Question

我尝试使用以下代码恢复模型：

new_saver = tf.train.import_meta_graph(model_path+'.meta')
new_saver.restore(sess, model_path)
g=tf.get_default_graph()

对于原始图表中的每个权重或偏差，我做了g.get_tensrr_by_name()。但是，当我尝试在deconv2d图层上执行此操作时，如下所示：

def deconv2d(self,inputs, num_outputs, kernel_shape, g,scope,strides=[1, 1]):
  with tf.variable_scope(scope) as scope:
    weights_initializer = g.get_tensor_by_name("prsr/conditioning/deconv/Conv2d_transpose/weights:0")
    biases_initializer = g.get_tensor_by_name("prsr/conditioning/deconv/Conv2d_transpose/biases:0")
    return tf.contrib.layers.convolution2d_transpose(inputs=inputs, num_outputs=num_outputs,kernel_size=kernel_shape,stride=strides, \
      padding='SAME', weights_initializer=weights_initializer,biases_initializer=biases_initializer)

失败并显示以下错误：

  File "restore.py", line 41, in deconv2d
    padding='SAME', weights_initializer=weights_initializer,biases_initializer=biases_initializer)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args
    return func(*args, **current_args)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1126, in convolution2d_transpose
    outputs = layer.apply(inputs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 323, in apply
    return self.__call__(inputs, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 289, in __call__
    self.build(input_shapes[0])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/convolutional.py", line 1043, in build
    dtype=self.dtype)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1033, in get_variable
    use_resource=use_resource, custom_getter=custom_getter)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 932, in get_variable
    use_resource=use_resource, custom_getter=custom_getter)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 349, in get_variable
    validate_shape=validate_shape, use_resource=use_resource)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 278, in variable_getter
    variable_getter=functools.partial(getter, **kwargs))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 228, in _add_variable
    trainable=trainable and self.trainable)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1327, in layer_variable_getter
    return _model_variable_getter(getter, *args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1316, in _model_variable_getter
    custom_getter=getter, use_resource=use_resource)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args
    return func(*args, **current_args)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/variables.py", line 259, in model_variable
    use_resource=use_resource)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args
    return func(*args, **current_args)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/variables.py", line 214, in variable
    use_resource=use_resource)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 341, in _true_getter
    use_resource=use_resource)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 638, in _get_single_variable
    raise ValueError("If initializer is a constant, do not specify shape.")
ValueError: If initializer is a constant, do not specify shape.

我不知道这引用了哪个shape，我不认为weights_initializer和biases_initializer是常数，它们是张量，对吧？顺便说一下，我非常确定原始图表中存在这两个张量prsr/conditioning/deconv/Conv2d_transpose/weights和prsr/conditioning/deconv/Conv2d_transpose/biasess，因为我使用print_tensors_in_checkpoint_file进行了检查，我实际上可以看到值。
那么如何恢复应用此tf.contrib.layers.convolution2d_transpose()图层的模型？我在stackoverflow和github上搜索了很多，但没有任何效果。任何帮助，将不胜感激。

Answer 1

weights_initializer和bias_initializer不是您认为的那样。你可能会认为这两个张量是去卷积中使用的权重的初始值，对吧？但是，初始化器参数是一个函数而不是一个应该看起来像这样的张量：

def my_initializer(shape, dtype=tf.float32, partition_info=None):
    # do some computation to build up a tensor of the given shape
    return that_tensor

然后您可以像这样使用此初始值设定项：

tf.contrib.layers.convolution2d_transpose(inputs=inputs, ..., weights_initializer=my_initializer)

因此，作为解决问题的方法，我认为以下内容应该有效：

def weights_initializer(shape, dtype=tf.float32, partition_info=None):
    weights = tf.get_default_graph().get_tensor_by_name("prsr/conditioning/deconv/Conv2d_transpose/weights:0")
    return weights

但是，在我看来，这感觉有些笨拙。为什么要加载图形，然后在新操作中使用预先训练的权重？在设置初始模型之前，为什么这些权重与此操作无关？

PS：在处理变量时，您可能会发现tf.get_variable派上用场。如果您使用tf.get_variable创建变量，则可以稍后再次使用tf.get_variable检索这些变量，而无需调用繁琐的get_tensor_by_name。查看this了解详情。

无法使用tf.contrib.layers.convolution2d_transpose应用

1 个答案: