问题描述
我正在使用Python 3.7.7。以及带有功能性API和热切执行的Tensorflow 2.1.0。
我正在尝试使用从U-Net预训练网络提取的编码器来进行custom training：
- 我无需编译即可获得U-Net模型。
- 我已将权重加载到模型中。
- 我从那个模型中提取了编码器和解码器。
然后我要在此摘要中使用编码器：
Model: "encoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None,200,1)] 0
_________________________________________________________________
conv1_1 (Conv2D) (None,64) 1664
_________________________________________________________________
conv1_2 (Conv2D) (None,64) 102464
_________________________________________________________________
pool1 (MaxPooling2D) (None,100,64) 0
_________________________________________________________________
conv2_1 (Conv2D) (None,96) 55392
_________________________________________________________________
conv2_2 (Conv2D) (None,96) 83040
_________________________________________________________________
pool2 (MaxPooling2D) (None,50,96) 0
_________________________________________________________________
conv3_1 (Conv2D) (None,128) 110720
_________________________________________________________________
conv3_2 (Conv2D) (None,128) 147584
_________________________________________________________________
pool3 (MaxPooling2D) (None,25,128) 0
_________________________________________________________________
conv4_1 (Conv2D) (None,256) 295168
_________________________________________________________________
conv4_2 (Conv2D) (None,256) 1048832
_________________________________________________________________
pool4 (MaxPooling2D) (None,12,256) 0
_________________________________________________________________
conv5_1 (Conv2D) (None,512) 1180160
_________________________________________________________________
conv5_2 (Conv2D) (None,512) 2359808
=================================================================
Total params: 5,384,832
Trainable params: 5,832
Non-trainable params: 0
_________________________________________________________________
def train_encoder_unet_custom(model,dataset):
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
for episode in range(num_episodes):
selected = np.random.permutation(no_of_samples)[:num_shot + num_query]
# Create our Support Set.
support_set = np.array(dataset[selected[:num_shot]])
X_train = support_set[:,:]
y_train = support_set[:,1,:]
loss_value,grads = grad(model,X_train,y_train)
optimizer.apply_gradients(zip(grads,model.trainable_variables))
grad函数是：
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
def loss(model,x,y,training):
# training=training is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
y_ = model(x,training=training)
return loss_object(y_true=y,y_pred=y_)
def grad(model,inputs,targets):
with tf.GradientTape() as tape:
loss_value = loss(model,targets,training=False)
return loss_value,tape.gradient(loss_value,model.trainable_variables)
但是当我尝试运行它时，出现错误：
InvalidArgumentError: Shapes of all inputs must match: values[0].shape = [5,512] != values[1].shape = [5,256] [Op:Pack] name: packed
在loss函数中，我检查了y_变量的值。 y_是具有以下形状的6个元素的列表：
(5,512)
(5,256)
(5,128)
(5,96)
(5,64)
(5,1)
有什么想法吗？
如果您需要更多详细信息，请问我。
这是完整的调用堆栈：
<ipython-input-133-22827956a9f6> in train_encoder_unet_custom(model,dataset,feat_type,show)
22 y_valid = query_set[:,:]
23
---> 24 loss_value,y_train)
25
26 optimizer.apply_gradients(zip(grads,model.trainable_variables))
<ipython-input-143-58ff4de686d6> in grad(model,targets)
10 def grad(model,targets):
11 with tf.GradientTape() as tape:
---> 12 loss_value = loss(model,training=False)
13 return loss_value,model.trainable_variables)
<ipython-input-143-58ff4de686d6> in loss(model,training)
6 y_ = model(x,training=training)
7
----> 8 return loss_object(y_true=y,y_pred=y_)
9
10 def grad(model,targets):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py in __call__(self,y_true,y_pred,sample_weight)
147 with K.name_scope(self._name_scope),graph_ctx:
148 ag_call = autograph.tf_convert(self.call,ag_ctx.control_status_ctx())
--> 149 losses = ag_call(y_true,y_pred)
150 return losses_utils.compute_weighted_loss(
151 losses,sample_weight,reduction=self._get_reduction())
/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args,**kwargs)
253 try:
254 with conversion_ctx:
--> 255 return converted_call(f,args,kwargs,options=options)
256 except Exception as e: # pylint:disable=broad-except
257 if hasattr(e,'ag_error_Metadata'):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in converted_call(f,caller_fn_scope,options)
455 if conversion.is_in_whitelist_cache(f,options):
456 logging.log(2,'Whitelisted %s: from cache',f)
--> 457 return _call_unconverted(f,options,False)
458
459 if ag_ctx.control_status_ctx().status == ag_ctx.Status.disABLED:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f,update_cache)
337
338 if kwargs is not None:
--> 339 return f(*args,**kwargs)
340 return f(*args)
341
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py in call(self,y_pred)
251 y_pred,y_true)
252 ag_fn = autograph.tf_convert(self.fn,ag_ctx.control_status_ctx())
--> 253 return ag_fn(y_true,**self._fn_kwargs)
254
255 def get_config(self):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py in wrapper(*args,**kwargs)
199 """Call target,and fall back on dispatchers if there is a TypeError."""
200 try:
--> 201 return target(*args,**kwargs)
202 except (TypeError,ValueError):
203 # Note: convert_to_eager_tensor currently raises a ValueError,not a
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py in sparse_categorical_crossentropy(y_true,from_logits,axis)
1562 Sparse categorical crossentropy loss value.
1563 """
-> 1564 y_pred = ops.convert_to_tensor_v2(y_pred)
1565 y_true = math_ops.cast(y_true,y_pred.dtype)
1566 return K.sparse_categorical_crossentropy(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor_v2(value,dtype,dtype_hint,name)
1380 name=name,1381 preferred_dtype=dtype_hint,-> 1382 as_ref=False)
1383
1384
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value,name,as_ref,preferred_dtype,ctx,accepted_result_types)
1497
1498 if ret is None:
-> 1499 ret = conversion_func(value,dtype=dtype,name=name,as_ref=as_ref)
1500
1501 if ret is NotImplemented:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py in _autopacking_conversion_function(v,as_ref)
1500 elif dtype != inferred_dtype:
1501 v = nest.map_structure(_cast_nested_seqs_to_dtype(dtype),v)
-> 1502 return _autopacking_helper(v,name or "packed")
1503
1504
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py in _autopacking_helper(list_or_tuple,name)
1406 # checking.
1407 if all(isinstance(elem,core.Tensor) for elem in list_or_tuple):
-> 1408 return gen_array_ops.pack(list_or_tuple,name=name)
1409 must_pack = False
1410 converted_elems = []
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py in pack(values,axis,name)
6457 return _result
6458 except _core._NotOkStatusException as e:
-> 6459 _ops.raise_from_not_ok_status(e,name)
6460 except _core._FallbackException:
6461 pass
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e,name)
6841 message = e.message + (" name: " + name if name is not None else "")
6842 # pylint: disable=protected-access
-> 6843 six.raise_from(core._status_to_exception(e.code,message),None)
6844 # pylint: enable=protected-access
6845
/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value,from_value)
解决方法
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