Python graph_util.tensor_shape_from_node_def_name方法代碼示例

本文整理匯總了Python中tensorflow.python.framework.graph_util.tensor_shape_from_node_def_name方法的典型用法代碼示例。如果您正苦於以下問題：Python graph_util.tensor_shape_from_node_def_name方法的具體用法？Python graph_util.tensor_shape_from_node_def_name怎麽用？Python graph_util.tensor_shape_from_node_def_name使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.framework.graph_util的用法示例。

在下文中一共展示了graph_util.tensor_shape_from_node_def_name方法的13個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: _pool_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _pool_flops(graph, node):
  """Common code which compute flops for pooling operations."""
  # compute flops for average and max pooling
  _verify_conv_data_format(node)
  #
  # Pooling declaration:
  #   Inputs:
  #     - value
  #   Outputs:
  #     - output
  #   Attributes:
  #     - ksize
  #     - strides
  #     - padding
  #     - data_format
  #
  # Pooling implenetation:
  out_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  out_shape.assert_is_fully_defined()
  kernel_shape = list(node.attr["ksize"].list.i)
  kernel_area = _list_product(kernel_shape)
  return ops.OpStats("flops", kernel_area * out_shape.num_elements())

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:24，代碼來源:flops_registry.py

示例2: _conv_2d_backprop_filter_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _conv_2d_backprop_filter_flops(graph, node):
  """Compute flops for Conv2DBackpropFilter operation."""
  # Formula same as for Conv2DBackpropInput:
  #  batch_size * image_x_dim * image_y_dim * kernel_x_dim * kernel_y_dim
  #  * input_depth * output_depth * 2 / (image_x_stride * image_x_stride)
  #
  _verify_conv_data_format(node)
  # image_shape = [batch_size, image_y_dim, image_x_dim, input_depth]
  image_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  image_shape.assert_is_fully_defined()
  # kernel_shape = [kernel_y_dim, kernel_x_dim, input_depth, output_depth]
  kernel_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  kernel_shape.assert_is_fully_defined()
  # strides
  strides_shape = list(node.attr["strides"].list.i)
  strides_product = strides_shape[1] * strides_shape[2]
  return ops.OpStats("flops",
                     (2 * image_shape.num_elements()
                      * kernel_shape.num_elements()
                      / (image_shape[-1].value * strides_product)))

################################################################################
# Other ops
################################################################################

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:26，代碼來源:flops_registry.py

示例3: _calc_conv_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _calc_conv_flops(graph, node):
  """Calculates the compute resources needed for Conv2D."""
  input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  input_shape.assert_is_fully_defined()
  filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
                                                            node.input[1])
  filter_shape.assert_is_fully_defined()
  output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  output_shape.assert_is_fully_defined()
  filter_height = int(filter_shape[0])
  filter_width = int(filter_shape[1])
  filter_in_depth = int(filter_shape[2])
  output_count = np.prod(output_shape.as_list())
  return ops.OpStats("flops", (output_count * filter_in_depth * filter_height *
                               filter_width * 2))

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:17，代碼來源:nn_ops.py

示例4: _calc_depthwise_conv_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _calc_depthwise_conv_flops(graph, node):
  """Calculates the compute resources needed for DepthwiseConv2dNative."""
  input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  input_shape.assert_is_fully_defined()
  filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
                                                            node.input[1])
  filter_shape.assert_is_fully_defined()
  output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  output_shape.assert_is_fully_defined()
  filter_height = int(filter_shape[0])
  filter_width = int(filter_shape[1])
  output_count = np.prod(output_shape.as_list())
  return ops.OpStats("flops", (output_count * filter_height * filter_width * 2))

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:15，代碼來源:nn_ops.py

示例5: _calc_bias_add_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _calc_bias_add_flops(graph, node):
  """Calculates the computing needed for BiasAdd."""
  input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  input_shape.assert_is_fully_defined()
  input_count = np.prod(input_shape.as_list())
  return ops.OpStats("flops", input_count)

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:8，代碼來源:nn_ops.py

示例6: _calc_dilation2d_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _calc_dilation2d_flops(graph, node):
  """Calculates the compute resources needed for Dilation2D."""
  input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  input_shape.assert_is_fully_defined()
  filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
                                                            node.input[1])
  filter_shape.assert_is_fully_defined()
  output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  output_shape.assert_is_fully_defined()
  filter_height = int(filter_shape[0])
  filter_width = int(filter_shape[1])
  output_count = np.prod(output_shape.as_list())
  return ops.OpStats("flops", (output_count * filter_height * filter_width * 2))

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:15，代碼來源:nn_ops.py

示例7: _calc_mat_mul_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _calc_mat_mul_flops(graph, node):
  """Calculates the compute resources needed for MatMul."""
  transpose_a = node.attr["transpose_a"].b
  a_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  a_shape.assert_is_fully_defined()
  if transpose_a:
    k = int(a_shape[0])
  else:
    k = int(a_shape[1])
  output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  output_shape.assert_is_fully_defined()
  output_count = np.prod(output_shape.as_list())
  return ops.OpStats("flops", (k * output_count * 2))

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:15，代碼來源:math_ops.py

示例8: _l2_loss_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _l2_loss_flops(graph, node):
  """Compute flops for L2Loss operation."""
  in_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  in_shape.assert_is_fully_defined()
  # Tensorflow uses inefficient implementation, with (3*N-1) flops:
  # Optimal implementation is 2*N flops
  return ops.OpStats("flops", in_shape.num_elements() * 3 - 1)

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:9，代碼來源:flops_registry.py

示例9: _binary_per_element_op_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _binary_per_element_op_flops(graph, node, ops_per_element=1):
  """Common code which compute flops for binary operations."""
  out_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  out_shape.assert_is_fully_defined()
  return ops.OpStats("flops", out_shape.num_elements() * ops_per_element)

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:7，代碼來源:flops_registry.py

示例10: _reduction_op_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _reduction_op_flops(graph, node, reduce_flops=1, finalize_flops=0):
  """Common code which compute flops for reduction operations."""
  in_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  in_shape.assert_is_fully_defined()
  out_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  out_shape.assert_is_fully_defined()
  num_flops = (in_shape.num_elements() * reduce_flops
               + out_shape.num_elements() * (finalize_flops - reduce_flops))
  return ops.OpStats("flops", num_flops)

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:11，代碼來源:flops_registry.py

示例11: _avg_pool_grad_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _avg_pool_grad_flops(graph, node):
  """Compute flops for AvgPoolGrad operation."""
  _verify_conv_data_format(node)
  # Pooling gradient implementation:
  out_backprop_shape = graph_util.tensor_shape_from_node_def_name(graph,
                                                                  node.input[1])
  out_backprop_shape.assert_is_fully_defined()
  kernel_shape = list(node.attr["ksize"].list.i)
  kernel_area = _list_product(kernel_shape)
  # TensorFlow multiply each element of pooling window by coefficient,
  # then sum up all of them, thus we have 2 flops per element:
  # More optimal implementation - if division is done after.
  return ops.OpStats("flops",
                     kernel_area * out_backprop_shape.num_elements() * 2)

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:16，代碼來源:flops_registry.py

示例12: _conv_2d_backprop_input_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _conv_2d_backprop_input_flops(graph, node):
  """Compute flops for Conv2DBackpropInput operation."""
  # Formula:
  #  batch_size * image_x_dim * image_y_dim * kernel_x_dim * kernel_y_dim
  #  * input_depth * output_depth * 2 / (image_x_stride * image_x_stride)
  #
  # Where:
  # image_x_dim, image_y_dim and input_depth --- size of input to source (no
  #   backprop) convolution, in other words they are sizes of backprop output.
  # output_depth --- number of filters in the original convolution, thus
  #   depth of backprop input.
  # kernel_x_dim and kernel_y_dim --- sizes of filter in spatial dimension
  # image_x_stride and image_x_stride --- strides of the convolution
  #
  _verify_conv_data_format(node)
  # out_shape = [batch_size, image_y_dim, image_x_dim, input_depth]
  out_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
  out_shape.assert_is_fully_defined()
  # kernel_shape = [kernel_y_dim, kernel_x_dim, input_depth, output_depth]
  kernel_shape = graph_util.tensor_shape_from_node_def_name(graph,
                                                            node.input[1])
  kernel_shape.assert_is_fully_defined()
  # strides
  strides_shape = list(node.attr["strides"].list.i)
  strides_product = strides_shape[1] * strides_shape[2]
  return ops.OpStats("flops",
                     (2 * out_shape.num_elements()
                      * kernel_shape.num_elements()
                      / (out_shape[-1].value * strides_product)))

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:31，代碼來源:flops_registry.py

示例13: _add_n_flops

# 需要導入模塊: from tensorflow.python.framework import graph_util [as 別名]
# 或者: from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name [as 別名]
def _add_n_flops(graph, node):
  """Compute flops for AddN operation."""
  if not node.input:
    return _zero_flops(graph, node)
  in_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
  in_shape.assert_is_fully_defined()
  return ops.OpStats("flops", in_shape.num_elements() * (len(node.input) - 1))

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:9，代碼來源:flops_registry.py

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