Python tensorflow.python.ops.array_ops.squeeze() Examples

def _softmax_cross_entropy_loss(labels, logits, weights=None):
  with ops.name_scope(
      None, "softmax_cross_entropy_loss", (logits, labels,)) as name:
    labels = ops.convert_to_tensor(labels)
    # Check that we got integer for classification.
    if not labels.dtype.is_integer:
      raise ValueError("Labels dtype should be integer "
                       "Instead got %s." % labels.dtype)

    # sparse_softmax_cross_entropy_with_logits requires [batch_size] labels.
    is_squeezed_labels = False
    # TODO(ptucker): This will break for dynamic shapes.
    if len(labels.get_shape()) == 2:
      labels = array_ops.squeeze(labels, squeeze_dims=(1,))
      is_squeezed_labels = True

    loss = nn.sparse_softmax_cross_entropy_with_logits(
        labels=labels, logits=logits, name=name)

    # Restore squeezed dimension, if necessary, so loss matches weights shape.
    if is_squeezed_labels:
      loss = array_ops.expand_dims(loss, axis=(1,))

    return _compute_weighted_loss(loss, weights) 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    input_tensor = input_tensors[0]
    mask = input_tensors[1]
    if mask.get_shape().ndims > 1:
      mask = array_ops.squeeze(mask)

    if isinstance(input_tensor, sparse_tensor_py.SparseTensor):
      mask_fn = sparse_boolean_mask
    else:
      mask_fn = array_ops.boolean_mask

    # pylint: disable=not-callable
    return self.return_type(mask_fn(input_tensor, mask)) 

def call(self, inputs):
    # There is no TF op for 1D pooling, hence we make the inputs 4D.
    if self.data_format == 'channels_last':
      inputs = array_ops.expand_dims(inputs, 2)
      pool_shape = (1,) + self.pool_size + (1, 1)
      strides = (1,) + self.strides + (1, 1)
      data_format = 'NHWC'
    else:
      inputs = array_ops.expand_dims(inputs, 1)
      pool_shape = (1, 1) + self.pool_size + (1,)
      strides = (1, 1) + self.strides + (1,)
      data_format = 'NCHW'

    outputs = self.pool_function(
        inputs,
        ksize=pool_shape,
        strides=strides,
        padding=self.padding.upper(),
        data_format=data_format)

    if self.data_format == 'channels_last':
      return array_ops.squeeze(outputs, 2)
    else:
      return array_ops.squeeze(outputs, 1) 

def _define_partial_maximization_operation(self, shard_id, shard):
    """Computes the partial statistics of the means and covariances.

    Args:
      shard_id: current shard id.
      shard: current data shard, 1 X num_examples X dimensions.
    """
    # Soft assignment of each data point to each of the two clusters.
    self._points_in_k[shard_id] = math_ops.reduce_sum(
        self._w[shard_id], 0, keep_dims=True)
    # Partial means.
    w_mul_x = array_ops.expand_dims(
        math_ops.matmul(
            self._w[shard_id], array_ops.squeeze(shard, [0]), transpose_a=True),
        1)
    self._w_mul_x.append(w_mul_x)
    # Partial covariances.
    x = array_ops.concat([shard for _ in range(self._num_classes)], 0)
    x_trans = array_ops.transpose(x, perm=[0, 2, 1])
    x_mul_w = array_ops.concat([
        array_ops.expand_dims(x_trans[k, :, :] * self._w[shard_id][:, k], 0)
        for k in range(self._num_classes)
    ], 0)
    self._w_mul_x2.append(math_ops.matmul(x_mul_w, x)) 

def average_impurity(self):
    """Constructs a TF graph for evaluating the average leaf impurity of a tree.

    If in regression mode, this is the leaf variance. If in classification mode,
    this is the gini impurity.

    Returns:
      The last op in the graph.
    """
    children = array_ops.squeeze(array_ops.slice(
        self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
    is_leaf = math_ops.equal(constants.LEAF_NODE, children)
    leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
                                                 squeeze_dims=[1]))
    counts = array_ops.gather(self.variables.node_sums, leaves)
    gini = self._weighted_gini(counts)
    # Guard against step 1, when there often are no leaves yet.
    def impurity():
      return gini
    # Since average impurity can be used for loss, when there's no data just
    # return a big number so that loss always decreases.
    def big():
      return array_ops.ones_like(gini, dtype=dtypes.float32) * 10000000.
    return control_flow_ops.cond(math_ops.greater(
        array_ops.shape(leaves)[0], 0), impurity, big) 

def _define_diag_covariance_probs(self, shard_id, shard):
    """Defines the diagonal covariance probabilities per example in a class.

    Args:
      shard_id: id of the current shard.
      shard: current data shard, 1 X num_examples X dimensions.

    Returns a matrix num_examples * num_classes.
    """
    # num_classes X 1
    # TODO(xavigonzalvo): look into alternatives to log for
    # reparametrization of variance parameters.
    det_expanded = math_ops.reduce_sum(
        math_ops.log(self._covs + 1e-3), 1, keep_dims=True)
    diff = shard - self._means
    x2 = math_ops.square(diff)
    cov_expanded = array_ops.expand_dims(1.0 / (self._covs + 1e-3), 2)
    # num_classes X num_examples
    x2_cov = math_ops.matmul(x2, cov_expanded)
    x2_cov = array_ops.transpose(array_ops.squeeze(x2_cov, [2]))
    self._probs[shard_id] = -0.5 * (
        math_ops.to_float(self._dimensions) * math_ops.log(2.0 * np.pi) +
        array_ops.transpose(det_expanded) + x2_cov) 

def _define_partial_maximization_operation(self, shard_id, shard):
    """Computes the partial statistics of the means and covariances.

    Args:
      shard_id: current shard id.
      shard: current data shard, 1 X num_examples X dimensions.
    """
    # Soft assignment of each data point to each of the two clusters.
    self._points_in_k[shard_id] = math_ops.reduce_sum(
        self._w[shard_id], 0, keep_dims=True)
    # Partial means.
    w_mul_x = array_ops.expand_dims(
        math_ops.matmul(
            self._w[shard_id], array_ops.squeeze(shard, [0]), transpose_a=True),
        1)
    self._w_mul_x.append(w_mul_x)
    # Partial covariances.
    x = array_ops.concat([shard for _ in range(self._num_classes)], 0)
    x_trans = array_ops.transpose(x, perm=[0, 2, 1])
    x_mul_w = array_ops.concat([
        array_ops.expand_dims(x_trans[k, :, :] * self._w[shard_id][:, k], 0)
        for k in range(self._num_classes)
    ], 0)
    self._w_mul_x2.append(math_ops.matmul(x_mul_w, x)) 

def _define_diag_covariance_probs(self, shard_id, shard):
    """Defines the diagonal covariance probabilities per example in a class.

    Args:
      shard_id: id of the current shard.
      shard: current data shard, 1 X num_examples X dimensions.

    Returns a matrix num_examples * num_classes.
    """
    # num_classes X 1
    # TODO(xavigonzalvo): look into alternatives to log for
    # reparametrization of variance parameters.
    det_expanded = math_ops.reduce_sum(
        math_ops.log(self._covs + 1e-3), 1, keep_dims=True)
    diff = shard - self._means
    x2 = math_ops.square(diff)
    cov_expanded = array_ops.expand_dims(1.0 / (self._covs + 1e-3), 2)
    # num_classes X num_examples
    x2_cov = math_ops.matmul(x2, cov_expanded)
    x2_cov = array_ops.transpose(array_ops.squeeze(x2_cov, [2]))
    self._probs[shard_id] = -0.5 * (
        math_ops.to_float(self._dimensions) * math_ops.log(2.0 * np.pi) +
        array_ops.transpose(det_expanded) + x2_cov) 

def loss(self, data, labels):
    """The loss to minimize while training."""

    if self.is_regression:
      diff = self.training_inference_graph(data) - math_ops.to_float(labels)
      mean_squared_error = math_ops.reduce_mean(diff * diff)
      root_mean_squared_error = math_ops.sqrt(mean_squared_error, name="loss")
      loss = root_mean_squared_error
    else:
      loss = math_ops.reduce_mean(
          nn_ops.sparse_softmax_cross_entropy_with_logits(
              labels=array_ops.squeeze(math_ops.to_int32(labels)),
              logits=self.training_inference_graph(data)),
          name="loss")
    if self.regularizer:
      loss += layers.apply_regularization(self.regularizer,
                                          variables.trainable_variables())
    return loss 

def call(self, inputs):
    # There is no TF op for 1D pooling, hence we make the inputs 4D.
    if self.data_format == 'channels_last':
      inputs = array_ops.expand_dims(inputs, 2)
      pool_shape = (1,) + self.pool_size + (1, 1)
      strides = (1,) + self.strides + (1, 1)
      data_format = 'NHWC'
    else:
      inputs = array_ops.expand_dims(inputs, 1)
      pool_shape = (1, 1) + self.pool_size + (1,)
      strides = (1, 1) + self.strides + (1,)
      data_format = 'NCHW'

    outputs = self.pool_function(
        inputs,
        ksize=pool_shape,
        strides=strides,
        padding=self.padding.upper(),
        data_format=data_format)

    if self.data_format == 'channels_last':
      return array_ops.squeeze(outputs, 2)
    else:
      return array_ops.squeeze(outputs, 1) 

def average_impurity(self):
    """Constructs a TF graph for evaluating the average leaf impurity of a tree.

    If in regression mode, this is the leaf variance. If in classification mode,
    this is the gini impurity.

    Returns:
      The last op in the graph.
    """
    children = array_ops.squeeze(array_ops.slice(
        self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
    is_leaf = math_ops.equal(constants.LEAF_NODE, children)
    leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
                                                 squeeze_dims=[1]))
    counts = array_ops.gather(self.variables.node_sums, leaves)
    gini = self._weighted_gini(counts)
    # Guard against step 1, when there often are no leaves yet.
    def impurity():
      return gini
    # Since average impurity can be used for loss, when there's no data just
    # return a big number so that loss always decreases.
    def big():
      return array_ops.ones_like(gini, dtype=dtypes.float32) * 10000000.
    return control_flow_ops.cond(math_ops.greater(
        array_ops.shape(leaves)[0], 0), impurity, big) 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    input_tensor = input_tensors[0]
    mask = input_tensors[1]
    if mask.get_shape().ndims > 1:
      mask = array_ops.squeeze(mask)

    if isinstance(input_tensor, sparse_tensor_py.SparseTensor):
      mask_fn = sparse_boolean_mask
    else:
      mask_fn = array_ops.boolean_mask

    # pylint: disable=not-callable
    return self.return_type(mask_fn(input_tensor, mask)) 

def one_hot_wrapper(num_classes, loss_fn):
  """Some loss functions take one-hot labels."""
  def _loss(probs, targets):
    if targets.get_shape().ndims > 1:
      targets = array_ops.squeeze(targets, squeeze_dims=[1])
    one_hot_labels = array_ops.one_hot(
        math_ops.to_int32(targets),
        num_classes,
        on_value=1.,
        off_value=0.,
        dtype=dtypes.float32)
    return loss_fn(probs, one_hot_labels)
  return _loss 

def _streaming_at_threshold(streaming_metrics_fn, threshold):

  def _streaming_metrics(predictions, labels, weights=None):
    precision_tensor, update_op = streaming_metrics_fn(
        predictions,
        labels=labels,
        thresholds=[threshold],
        weights=_float_weights_or_none(weights))
    return array_ops.squeeze(precision_tensor), update_op

  return _streaming_metrics 

def crf_log_norm(inputs, sequence_lengths, transition_params):
  """Computes the normalization for a CRF.

  Args:
    inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
        to use as input to the CRF layer.
    sequence_lengths: A [batch_size] vector of true sequence lengths.
    transition_params: A [num_tags, num_tags] transition matrix.
  Returns:
    log_norm: A [batch_size] vector of normalizers for a CRF.
  """
  # Split up the first and rest of the inputs in preparation for the forward
  # algorithm.
  first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1])
  first_input = array_ops.squeeze(first_input, [1])
  rest_of_input = array_ops.slice(inputs, [0, 1, 0], [-1, -1, -1])

  # Compute the alpha values in the forward algorithm in order to get the
  # partition function.
  forward_cell = CrfForwardRnnCell(transition_params)
  _, alphas = rnn.dynamic_rnn(
      cell=forward_cell,
      inputs=rest_of_input,
      sequence_length=sequence_lengths - 1,
      initial_state=first_input,
      dtype=dtypes.float32)
  log_norm = math_ops.reduce_logsumexp(alphas, [1])
  return log_norm 

def _squeeze_and_onehot(targets, depth):
  targets = array_ops.squeeze(targets, squeeze_dims=[1])
  return array_ops.one_hot(math_ops.to_int32(targets), depth) 

def inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      # Compute activations for the neural network.
      nn_activations = layers.fully_connected(data, 1)

      # There is always one activation per instance by definition, so squeeze
      # away the extra dimension.
      return array_ops.squeeze(nn_activations, squeeze_dims=[1]) 

def _streaming_at_threshold(streaming_metrics_fn, threshold):

  def _streaming_metrics(predictions, labels, weights=None):
    precision_tensor, update_op = streaming_metrics_fn(
        predictions,
        labels=labels,
        thresholds=(threshold,),
        weights=_float_weights_or_none(weights))
    return array_ops.squeeze(precision_tensor), array_ops.squeeze(update_op)

  return _streaming_metrics 

def new_sparse_categorical_accuracy(y_true, y_pred):
	y_pred_rank = ops.convert_to_tensor(y_pred).get_shape().ndims
	y_true_rank = ops.convert_to_tensor(y_true).get_shape().ndims
	# If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
	if (y_true_rank is not None) and (y_pred_rank is not None) and (len(K.int_shape(y_true)) == len(K.int_shape(y_pred))):
		y_true = array_ops.squeeze(y_true, [-1])
	y_pred = math_ops.argmax(y_pred, axis=-1)
	# If the predicted output and actual output types don't match, force cast them
	# to match.
	if K.dtype(y_pred) != K.dtype(y_true):
		y_pred = math_ops.cast(y_pred, K.dtype(y_true))
	return math_ops.cast(math_ops.equal(y_true, y_pred), K.floatx())

#Load the model 

def tree_initialization(self):
    def _init_tree():
      return state_ops.scatter_update(self.variables.tree, [0], [[-1, -1]]).op

    def _nothing():
      return control_flow_ops.no_op()

    return control_flow_ops.cond(
        math_ops.equal(
            array_ops.squeeze(
                array_ops.strided_slice(self.variables.tree, [0, 0], [1, 1])),
            -2), _init_tree, _nothing) 

def compute_weighted_loss(losses, weights=1.0, scope=None):
  """Computes the weighted loss.

  Args:
    losses: A tensor of size [batch_size, d1, ... dN].
    weights: A tensor of size [1] or [batch_size, d1, ... dK] where K < N.
    scope: the scope for the operations performed in computing the loss.

  Returns:
    A scalar `Tensor` that returns the weighted loss.

  Raises:
    ValueError: If `weights` is `None` or the shape is not compatible with
      `losses`, or if the number of dimensions (rank) of either `losses` or
      `weights` is missing.
  """
  with ops.name_scope(scope, "weighted_loss", [losses, weights]):
    losses = ops.convert_to_tensor(losses)
    input_dtype = losses.dtype
    losses = math_ops.to_float(losses)
    weights = math_ops.to_float(ops.convert_to_tensor(weights))

    if losses.get_shape().ndims is None:
      raise ValueError("losses.get_shape().ndims cannot be None")
    weights_shape = weights.get_shape()
    if weights_shape.ndims is None:
      raise ValueError("weights.get_shape().ndims cannot be None")

    if weights_shape.ndims > 1 and weights_shape.dims[-1].is_compatible_with(1):
      weights = array_ops.squeeze(weights, [-1])

    total_loss = _scale_losses(losses, weights)
    num_present = _num_present(losses, weights)
    mean_loss = _safe_mean(total_loss, num_present)
    # convert the result back to the input type
    mean_loss = math_ops.cast(mean_loss, input_dtype)
    add_loss(mean_loss)
    return mean_loss 

def _solvevec(self, rhs, adjoint=False):
    """Default implementation of _solvevec."""
    rhs_mat = array_ops.expand_dims(rhs, axis=-1)
    solution_mat = self.solve(rhs_mat, adjoint=adjoint)
    return array_ops.squeeze(solution_mat, axis=-1) 

def _matvec(self, x, adjoint=False):
    x_mat = array_ops.expand_dims(x, axis=-1)
    y_mat = self.matmul(x_mat, adjoint=adjoint)
    return array_ops.squeeze(y_mat, axis=-1) 

def _softmax_cross_entropy_loss(logits, target):
  # Check that we got integer for classification.
  if not target.dtype.is_integer:
    raise ValueError("Target's dtype should be integer "
                     "Instead got %s." % target.dtype)
  # sparse_softmax_cross_entropy_with_logits requires [batch_size] target.
  if len(target.get_shape()) == 2:
    target = array_ops.squeeze(target, squeeze_dims=[1])
  loss_vec = nn.sparse_softmax_cross_entropy_with_logits(
      labels=target, logits=logits)
  return loss_vec 

def logits_to_predictions(self, logits, proba=False):
    if self.num_label_columns == 1:
      return array_ops.squeeze(logits, squeeze_dims=[1])
    return logits 

def logits_to_predictions(self, logits, proba=False):
    if self.num_label_columns == 1:
      return array_ops.squeeze(logits, squeeze_dims=[1])
    return logits 

def _streaming_recall_at_threshold(predictions, labels, weights, threshold):
  precision_tensor, update_op = metrics_lib.streaming_recall_at_thresholds(
      predictions, labels=labels, thresholds=(threshold,),
      weights=_float_weights_or_none(weights))
  return array_ops.squeeze(precision_tensor), array_ops.squeeze(update_op) 

def _streaming_precision_at_threshold(predictions, labels, weights, threshold):
  precision_tensor, update_op = metrics_lib.streaming_precision_at_thresholds(
      predictions, labels=labels, thresholds=(threshold,),
      weights=_float_weights_or_none(weights))
  return array_ops.squeeze(precision_tensor), array_ops.squeeze(update_op) 

def get_stats(self, session):
    num_nodes = self.variables.end_of_tree.eval(session=session) - 1
    num_leaves = array_ops.where(
        math_ops.equal(array_ops.squeeze(array_ops.slice(
            self.variables.tree, [0, 0], [-1, 1])), constants.LEAF_NODE)
        ).eval(session=session).shape[0]
    return TreeStats(num_nodes, num_leaves) 

def _softmax_cross_entropy_loss(logits, target):
  # Check that we got integer for classification.
  if not target.dtype.is_integer:
    raise ValueError("Target's dtype should be integer "
                     "Instead got %s." % target.dtype)
  # sparse_softmax_cross_entropy_with_logits requires [batch_size] target.
  if len(target.get_shape()) == 2:
    target = array_ops.squeeze(target, squeeze_dims=[1])
  loss_vec = nn.sparse_softmax_cross_entropy_with_logits(
      labels=target, logits=logits)
  return loss_vec