本文整理汇总了Python中tensorflow.python.client.graph_util.tensor_shape_from_node_def_name函数的典型用法代码示例。如果您正苦于以下问题：Python tensor_shape_from_node_def_name函数的具体用法？Python tensor_shape_from_node_def_name怎么用？Python tensor_shape_from_node_def_name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tensor_shape_from_node_def_name函数的7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: _calc_depthwise_conv_flops
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))示例2: _calc_mat_mul_flops
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))示例3: _calc_conv_weight_params
def _calc_conv_weight_params(graph, node):
"""Calculates the on-disk size of the weights 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])
filter_out_depth = int(filter_shape[3])
return ops.OpStats("weight_parameters", (filter_height * filter_width * filter_in_depth * filter_out_depth))示例4: _calc_mat_mul_weight_parameters
def _calc_mat_mul_weight_parameters(graph, node):
"""Calculates the on-disk size of the weights for MatMul."""
# We assume here that the weights are always in the second input to the op,
# which is generally true by convention for fully-connected layers, but not
# enforced or checked.
weights_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[1])
weights_shape.assert_is_fully_defined()
return ops.OpStats("weight_parameters", (int(weights_shape[1]) * int(weights_shape[0])))示例5: _calc_dilation2d_weight_params
def _calc_dilation2d_weight_params(graph, node):
"""Calculates the on-disk size of the weights for Dilation2D."""
filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
node.input[1])
filter_shape.assert_is_fully_defined()
filter_height = int(filter_shape[0])
filter_width = int(filter_shape[1])
filter_depth = int(filter_shape[2])
return ops.OpStats("weight_parameters",
(filter_height * filter_width * filter_depth))示例6: _calc_bias_add_weight_params
def _calc_bias_add_weight_params(graph, node):
"""Calculates the on-disk weight parameters for BiasAdd."""
bias_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[1])
bias_shape.assert_is_fully_defined()
bias_count = np.prod(bias_shape.as_list())
return ops.OpStats("weight_parameters", bias_count)示例7: _calc_bias_add_flops
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)