodes in the TensorNetwork.

    Args:
      edge: The edge contract next.
      name: Name of the new node created.

    Returns:
      new_node: The new node created after the contraction.

    Raises:
      ValueError: When edge is a dangling edge or if it already has been
        contracted.
    """
    if not edge.is_being_used() or edge.node1 not in self.nodes_set:
      raise ValueError("Attempting to contract edge '{}' that is not part of "
                       "the network.".format(edge))
    if edge.is_dangling():
      raise ValueError("Attempting to contract dangling edge")
    if edge.node1 is edge.node2:
      return self._contract_trace(edge, name)
    new_tensor = tf.tensordot(edge.node1.tensor, edge.node2.tensor,
                              [[edge.axis1], [edge.axis2]])
    new_node = self.add_node(new_tensor, name)
    self._remove_edge(edge, new_node)
    return new_node

  def tensordot(self,inp,out_dim,in_dim=None,activation=None,use_bias=False,w_name="batch-fnn-W"):
    '''
    function: the implement of FNN ,input is a 3D batch tesor,W is a 2D tensor
    :param input: a 3D tensor of (b,seq_len,h)
    :param out_dim: the out_dim of W
    :param in_dim: the in_dim of W
    :param activation: activation function
    :param use_bias: use bias or not
    :param w_name: the unique name for W
    :return: (b,seq_len,in_dim)*(in_dim,out_dim) ->(b,seq_len,out_dim)
    '''
    with tf.variable_scope("3D-batch-fnn-layer"):
      inp_shape = inp.get_shape().as_list()
      batch_size= inp_shape[0]
      seq_len = inp_shape[1]

      if in_dim==None:
        in_dim = inp_shape[-1]
      
      W = tf.get_variable(w_name,shape=[in_dim,out_dim])
      out = tf.tensordot(inp,W,axes=1)

      if use_bias == True:
        b_name = w_name + '-b'
        b = tf.get_variable(b_name, shape=[out_dim])
        out = out + b

      if activation is not None:
        out = activation(out)
      out.set_shape([batch_size,seq_len,out_dim])
      return out

def boolean_mask(boxlist, indicator, fields=None, scope=None,
                 use_static_shapes=False, indicator_sum=None):
  """Select boxes from BoxList according to indicator and return new BoxList.

  `boolean_mask` returns the subset of boxes that are marked as "True" by the
  indicator tensor. By default, `boolean_mask` returns boxes corresponding to
  the input index list, as well as all additional fields stored in the boxlist
  (indexing into the first dimension).  However one can optionally only draw
  from a subset of fields.

  Args:
    boxlist: BoxList holding N boxes
    indicator: a rank-1 boolean tensor
    fields: (optional) list of fields to also gather from.  If None (default),
      all fields are gathered from.  Pass an empty fields list to only gather
      the box coordinates.
    scope: name scope.
    use_static_shapes: Whether to use an implementation with static shape
      gurantees.
    indicator_sum: An integer containing the sum of `indicator` vector. Only
      required if `use_static_shape` is True.

  Returns:
    subboxlist: a BoxList corresponding to the subset of the input BoxList
      specified by indicator
  Raises:
    ValueError: if `indicator` is not a rank-1 boolean tensor.
  """
  with tf.name_scope(scope, 'BooleanMask'):
    if indicator.shape.ndims != 1:
      raise ValueError('indicator should have rank 1')
    if indicator.dtype != tf.bool:
      raise ValueError('indicator should be a boolean tensor')
    if use_static_shapes:
      if not (indicator_sum and isinstance(indicator_sum, int)):
        raise ValueError('`indicator_sum` must be a of type int')
      selected_positions = tf.to_float(indicator)
      indexed_positions = tf.cast(
          tf.multiply(
              tf.cumsum(selected_positions), selected_positions),
          dtype=tf.int32)
      one_hot_selector = tf.one_hot(
          indexed_positions - 1, indicator_sum, dtype=tf.float32)
      sampled_indices = tf.cast(
          tf.tensordot(
              tf.to_float(tf.range(tf.shape(indicator)[0])),
              one_hot_selector,
              axes=[0, 0]),
          dtype=tf.int32)
      return gather(boxlist, sampled_indices, use_static_shapes=True)
    else:
      subboxlist = box_list.BoxList(tf.boolean_mask(boxlist.get(), indicator))
      if fields is None:
        fields = boxlist.get_extra_fields()
      for field in fields:
        if not boxlist.has_field(field):
          raise ValueError('boxlist must contain all specified fields')
        subfieldlist = tf.boolean_mask(boxlist.get_field(field), indicator)
        subboxlist.add_field(field, subfieldlist)
      return subboxlist

def matmul(A, B, transpose_a=False, transpose_b=False):
    '''Batch support for matrix matrix product.

    Args:
        A: General matrix of size (A_Batch, M, X).
        B: General matrix of size (B_Batch, X, N).
        transpose_a: Whether A is transposed (A_Batch, X, M).
        transpose_b: Whether B is transposed (B_Batch, N, X).

    Returns:
        The result of multiplying A with B (A_Batch, B_Batch, M, N).
        Works more efficiently if B_Batch is empty.
    '''
    Andim = len(A.shape)
    Bndim = len(B.shape)
    if Andim == Bndim:
        return tf.matmul(A, B, transpose_a=transpose_a,
                         transpose_b=transpose_b)  # faster than tensordot
    with tf.name_scope('matmul'):
        a_index = Andim - (2 if transpose_a else 1)
        b_index = Bndim - (1 if transpose_b else 2)
        AB = tf.tensordot(A, B, axes=[a_index, b_index])
        if Bndim > 2:  # only if B is batched, rearrange the axes
            A_Batch = np.arange(Andim - 2)
            M = len(A_Batch)
            B_Batch = (M + 1) + np.arange(Bndim - 2)
            N = (M + 1) + len(B_Batch)
            perm = np.concatenate((A_Batch, B_Batch, [M, N]))
            AB = tf.transpose(AB, perm)
        return AB

  def _get_values_from_start_and_end(self, input_tensor, num_start_samples,
                                     num_end_samples, total_num_samples):
    """slices num_start_samples and last num_end_samples from input_tensor.

    Args:
      input_tensor: An int32 tensor of shape [N] to be sliced.
      num_start_samples: Number of examples to be sliced from the beginning
        of the input tensor.
      num_end_samples: Number of examples to be sliced from the end of the
        input tensor.
      total_num_samples: Sum of is num_start_samples and num_end_samples. This
        should be a scalar.

    Returns:
      A tensor containing the first num_start_samples and last num_end_samples
      from input_tensor.

    """
    input_length = tf.shape(input_tensor)[0]
    start_positions = tf.less(tf.range(input_length), num_start_samples)
    end_positions = tf.greater_equal(
        tf.range(input_length), input_length - num_end_samples)
    selected_positions = tf.logical_or(start_positions, end_positions)
    selected_positions = tf.cast(selected_positions, tf.int32)
    indexed_positions = tf.multiply(tf.cumsum(selected_positions),
                                    selected_positions)
    one_hot_selector = tf.one_hot(indexed_positions - 1,
                                  total_num_samples,
                                  dtype=tf.int32)
    return tf.tensordot(input_tensor, one_hot_selector, axes=[0, 0])

    def save(self, sess, dst_path):
        if os.path.exists(dst_path):
            shutil.rmtree(dst_path)
        with self.__graph.as_default():
            x = tf.placeholder(dtype=tf.int32, shape=[None])
            y = tf.nn.embedding_lookup(self.__normalized_embedding, x)

            x_similiar_score = tf.tensordot(self.__normalized_embedding,tf.transpose(y), 1)

            x_tensor_info = tf.saved_model.utils.build_tensor_info(x)
            y_tensor_info = tf.saved_model.utils.build_tensor_info(y)
            x_similiar_score_info = tf.saved_model.utils.build_tensor_info(x_similiar_score)
            signature_map = tf.saved_model.signature_def_utils.build_signature_def(
                inputs={
                    "word": x_tensor_info
                },
                outputs={
                    "embedding": y_tensor_info,
                    "x_similiar_score": x_similiar_score_info
                },
                method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
            )
            builder = tf.saved_model.builder.SavedModelBuilder(dst_path)

            builder.add_meta_graph_and_variables(sess,
                                                 [tf.saved_model.tag_constants.SERVING],
                                                 signature_def_map={"word_embedding": signature_map}
                                                 )
            builder.save()

def reshape_stft(stfts, num_mel_bins):
    magnitude_spectrograms = tf.abs(stfts)
    num_spectrogram_bins = magnitude_spectrograms.shape[-1].value

    # scale frequency to mel scale and put into bins to reduce dimensionality
    lower_edge_hertz, upper_edge_hertz = 30.0, 17000.0

    linear_to_mel_weight_matrix = tf.contrib.signal.linear_to_mel_weight_matrix(
        num_mel_bins, num_spectrogram_bins, utils.sample_rate, lower_edge_hertz,
        upper_edge_hertz)
    mel_spectrograms = tf.tensordot(magnitude_spectrograms, linear_to_mel_weight_matrix, 1)
    mel_spectrograms.set_shape(
        magnitude_spectrograms.shape[:-1].concatenate(linear_to_mel_weight_matrix.shape[-1:]))

    # log scale the mel bins to better represent human loudness perception
    log_offset = 1e-6
    log_mel_spectrograms = tf.log(mel_spectrograms + log_offset)

    # compute first order differential and concat. "It indicates a raise or reduction of the energy for each
    # frequency bin at a frame relative to its predecessor"
    first_order_diff = tf.abs(
        tf.subtract(log_mel_spectrograms, tf.manip.roll(log_mel_spectrograms, shift=1, axis=1)))
    mel_fod = tf.concat([log_mel_spectrograms, first_order_diff], 1)

    return mel_fod