Question

在使用Tensorflow急切执行时，我在keras中使用kernel_constraint=maxnorm(3)时遇到问题。这在没有在急切执行之外使用标准Sequential方法时工作正常，但似乎在这里失败并出现错误（这似乎是因为我不知道的乘法步骤*=如果在这种情况下有替代品）。

问题：有没有办法在Eager Tensorflow执行框架中加入最大的$ L ^ 2 $规范功能？以下是更多细节。

以下是我激活tensorflow的方式。

from __future__ import absolute_import, division, print_function
import tensorflow as tf
import tensorflow.contrib.eager as tfe
from keras.datasets import cifar10
tf.enable_eager_execution()

以下代码可以正常使用

使用：

class ObjectDet(tf.keras.Model):
    def __init__(self):
        super(ObjectDet,self).__init__()
        self.layer1= tf.keras.layers.Conv2D(32, (3, 3), input_shape=(32,32,3), padding='same', activation='relu')
        self.layer2=tf.keras.layers.Dropout(0.2)
        self.layer3=tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')
        self.layer4=tf.keras.layers.MaxPooling2D(pool_size=(2,2))
        self.layer5=tf.keras.layers.Flatten()
        self.layer6=tf.keras.layers.Dense(512, activation='relu')
        self.layer7=tf.keras.layers.Dropout(0.1)
        self.layer8=tf.keras.layers.Dense(10, activation='softmax')

    def call(self, input):
        """Run the model."""
        result = self.layer1(input)
        result = self.layer2(result)
        result = self.layer3(result)
        result = self.layer4(result)
        result = self.layer5(result)
        result = self.layer6(result)
        result = self.layer7(result)
        result = self.layer8(result)

       return result



def loss(model, x, y):
  prediction = model(x)
  return cross_entropy(prediction,y)

def grad(model, inputs, targets):
  with tf.GradientTape() as tape:
    loss_value = loss(model, inputs, targets)
  return tape.gradient(loss_value, model.variables)


x, y = iter(train_ds).next()
print("Initial loss: {:.3f}".format(loss(model, x, y)))

# Training loop
for (i, (x, y)) in enumerate(train_ds):
  # Calculate derivatives of the input function with respect to its parameters.
  grads = grad(model, x, y)
  # Apply the gradient to the model

  optimizer.apply_gradients(zip(grads, model.variables),
                            global_step=tf.train.get_or_create_global_step())
  if i % 200 == 0:
    pass
    print("Loss at step {:04d}: {:.3f}".format(i, loss(model, x, y)))

不起作用：

如果我更换

self.layer1= tf.keras.layers.Conv2D(32, (3, 3), input_shape=(32,32,3), padding='same', activation='relu')

与

self.layer1= tf.keras.layers.Conv2D(32, (3, 3), input_shape=(32,32,3), padding='same', activation='relu',kernel_constraint=maxnorm(3))

我收到了错误：

RuntimeErrorTraceback (most recent call last)
<ipython-input-74-629273c4a534> in <module>()
     19 
     20   optimizer.apply_gradients(zip(grads, model.variables),
---> 21                             global_step=tf.train.get_or_create_global_step())
     22   if i % 200 == 0:
     23     pass

/home/dgoldma1/.local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.pyc in apply_gradients(self, grads_and_vars, global_step, name)
    615           scope_name = var.op.name
    616         with ops.name_scope("update_" + scope_name), ops.colocate_with(var):
--> 617           update_ops.append(processor.update_op(self, grad))
    618       if global_step is None:
    619         apply_updates = self._finish(update_ops, name)

/home/dgoldma1/.local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.pyc in update_op(self, optimizer, g)
    166     if self._v.constraint is not None:
    167       with ops.control_dependencies([update_op]):
--> 168         return self._v.assign(self._v.constraint(self._v))
    169     else:
    170       return update_op

/home/dgoldma1/.local/lib/python2.7/site-packages/keras/constraints.pyc in __call__(self, w)
     51         norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True))
     52         desired = K.clip(norms, 0, self.max_value)
---> 53         w *= (desired / (K.epsilon() + norms))
     54         return w
     55 

/home/dgoldma1/.local/lib/python2.7/site-packages/tensorflow/python/ops/resource_variable_ops.pyc in __imul__(self, unused_other)
    931 
    932   def __imul__(self, unused_other):
--> 933     raise RuntimeError("Variable *= value not supported. Use "
    934                        "variable.assign_mul(value) to modify the variable "
    935                        "value and variable = variable * value to get a new "

RuntimeError: Variable *= value not supported. Use variable.assign_mul(value) to modify the variable value and variable = variable * value to get a new Tensor object.

谢谢！

Answer 1

您似乎在代码中发现了一个错误。您可以将其提交给开发团队here。

Eager是一种最新的tensorflow，它对代码产生了深远的影响，所以它缺乏一点点润色。我不会感到惊讶的是，角落案件中的这种错误仍然会发生。

如何在Tensorflow eager conv2D中包含内核约束？

1 个答案: