漸變剪輯看起來窒息無

Question

我想添加漸變剪輯到我的圖形。我使用的方法這裏推薦：How to effectively apply gradient clipping in tensor flow?

optimizer = tf.train.GradientDescentOptimizer(learning_rate) 
    if gradient_clipping: 
     gradients = optimizer.compute_gradients(loss) 
     clipped_gradients = [(tf.clip_by_value(grad, -1, 1), var) for grad, var in gradients] 
     opt = optimizer.apply_gradients(clipped_gradients, global_step=global_step) 
    else: 
     opt = optimizer.minimize(loss, global_step=global_step) 

但是，當我打開梯度裁剪，我得到以下堆棧跟蹤：

<ipython-input-19-be0dcc63725e> in <listcomp>(.0) 
    61   if gradient_clipping: 
    62    gradients = optimizer.compute_gradients(loss) 
---> 63    clipped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients] 
    64    opt = optimizer.apply_gradients(clipped_gradients, global_step=global_step) 
    65   else: 

/home/armence/mlsandbox/venv/lib/python3.4/site-packages/tensorflow/python/ops/clip_ops.py in clip_by_value(t, clip_value_min, clip_value_max, name) 
    51 with ops.op_scope([t, clip_value_min, clip_value_max], name, 
    52     "clip_by_value") as name: 
---> 53  t = ops.convert_to_tensor(t, name="t") 
    54 
    55  # Go through list of tensors, for each value in each tensor clip 

/home/armence/mlsandbox/venv/lib/python3.4/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref) 
    619  for base_type, conversion_func in funcs_at_priority: 
    620  if isinstance(value, base_type): 
--> 621   ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 
    622   if ret is NotImplemented: 
    623   continue 

/home/armence/mlsandbox/venv/lib/python3.4/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref) 
    178           as_ref=False): 
    179 _ = as_ref 
--> 180 return constant(v, dtype=dtype, name=name) 
    181 
    182 

/home/armence/mlsandbox/venv/lib/python3.4/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name) 
    161 tensor_value = attr_value_pb2.AttrValue() 
    162 tensor_value.tensor.CopyFrom(
--> 163  tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape)) 
    164 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype) 
    165 const_tensor = g.create_op(

/home/armence/mlsandbox/venv/lib/python3.4/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape) 
    344 else: 
    345  if values is None: 
--> 346  raise ValueError("None values not supported.") 
    347  # if dtype is provided, forces numpy array to be the type 
    348  # provided if possible. 

ValueError: None values not supported. 

我該如何解決這個問題？

Answer 1

所以，這似乎工作一個選項是這樣的：

optimizer = tf.train.GradientDescentOptimizer(learning_rate) 
    if gradient_clipping: 
     gradients = optimizer.compute_gradients(loss) 

     def ClipIfNotNone(grad): 
      if grad is None: 
       return grad 
      return tf.clip_by_value(grad, -1, 1) 
     clipped_gradients = [(ClipIfNotNone(grad), var) for grad, var in gradients] 
     opt = optimizer.apply_gradients(clipped_gradients, global_step=global_step) 
    else: 
     opt = optimizer.minimize(loss, global_step=global_step) 

它看起來像compute_gradients返回，而不是一個零張無當梯度將是一個零和張tf.clip_by_value不支持無值。所以不要將None傳遞給它並保留None值。