问题描述
pairs = [((0,1,2),0),((3,4,5,6,7,8,9,10,11),((12,),1),((13,14,15,16),((17,18,19,20),2)]
现在，我想生成数据的正例和负例。如果对不是嵌套的元组，则代码会正常工作。 这是代码段：
def data_generation(self,pairs):
"""Generate batches of samples for training"""
batch = np.zeros(shape=(self.batch_size,3),dtype=object)
# Adjust label based on task
if self.classification:
neg_label = 0
else:
neg_label = -1
# This creates a generator
while True:
for idx,(file_id,test_id) in enumerate(random.sample(pairs,self.n_positive)):
batch[idx,:] = (np.asarray(file_id),test_id,1)
# Increment idx by 1
idx += 1
# Add negative examples until reach batch size
while idx < self.batch_size:
# random selection
random_test = random.randrange(self.nr_tests)
# Check to make sure this is not a positive example
if (file_id,random_test) not in self.pairs_set:
# Add to batch and increment index
batch[idx,random_test,neg_label)
idx += 1
np.random.shuffle(batch)
yield {'file': batch[:,0],'test': batch[:,1]},batch[:,2]
批处理矢量的形状为：
print(batch[:,0].shape)
print(batch[:,1].shape)
print(batch[:,2].shape)
输出：
(2000,)
(2000,)
问题在于，每个批次[：，0]都有一个长度可变的嵌套nd.array。
现在data_generation()的输出是：
{'file': array([array([809,386,813,75,248,614,34,332,389]),array([ 52,53,486,489]),...,489])],dtype=object),'test': array([1247,1566,814,142,2336,674],dtype=object)} [1 0 0 ... 0 0 0]
然后，我构建以下Keras模型，该模型训练一组文件的嵌入和一组测试的嵌入：
def build_model(self,embedding_size=50,optimizer='Adam',classification=True):
"""
Build model architecture/framework
:return: model
"""
from keras.layers import Input,Embedding,Dot,Reshape,Dense
from keras.models import Model
# Both inputs are 1-dimensional
revision = Input(name='revision',shape=[1])
test = Input(name='test',shape=[1])
# Embedding the book (shape will be (None,50))
file_embedding = Embedding(name='file_embedding',input_dim=len(self.Data.file_index),output_dim=embedding_size,input_length=self.max_len - 1)(revision)
# Embedding the link (shape will be (None,50))
test_embedding = Embedding(name='test_embedding',input_dim=len(self.Data.test_index),output_dim=embedding_size)(test)
# Merge the layers with a dot product along the second axis (shape will be (None,1))
merged = Dot(name='dot_product',normalize=True,axes=2)([file_embedding,test_embedding])
# Reshape to be a single number (shape will be (None,1))
merged = Reshape(target_shape=[1])(merged)
# If classification,add extra layer and loss function is binary cross entropy
if classification:
merged = Dense(1,activation='sigmoid')(merged)
model = Model(inputs=[revision,test],outputs=merged)
model.compile(optimizer=optimizer,loss='binary_crossentropy',metrics=['accuracy'])
调用model.fit（）之后，我得到以下回溯：
File "/Users/joaolousada/Documents/5ºAno/Master-Thesis/main/Prioritizer/Prioritizer.py",line 170,in crossValidation
verbose=2)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py",line 108,in _method_wrapper
return method(self,*args,**kwargs)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py",line 1063,in fit
steps_per_execution=self._steps_per_execution)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py",line 1117,in __init__
model=model)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py",line 916,in __init__
**kwargs)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py",line 788,in __init__
peek = _process_tensorlike(peek)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py",line 1021,in _process_tensorlike
inputs = nest.map_structure(_convert_numpy_and_scipy,inputs)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/nest.py",line 635,in map_structure
structure[0],[func(*x) for x in entries],File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/nest.py",in <listcomp>
structure[0],File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py",line 1016,in _convert_numpy_and_scipy
return ops.convert_to_tensor(x,dtype=dtype)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py",line 1499,in convert_to_tensor
ret = conversion_func(value,dtype=dtype,name=name,as_ref=as_ref)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/tensor_conversion_registry.py",line 52,in _default_conversion_function
return constant_op.constant(value,dtype,name=name)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py",line 264,in constant
allow_broadcast=True)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py",line 275,in _constant_impl
return _constant_eager_impl(ctx,value,shape,verify_shape)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py",line 300,in _constant_eager_impl
t = convert_to_eager_tensor(value,ctx,dtype)
File "/Users/joaolousada/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py",line 98,in convert_to_eager_tensor
return ops.EagerTensor(value,ctx.device_name,dtype)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).
任何帮助将不胜感激！
解决方法
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