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猪仔很忙 发表于 2020-8-6 17:09:01

tf.add()的一个奇怪报错

本帖最后由猪仔很忙于 2020-8-7 18:12 编辑

执行以下代码，为什么会输出两次perturb？而且第二次的类型为什么变成None了？
import sys
sys.path.append('../')
from keras import Input
from keras.layers import Dense
import keras.backend as K
from keras.layers import Lambda
from keras.layers import LSTM
import tensorflow as tf

class RNN_SEPARATE_2(object):

def __init__(self,
            time_step=64,
            fearure_dim=2,
            hidden_size=256, # 128
            dropout_rate=0.2,
            name='rnn'):
   self.name = name
   self.time_step = time_step
   self.fearure_dim = fearure_dim
   self.hidden_size = hidden_size
   self.dropout_rate = dropout_rate

def __call__(self, inputs):
   x = inputs
   x_1 = Lambda(lambda x: x[:,:,0])(x)
   x_1 = Lambda(lambda x: K.expand_dims(x, axis=-1))(x_1)
   x_2 = Lambda(lambda x: x[:,:,1:])(x)

   h_2 = LSTM(self.hidden_size, return_sequences=True)(x_2) # batch, seq2, dim
   h_2_first = Lambda(lambda x: x[:,1:,])(h_2)
   h_2_last = Lambda(lambda x: x[:,-1,:])(h_2)

   h_1 = LSTM(self.hidden_size, return_sequences=True)(x_1) # batch, seq1, dim
   h_1_first = Lambda(lambda x:x[:1:,:])(h_1)
   h_1_last = Lambda(lambda x: x[:,-1,:])(h_1) # batch, dim

   h = Lambda(lambda x: K.concatenate(, x], axis=-1))() # batch, seq, dim
   h = LSTM(self.hidden_size, return_sequences=True)(h) # batch, seq, dim
   h = LSTM(self.hidden_size, return_sequences=True)(h) # batch, seq, dim
   h_first = Lambda(lambda x: x[:,:-1,:])(h) # batch, seq-1, dim
   h_last = Lambda(lambda x: x[:,-1,:])(h) # batch, dim

   y = Lambda(lambda x:x+x+x)()
   return y

def get_adversary(args):
y_true = args
y_pred = args
v_final = args
pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
loss = -K.mean(0.75 * K.pow(1. - pt_1, 0) * K.log(pt_1)) - K.mean((1 - 0.75) * K.pow(pt_0, 0) * K.log(1. - pt_0))
perturb = tf.gradients(loss, )
print(perturb)
v_final_adv = tf.add(v_final, perturb)
return v_final_adv

time_step = 10
feature_dim = 7
trace_input = Input(shape=(time_step, feature_dim))
label_input = Input(shape=(1,))
v_final = RNN_SEPARATE_2(time_step, feature_dim)(trace_input)
pred = Dense(1, activation='sigmoid', name='pred')(v_final)
v_final_adv = Lambda(get_adversary)()

输出结果：
Tensor("lambda_12/gradients/pred/MatMul_grad/MatMul:0", shape=(?, 256), dtype=float32)
None

报错信息：
Traceback (most recent call last):
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 527, in _apply_op_helper
preferred_dtype=default_dtype)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\ops.py", line 1224, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\constant_op.py", line 305, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\constant_op.py", line 246, in constant
allow_broadcast=True)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\constant_op.py", line 284, in _constant_impl
allow_broadcast=allow_broadcast))
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 454, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 541, in _apply_op_helper
values, as_ref=input_arg.is_ref).dtype.name
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\ops.py", line 1224, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\constant_op.py", line 305, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\constant_op.py", line 246, in constant
allow_broadcast=True)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\constant_op.py", line 284, in _constant_impl
allow_broadcast=allow_broadcast))
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 454, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "D:\JetBrains\PyCharm 2020.1.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars)# execute the script
File "D:\JetBrains\PyCharm 2020.1.2\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/PycharmProjects/turningpoint-master/main.py", line 67, in <module>
modelName=modelName)
File "D:\PycharmProjects\turningpoint-master\baseline_main_rnn.py", line 37, in train_rnn_turning_point
clf = rnn_turning_point.build_train(datas, machineID, modelName)
File "D:\PycharmProjects\turningpoint-master\model\rnn_turning_point.py", line 56, in build_train
v_final_adv = Lambda(get_adversary)()
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\engine\base_layer.py", line 474, in __call__
output_shape = self.compute_output_shape(input_shape)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\layers\core.py", line 649, in compute_output_shape
x = self.call(xs)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\layers\core.py", line 687, in call
return self.function(inputs, **arguments)
File "D:\PycharmProjects\turningpoint-master\model\rnn_turning_point.py", line 37, in get_adversary
v_final_adv = tf.add(v_final, perturb)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 386, in add
"Add", x=x, y=y, name=name)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 545, in _apply_op_helper
(input_name, err))
ValueError: Tried to convert 'y' to a tensor and failed. Error: None values not supported.

陈尚涵 发表于 2020-8-6 17:12:33

无能为力{:5_94:}

Twilight6 发表于 2020-8-6 17:41:27

代码都不完整...我们测试不了

猪仔很忙 发表于 2020-8-6 17:55:56

Twilight6 发表于 2020-8-6 17:41
代码都不完整...我们测试不了

抱歉..以下是代码：
import sys
sys.path.append('../')
from keras import Input
from keras.layers import Dense
import keras.backend as K
from keras.layers import Lambda
from keras.layers import LSTM
import tensorflow as tf

class RNN_SEPARATE_2(object):

def __init__(self,
            time_step=64,
            fearure_dim=2,
            hidden_size=256, # 128
            dropout_rate=0.2,
            name='rnn'):
   self.name = name
   self.time_step = time_step
   self.fearure_dim = fearure_dim
   self.hidden_size = hidden_size
   self.dropout_rate = dropout_rate

def __call__(self, inputs):
   x = inputs
   x_1 = Lambda(lambda x: x[:,:,0])(x)
   x_1 = Lambda(lambda x: K.expand_dims(x, axis=-1))(x_1)
   x_2 = Lambda(lambda x: x[:,:,1:])(x)

   h_2 = LSTM(self.hidden_size, return_sequences=True)(x_2) # batch, seq2, dim
   h_2_first = Lambda(lambda x: x[:,1:,])(h_2)
   h_2_last = Lambda(lambda x: x[:,-1,:])(h_2)

   h_1 = LSTM(self.hidden_size, return_sequences=True)(x_1) # batch, seq1, dim
   h_1_first = Lambda(lambda x:x[:1:,:])(h_1)
   h_1_last = Lambda(lambda x: x[:,-1,:])(h_1) # batch, dim

   h = Lambda(lambda x: K.concatenate(, x], axis=-1))() # batch, seq, dim
   h = LSTM(self.hidden_size, return_sequences=True)(h) # batch, seq, dim
   h = LSTM(self.hidden_size, return_sequences=True)(h) # batch, seq, dim
   h_first = Lambda(lambda x: x[:,:-1,:])(h) # batch, seq-1, dim
   h_last = Lambda(lambda x: x[:,-1,:])(h) # batch, dim

   y = Lambda(lambda x:x+x+x)()
   return y

def gradient_operation(args):
y_true = args
y_pred = args
v_final = args
pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
loss = -K.mean(0.75 * K.pow(1. - pt_1, 0) * K.log(pt_1)) - K.mean((1 - 0.75) * K.pow(pt_0, 0) * K.log(1. - pt_0))
perturb = tf.gradients(loss, )
print(perturb)
v_final_adv = tf.add(v_final, perturb)
return v_final_adv

time_step = 10
feature_dim = 7
trace_input = Input(shape=(time_step, feature_dim))
label_input = Input(shape=(1,))
v_final = RNN_SEPARATE_2(time_step, feature_dim)(trace_input)
pred = Dense(1, activation='sigmoid', name='pred')(v_final)
v_final_adv = Lambda(gradient_operation)()

猪仔很忙 发表于 2020-8-6 18:09:08

我用的tensorflow版本是1.14.0，keras是2.2.4

陈尚涵 发表于 2020-8-7 10:20:43

猪仔很忙发表于 2020-8-6 18:09
我用的tensorflow版本是1.14.0，keras是2.2.4

更加无能为力{:10_285:}

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tf.add()的一个奇怪报错