Python nn_ops.relu方法代码示例

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def vgg_arg_scope(weight_decay=0.0005):
  """Defines the VGG arg scope.

  Args:
    weight_decay: The l2 regularization coefficient.

  Returns:
    An arg_scope.
  """
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
      return arg_sc 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def relu_layer(x, weights, biases, name=None):
  """Computes Relu(x * weight + biases).

  Args:
    x: a 2D tensor.  Dimensions typically: batch, in_units
    weights: a 2D tensor.  Dimensions typically: in_units, out_units
    biases: a 1D tensor.  Dimensions: out_units
    name: A name for the operation (optional).  If not specified
      "nn_relu_layer" is used.

  Returns:
    A 2-D Tensor computing relu(matmul(x, weights) + biases).
    Dimensions typically: batch, out_units.
  """
  with ops.name_scope(name, "relu_layer", [x, weights, biases]) as name:
    x = ops.convert_to_tensor(x, name="x")
    weights = ops.convert_to_tensor(weights, name="weights")
    biases = ops.convert_to_tensor(biases, name="biases")
    xw_plus_b = nn_ops.bias_add(math_ops.matmul(x, weights), biases)
    return nn_ops.relu(xw_plus_b, name=name) 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def hinge_loss(logits, labels=None, scope=None):
  """Method that returns the loss tensor for hinge loss.

  Args:
    logits: The logits, a float tensor.
    labels: The ground truth output tensor. Its shape should match the shape of
      logits. The values of the tensor are expected to be 0.0 or 1.0.
    scope: The scope for the operations performed in computing the loss.

  Returns:
    A `Tensor` of same shape as `logits` and `labels` representing the loss
      values across the batch.

  Raises:
    ValueError: If the shapes of `logits` and `labels` don't match.
  """
  with ops.name_scope(scope, "hinge_loss", [logits, labels]) as scope:
    logits.get_shape().assert_is_compatible_with(labels.get_shape())
    # We first need to convert binary labels to -1/1 labels (as floats).
    labels = math_ops.to_float(labels)
    all_ones = array_ops.ones_like(labels)
    labels = math_ops.subtract(2 * labels, all_ones)
    return nn_ops.relu(
        math_ops.subtract(all_ones, math_ops.multiply(labels, logits))) 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def test_unary_ops(self):
    ops = [
        ('relu', nn_ops.relu, nn.relu),
        ('relu6', nn_ops.relu6, nn.relu6),
        ('crelu', nn_ops.crelu, nn.crelu),
        ('elu', nn_ops.elu, nn.elu),
        ('softplus', nn_ops.softplus, nn.softplus),
        ('l2_loss', nn_ops.l2_loss, nn.l2_loss),
        ('softmax', nn_ops.softmax, nn.softmax),
        ('log_softmax', nn_ops.log_softmax, nn.log_softmax),
    ]
    for op_name, tf_op, lt_op in ops:
      golden_tensor = tf_op(self.original_lt.tensor)
      golden_lt = core.LabeledTensor(golden_tensor, self.axes)
      actual_lt = lt_op(self.original_lt)
      self.assertIn(op_name, actual_lt.name)
      self.assertLabeledTensorsEqual(golden_lt, actual_lt) 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def _fully_connected_basic_use(self, x, num_output_units, expected_shape):
    output = _layers.legacy_fully_connected(
        x, num_output_units, activation_fn=nn_ops.relu)

    with session.Session() as sess:
      with self.assertRaises(errors_impl.FailedPreconditionError):
        sess.run(output)

      variables_lib.global_variables_initializer().run()
      out_value, shape_value = sess.run([output, array_ops.shape(output)])

    self.assertAllClose(shape_value, expected_shape)
    self.assertEqual(output.get_shape().as_list(), expected_shape)
    self.assertTrue(np.all(out_value >= 0), 'Relu should have all values >= 0.')

    self.assertEqual(2,
                     len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)))
    self.assertEqual(
        0, len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES))) 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def vgg_arg_scope(weight_decay=0.0005):
    """Defines the VGG arg scope.

    Args:
      weight_decay: The l2 regularization coefficient.

    Returns:
      An arg_scope.
    """
    with arg_scope(
        [layers.conv2d, layers_lib.fully_connected],
        activation_fn=nn_ops.relu,
        weights_regularizer=regularizers.l2_regularizer(weight_decay),
        biases_initializer=init_ops.zeros_initializer()
    ):
        with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
            return arg_sc 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def hinge_loss(logits, labels=None, scope=None, target=None):
  """Method that returns the loss tensor for hinge loss.

  Args:
    logits: The logits, a float tensor.
    labels: The ground truth output tensor. Its shape should match the shape of
      logits. The values of the tensor are expected to be 0.0 or 1.0.
    scope: The scope for the operations performed in computing the loss.
    target: Deprecated alias for `labels`.

  Returns:
    A `Tensor` of same shape as logits and target representing the loss values
      across the batch.

  Raises:
    ValueError: If the shapes of `logits` and `labels` don't match.
  """
  labels = _labels(labels, target)
  with ops.name_scope(scope, "hinge_loss", [logits, labels]) as scope:
    logits.get_shape().assert_is_compatible_with(labels.get_shape())
    # We first need to convert binary labels to -1/1 labels (as floats).
    labels = math_ops.to_float(labels)
    all_ones = array_ops.ones_like(labels)
    labels = math_ops.sub(2 * labels, all_ones)
    return nn_ops.relu(math_ops.sub(all_ones, math_ops.mul(labels, logits))) 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def network_arg_scope(is_training=True,
                      weight_decay=cfg.train.weight_decay,
                      batch_norm_decay=0.997,
                      batch_norm_epsilon=1e-5,
                      batch_norm_scale=True):
    batch_norm_params = {
        'is_training': is_training, 'decay': batch_norm_decay,
        'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale,
        'updates_collections': ops.GraphKeys.UPDATE_OPS,
        #'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ],
        'trainable': cfg.train.bn_training,
    }

    with slim.arg_scope(
            [slim.conv2d, slim.separable_convolution2d],
            weights_regularizer=slim.l2_regularizer(weight_decay),
            weights_initializer=slim.variance_scaling_initializer(),
            trainable=is_training,
            activation_fn=tf.nn.relu6,
            #activation_fn=tf.nn.relu,
            normalizer_fn=slim.batch_norm,
            normalizer_params=batch_norm_params,
            padding='SAME'):
        with slim.arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc:
            return arg_sc 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def __init__(self, num_units, forget_bias=1.0, reuse_norm=False,
               input_size=None, activation=nn_ops.relu,
               layer_norm=True, norm_gain=1.0, norm_shift=0.0,
               loop_steps=1, decay_rate=0.9, learning_rate=0.5,
               dropout_keep_prob=1.0, dropout_prob_seed=None):

    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)

    self._num_units = num_units
    self._activation = activation
    self._forget_bias = forget_bias
    self._reuse_norm = reuse_norm
    self._keep_prob = dropout_keep_prob
    self._seed = dropout_prob_seed
    self._layer_norm = layer_norm
    self._S = loop_steps
    self._eta = learning_rate
    self._lambda = decay_rate
    self._g = norm_gain
    self._b = norm_shift 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def inception_v2_arg_scope(weight_decay=0.00004,
                           batch_norm_var_collection='moving_vars'):
  """Defines the default InceptionV2 arg scope.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    batch_norm_var_collection: The name of the collection for the batch norm
      variables.

  Returns:
    An `arg_scope` to use for the inception v3 model.
  """
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': 0.9997,
      # epsilon to prevent 0s in variance.
      'epsilon': 0.001,
      # collection containing update_ops.
      'updates_collections': ops.GraphKeys.UPDATE_OPS,
      # collection containing the moving mean and moving variance.
      'variables_collections': {
          'beta': None,
          'gamma': None,
          'moving_mean': [batch_norm_var_collection],
          'moving_variance': [batch_norm_var_collection],
      }
  }

  # Set weight_decay for weights in Conv and FC layers.
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope(
        [layers.conv2d],
        weights_initializer=initializers.variance_scaling_initializer(),
        activation_fn=nn_ops.relu,
        normalizer_fn=layers_lib.batch_norm,
        normalizer_params=batch_norm_params) as sc:
      return sc 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def alexnet_v2_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      biases_initializer=init_ops.constant_initializer(0.1),
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def hinge_loss(labels, logits, weights=1.0, scope=None,
               loss_collection=ops.GraphKeys.LOSSES,
               reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
  """Adds a hinge loss to the training procedure.

  Args:
    labels: The ground truth output tensor. Its shape should match the shape of
      logits. The values of the tensor are expected to be 0.0 or 1.0.
    logits: The logits, a float tensor.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `losses` dimension).
    scope: The scope for the operations performed in computing the loss.
    loss_collection: collection to which the loss will be added.
    reduction: Type of reduction to apply to loss.

  Returns:
    Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same
    shape as `labels`; otherwise, it is scalar.

  Raises:
    ValueError: If the shapes of `logits` and `labels` don't match.
  """
  with ops.name_scope(scope, "hinge_loss", (logits, labels)) as scope:
    logits = math_ops.to_float(logits)
    labels = math_ops.to_float(labels)
    logits.get_shape().assert_is_compatible_with(labels.get_shape())
    # We first need to convert binary labels to -1/1 labels (as floats).
    all_ones = array_ops.ones_like(labels)
    labels = math_ops.subtract(2 * labels, all_ones)
    losses = nn_ops.relu(
        math_ops.subtract(all_ones, math_ops.multiply(labels, logits)))
    return compute_weighted_loss(
        losses, weights, scope, loss_collection, reduction=reduction) 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def zero_fraction(value, name=None):
  """Returns the fraction of zeros in `value`.

  If `value` is empty, the result is `nan`.

  This is useful in summaries to measure and report sparsity.  For example,

  ```python
      z = tf.nn.relu(...)
      summ = tf.summary.scalar('sparsity', tf.nn.zero_fraction(z))
  ```

  Args:
    value: A tensor of numeric type.
    name: A name for the operation (optional).

  Returns:
    The fraction of zeros in `value`, with type `float32`.
  """
  with ops.name_scope(name, "zero_fraction", [value]):
    value = ops.convert_to_tensor(value, name="value")
    zero = constant_op.constant(0, dtype=value.dtype, name="zero")
    return math_ops.reduce_mean(
        math_ops.cast(math_ops.equal(value, zero), dtypes.float32))


# pylint: disable=redefined-builtin 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def __init__(self, num_units, num_in_proj=None,
               initializer=None, forget_bias=1.0,
               y_activation=nn_ops.relu, reuse=None):
    """Initialize the parameters for an +RNN cell.

    Args:
      num_units: int, The number of units in the +RNN cell
      num_in_proj: (optional) int, The input dimensionality for the RNN.
        If creating the first layer of an +RNN, this should be set to
        `num_units`. Otherwise, this should be set to `None` (default).
        If `None`, dimensionality of `inputs` should be equal to `num_units`,
        otherwise ValueError is thrown.
      initializer: (optional) The initializer to use for the weight matrices.
      forget_bias: (optional) float, default 1.0, The initial bias of the
        forget gates, used to reduce the scale of forgetting at the beginning
        of the training.
      y_activation: (optional) Activation function of the states passed
        through depth. Default is 'tf.nn.relu`.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
    """
    super(IntersectionRNNCell, self).__init__(_reuse=reuse)
    self._num_units = num_units
    self._initializer = initializer
    self._forget_bias = forget_bias
    self._num_input_proj = num_in_proj
    self._y_activation = y_activation
    self._reuse = reuse 

# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import relu [as 别名]
def overfeat_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc