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Python backend.name_scope函数代码示例

本文整理汇总了Python中tensorflow.python.keras._impl.keras.backend.name_scope函数的典型用法代码示例。如果您正苦于以下问题:Python name_scope函数的具体用法?Python name_scope怎么用?Python name_scope使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。


在下文中一共展示了name_scope函数的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: __init__

 def __init__(self, lr=0.01, momentum=0., decay=0., nesterov=False, **kwargs):
   super(SGD, self).__init__(**kwargs)
   with K.name_scope(self.__class__.__name__):
     self.iterations = K.variable(0, dtype='int64', name='iterations')
     self.lr = K.variable(lr, name='lr')
     self.momentum = K.variable(momentum, name='momentum')
     self.decay = K.variable(decay, name='decay')
   self.initial_decay = decay
   self.nesterov = nesterov
开发者ID:AndrewTwinz,项目名称:tensorflow,代码行数:9,代码来源:optimizers.py

示例2: _separable_conv_block

def _separable_conv_block(ip,
                          filters,
                          kernel_size=(3, 3),
                          strides=(1, 1),
                          block_id=None):
  """Adds 2 blocks of [relu-separable conv-batchnorm].

  Arguments:
      ip: Input tensor
      filters: Number of output filters per layer
      kernel_size: Kernel size of separable convolutions
      strides: Strided convolution for downsampling
      block_id: String block_id

  Returns:
      A Keras tensor
  """
  channel_dim = 1 if K.image_data_format() == 'channels_first' else -1

  with K.name_scope('separable_conv_block_%s' % block_id):
    x = Activation('relu')(ip)
    x = SeparableConv2D(
        filters,
        kernel_size,
        strides=strides,
        name='separable_conv_1_%s' % block_id,
        padding='same',
        use_bias=False,
        kernel_initializer='he_normal')(
            x)
    x = BatchNormalization(
        axis=channel_dim,
        momentum=0.9997,
        epsilon=1e-3,
        name='separable_conv_1_bn_%s' % (block_id))(
            x)
    x = Activation('relu')(x)
    x = SeparableConv2D(
        filters,
        kernel_size,
        name='separable_conv_2_%s' % block_id,
        padding='same',
        use_bias=False,
        kernel_initializer='he_normal')(
            x)
    x = BatchNormalization(
        axis=channel_dim,
        momentum=0.9997,
        epsilon=1e-3,
        name='separable_conv_2_bn_%s' % (block_id))(
            x)
  return x
开发者ID:ChengYuXiang,项目名称:tensorflow,代码行数:52,代码来源:nasnet.py

示例3: _eager_metrics_fn

def _eager_metrics_fn(model, outputs, targets):
  """Calculates the metrics for each output of the given model.

  Arguments:
      model: The model on which metrics are being calculated.
      outputs: The outputs of the given model.
      targets: The predictions or targets of the given model.

  Returns:
      Returns the metric names and metric results for each output of the model.
  """
  metric_names = []
  metric_results = []
  if not isinstance(outputs, list):
    outputs = [outputs]

  if not isinstance(targets, list):
    targets = [targets]

  for i in range(len(model.outputs)):
    output_metrics = model.nested_metrics[i]
    for nested_output_metric in output_metrics:
      metric_name, metric_fn = _get_metrics_info(
          nested_output_metric, backend.int_shape(model.outputs[i]),
          model.loss_functions[i])

      if len(model.output_names) > 1:
        metric_name = model.output_names[i] + '_' + metric_name
        if metric_name not in model.metrics_names:
          model.metrics_names.append(metric_name)

      with backend.name_scope(metric_name):
        metric_result = metric_fn(outputs[i], targets[i])
        metric_names.append(metric_name)
        metric_results.append(backend.mean(metric_result))

  return metric_results
开发者ID:kimr843,项目名称:tensorflow,代码行数:37,代码来源:training_eager.py

示例4: build

 def build(self, input_shape):
   with K.name_scope(self.forward_layer.name):
     self.forward_layer.build(input_shape)
   with K.name_scope(self.backward_layer.name):
     self.backward_layer.build(input_shape)
   self.built = True
开发者ID:keithc61,项目名称:tensorflow,代码行数:6,代码来源:wrappers.py

示例5: _eager_loss_fn

def _eager_loss_fn(outputs, targets, loss_fn, output_name):
  with backend.name_scope(output_name + '_loss'):
    loss = loss_fn(targets, outputs)
  return loss
开发者ID:kimr843,项目名称:tensorflow,代码行数:4,代码来源:training_eager.py

示例6: _model_loss

def _model_loss(model, inputs, targets, sample_weights=None, training=False):
  """Calculates the loss for a given model.

  Arguments:
      model: The model on which metrics are being calculated.
      inputs: List of input arrays.
      targets: List of target arrays.
      sample_weights: Optional list of sample weight arrays.
      training: Whether the model should be run in inference or training mode.

  Returns:
     Returns the model output, total loss and loss value calculated using the
     specified loss function. The total loss includes regularization losses and
     applies masking and sample weighting to the loss value.
  """
  total_loss = 0
  if len(inputs) == 1:
    if model._expects_training_arg:
      outs = model.call(inputs[0], training=training)
    else:
      outs = model.call(inputs[0])
  else:
    if model._expects_training_arg:
      outs = model.call(inputs, training=training)
    else:
      outs = model.call(inputs)
  if not isinstance(outs, list):
    outs = [outs]

  if not isinstance(targets, list):
    targets = [targets]

  loss_metrics = []
  with backend.name_scope('loss'):
    for i, loss_fn in enumerate(model.loss_functions):
      if sample_weights:
        weights = sample_weights[i]
      else:
        weights = None

      # TODO(fchollet): support masking; in practice `_keras_mask` is never
      # set in this context currently.
      mask = outs[i]._keras_mask

      weighted_masked_fn = training_utils.weighted_masked_objective(loss_fn)
      with backend.name_scope(model.output_names[i] + '_loss'):
        output_loss = weighted_masked_fn(
            targets[i], outs[i], weights, mask=mask)
      # If the number of outputs is 1 then we don't append the loss metric
      # associated with each model output. When there are multiple outputs
      # associated with a model, each output's loss is calculated and returned
      # as part of the loss_metrics.
      if len(model.outputs) > 1:
        loss_metrics.append(backend.mean(output_loss))

      loss_weight = model.loss_weights_list[i]
      if total_loss is None:
        total_loss = loss_weight * output_loss
      else:
        total_loss += loss_weight * output_loss

    total_loss = backend.mean(total_loss)
    # Add regularization losses
    custom_losses = []
    for layer in model.layers:
      if layer.losses:
        custom_losses += layer.losses

    if custom_losses:
      total_loss += sum(custom_losses)

  return outs, total_loss, loss_metrics
开发者ID:kimr843,项目名称:tensorflow,代码行数:72,代码来源:training_eager.py

示例7: _reduction_a_cell

def _reduction_a_cell(ip, p, filters, block_id=None):
  """Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).

  Arguments:
      ip: Input tensor `x`
      p: Input tensor `p`
      filters: Number of output filters
      block_id: String block_id

  Returns:
      A Keras tensor
  """
  channel_dim = 1 if K.image_data_format() == 'channels_first' else -1

  with K.name_scope('reduction_A_block_%s' % block_id):
    p = _adjust_block(p, ip, filters, block_id)

    h = Activation('relu')(ip)
    h = Conv2D(
        filters, (1, 1),
        strides=(1, 1),
        padding='same',
        name='reduction_conv_1_%s' % block_id,
        use_bias=False,
        kernel_initializer='he_normal')(
            h)
    h = BatchNormalization(
        axis=channel_dim,
        momentum=0.9997,
        epsilon=1e-3,
        name='reduction_bn_1_%s' % block_id)(
            h)

    with K.name_scope('block_1'):
      x1_1 = _separable_conv_block(
          h,
          filters, (5, 5),
          strides=(2, 2),
          block_id='reduction_left1_%s' % block_id)
      x1_2 = _separable_conv_block(
          p,
          filters, (7, 7),
          strides=(2, 2),
          block_id='reduction_1_%s' % block_id)
      x1 = add([x1_1, x1_2], name='reduction_add_1_%s' % block_id)

    with K.name_scope('block_2'):
      x2_1 = MaxPooling2D(
          (3, 3),
          strides=(2, 2),
          padding='same',
          name='reduction_left2_%s' % block_id)(
              h)
      x2_2 = _separable_conv_block(
          p,
          filters, (7, 7),
          strides=(2, 2),
          block_id='reduction_right2_%s' % block_id)
      x2 = add([x2_1, x2_2], name='reduction_add_2_%s' % block_id)

    with K.name_scope('block_3'):
      x3_1 = AveragePooling2D(
          (3, 3),
          strides=(2, 2),
          padding='same',
          name='reduction_left3_%s' % block_id)(
              h)
      x3_2 = _separable_conv_block(
          p,
          filters, (5, 5),
          strides=(2, 2),
          block_id='reduction_right3_%s' % block_id)
      x3 = add([x3_1, x3_2], name='reduction_add3_%s' % block_id)

    with K.name_scope('block_4'):
      x4 = AveragePooling2D(
          (3, 3),
          strides=(1, 1),
          padding='same',
          name='reduction_left4_%s' % block_id)(
              x1)
      x4 = add([x2, x4])

    with K.name_scope('block_5'):
      x5_1 = _separable_conv_block(
          x1, filters, (3, 3), block_id='reduction_left4_%s' % block_id)
      x5_2 = MaxPooling2D(
          (3, 3),
          strides=(2, 2),
          padding='same',
          name='reduction_right5_%s' % block_id)(
              h)
      x5 = add([x5_1, x5_2], name='reduction_add4_%s' % block_id)

    x = concatenate(
        [x2, x3, x4, x5],
        axis=channel_dim,
        name='reduction_concat_%s' % block_id)
    return x, ip
开发者ID:ChengYuXiang,项目名称:tensorflow,代码行数:99,代码来源:nasnet.py

示例8: _adjust_block

def _adjust_block(p, ip, filters, block_id=None):
  """Adjusts the input `previous path` to match the shape of the `input`.

  Used in situations where the output number of filters needs to be changed.

  Arguments:
      p: Input tensor which needs to be modified
      ip: Input tensor whose shape needs to be matched
      filters: Number of output filters to be matched
      block_id: String block_id

  Returns:
      Adjusted Keras tensor
  """
  channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
  img_dim = 2 if K.image_data_format() == 'channels_first' else -2

  ip_shape = K.int_shape(ip)

  if p is not None:
    p_shape = K.int_shape(p)

  with K.name_scope('adjust_block'):
    if p is None:
      p = ip

    elif p_shape[img_dim] != ip_shape[img_dim]:
      with K.name_scope('adjust_reduction_block_%s' % block_id):
        p = Activation('relu', name='adjust_relu_1_%s' % block_id)(p)

        p1 = AveragePooling2D(
            (1, 1),
            strides=(2, 2),
            padding='valid',
            name='adjust_avg_pool_1_%s' % block_id)(
                p)
        p1 = Conv2D(
            filters // 2, (1, 1),
            padding='same',
            use_bias=False,
            name='adjust_conv_1_%s' % block_id,
            kernel_initializer='he_normal')(
                p1)

        p2 = ZeroPadding2D(padding=((0, 1), (0, 1)))(p)
        p2 = Cropping2D(cropping=((1, 0), (1, 0)))(p2)
        p2 = AveragePooling2D(
            (1, 1),
            strides=(2, 2),
            padding='valid',
            name='adjust_avg_pool_2_%s' % block_id)(
                p2)
        p2 = Conv2D(
            filters // 2, (1, 1),
            padding='same',
            use_bias=False,
            name='adjust_conv_2_%s' % block_id,
            kernel_initializer='he_normal')(
                p2)

        p = concatenate([p1, p2], axis=channel_dim)
        p = BatchNormalization(
            axis=channel_dim,
            momentum=0.9997,
            epsilon=1e-3,
            name='adjust_bn_%s' % block_id)(
                p)

    elif p_shape[channel_dim] != filters:
      with K.name_scope('adjust_projection_block_%s' % block_id):
        p = Activation('relu')(p)
        p = Conv2D(
            filters, (1, 1),
            strides=(1, 1),
            padding='same',
            name='adjust_conv_projection_%s' % block_id,
            use_bias=False,
            kernel_initializer='he_normal')(
                p)
        p = BatchNormalization(
            axis=channel_dim,
            momentum=0.9997,
            epsilon=1e-3,
            name='adjust_bn_%s' % block_id)(
                p)
  return p
开发者ID:ChengYuXiang,项目名称:tensorflow,代码行数:86,代码来源:nasnet.py

示例9: _model_loss

def _model_loss(model, inputs, targets):
  """Calculates the loss for a given model.

  Arguments:
     model: The model on which metrics are being calculated.
     inputs: The inputs of the given model. This is typically the mini batch of
              data that is fed to the model.
     targets: The predictions or targets of the given model.

  Returns:
     Returns the model output, total loss and loss value calculated using the
     specified loss function. The total loss includes regularization losses and
     applies masking and sample weighting to the loss value.
  """
  total_loss = 0
  if len(inputs) == 1:
    outs = model.call(inputs[0])
  else:
    outs = model.call(inputs)
  if not isinstance(outs, list):
    outs = [outs]

  if not isinstance(targets, list):
    targets = [targets]

  loss_metrics = []
  with K.name_scope('loss'):
    for i, loss_fn in enumerate(model.loss_functions):
      # compute the loss
      output_loss = _eager_loss_fn(outs[i], targets[i], loss_fn,
                                   model.output_names[i])
      loss_metrics.append(K.mean(output_loss))

      mask = outs[i]._keras_mask
      # adapted from weighted_loss_fn
      if mask is not None:
        # mask should have the same shape as output_loss
        output_loss *= mask
        #  the loss per batch should be proportional
        #  to the number of unmasked samples.
        output_loss /= K.mean(mask)

      # adapted from weighted_loss_fn
      # apply sample weighting
      if model.sample_weights:
        # reduce score_array to same ndim as weight array
        ndim = K.ndim(output_loss)
        weight_ndim = K.ndim(model.sample_weights)
        output_loss = K.mean(output_loss, axis=list(range(weight_ndim, ndim)))
        output_loss *= model.sample_weights
        output_loss /= K.mean(K.cast(K.not_equal(model.sample_weights, 0),
                                     K.floatx()))
        output_loss = K.mean(output_loss)

      loss_weight = model.loss_weights_list[i]
      if total_loss is None:
        total_loss = loss_weight * output_loss
      else:
        total_loss += loss_weight * output_loss

    total_loss = K.mean(total_loss)
    # Add regularization losses
    custom_losses = []
    for layer in model.layers:
      if layer.losses:
        custom_losses += layer.losses

    if custom_losses:
      total_loss += sum(custom_losses)

  return outs, total_loss, loss_metrics
开发者ID:dananjayamahesh,项目名称:tensorflow,代码行数:71,代码来源:training_eager.py

示例10: _model_loss

def _model_loss(model, inputs, targets, training=False):
  """Calculates the loss for a given model.

  Arguments:
     model: The model on which metrics are being calculated.
     inputs: The inputs of the given model. This is typically the mini batch of
              data that is fed to the model.
     targets: The predictions or targets of the given model.
     training: Whether the model should be run in inference or training mode.

  Returns:
     Returns the model output, total loss and loss value calculated using the
     specified loss function. The total loss includes regularization losses and
     applies masking and sample weighting to the loss value.
  """
  total_loss = 0
  if len(inputs) == 1:
    if model._expects_training_arg:
      outs = model.call(inputs[0], training=training)
    else:
      outs = model.call(inputs[0])
  else:
    if model._expects_training_arg:
      outs = model.call(inputs, training=training)
    else:
      outs = model.call(inputs)
  if not isinstance(outs, list):
    outs = [outs]

  if not isinstance(targets, list):
    targets = [targets]

  loss_metrics = []
  with K.name_scope('loss'):
    for i, loss_fn in enumerate(model.loss_functions):
      # compute the loss
      output_loss = _eager_loss_fn(outs[i], targets[i], loss_fn,
                                   model.output_names[i])
      loss_metrics.append(K.mean(output_loss))

      # TODO(fchollet): support masking; in practice `_keras_mask` is never
      # set in this context currently.
      mask = outs[i]._keras_mask
      # adapted from weighted_loss_fn
      if mask is not None:
        # mask should have the same shape as output_loss
        output_loss *= mask
        #  the loss per batch should be proportional
        #  to the number of unmasked samples.
        output_loss /= K.mean(mask)

      # TODO(fchollet): support sample weighting

      loss_weight = model.loss_weights_list[i]
      if total_loss is None:
        total_loss = loss_weight * output_loss
      else:
        total_loss += loss_weight * output_loss

    total_loss = K.mean(total_loss)
    # Add regularization losses
    custom_losses = []
    for layer in model.layers:
      if layer.losses:
        custom_losses += layer.losses

    if custom_losses:
      total_loss += sum(custom_losses)

  return outs, total_loss, loss_metrics
开发者ID:neuroradiology,项目名称:tensorflow,代码行数:70,代码来源:training_eager.py


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