本文介绍了InvalidArgumentError索引[i,0] = x不在keras的[0,x)中的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我有使用keras 1.2
和tensorflow 1.1
的代码.我已经运行了,但是有错误
I have the code using keras 1.2
and tensorflow 1.1
. I have run it but with error
import numpy as np
import keras
from keras import backend as K
from keras import initializers
from keras.models import Sequential, Model, load_model, save_model
from keras.layers.core import Dense, Lambda, Activation
from keras.layers import Embedding, Input, Dense, Multiply, Reshape, Flatten
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from keras.regularizers import l2
from sklearn.metrics import average_precision_score
from sklearn.metrics import auc
def init_normal(shape, name=None):
return initializers.lecun_uniform(seed=None)
def get_model(num_a, num_b, num_c, dim, regs=[0,0,0]):
a = Input(shape=(1,), dtype='int32', name = 'a')
b = Input(shape=(1,), dtype='int32', name = 'b')
c = Input(shape=(1,), dtype='int32', name = 'c')
Embedding_a = Embedding(input_dim = num_a, output_dim = dim,
embeddings_initializer='uniform', W_regularizer = l2(regs[0]), input_length=1)
Embedding_b = Embedding(input_dim = num_b, output_dim = dim,
embeddings_initializer='uniform', W_regularizer = l2(regs[1]), input_length=1)
Embedding_c = Embedding(input_dim = num_c, output_dim = dim,
embeddings_initializer='uniform', W_regularizer = l2(regs[2]), input_length=1)
a_latent = Flatten()(Embedding_a(a))
b_latent = Flatten()(Embedding_b(b))
c_latent = Flatten()(Embedding_c(c))
predict_vector = Multiply()([a_latent, b_latent, b_latent])
prediction = Dense(1, activation='sigmoid', init='lecun_uniform', name = 'prediction')(predict_vector)
model = Model(input=[a, b, c], output=prediction)
return model
def evaluate_model(model, test_pos, test_neg):
global _model
global _test_pos
global _test_neg
_model = model
_test_pos = test_pos
_test_neg = test_neg
print(_test_neg)
a, b, c, labels = [],[],[],[]
for item in _test_pos:
a.append(item[0])
b.append(item[1])
c.append(item[2])
labels.append(1)
for item in _test_neg:
a.append(item[0])
b.append(item[1])
c.append(item[2])
labels.append(0)
a = np.array(a)
b = np.array(b)
c = np.array(c)
predictions = _model.predict([a, b, c],
batch_size=100, verbose=0)
return average_precision_score(labels, predictions), auc(labels, predictions)
model = get_model(4, 8, 12, 2, [0,0,0])
model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy')
pos_test = [[0, 0, 2], [4, 8, 8], [2, 5, 4], [0, 0, 0]]
neg_test = [[3, 3, 2], [2, 1, 8], [1, 4, 1], [3, 3, 12]]
aupr, auc = evaluate_model(model, pos_test, neg_test)
print(aupr, auc)
但是,它给了我错误:任何解决方法?
However, It give me error:any way to fix it?
InvalidArgumentError (see above for traceback): indices[1,0] = 4 is not in [0, 4)
[[Node: embedding_4/embedding_lookup = Gather[Tindices=DT_INT32, Tparams=DT_FLOAT, _class=["loc:@embedding_4/embeddings"], validate_indices=true, _device="/job:localhost/replica:0/task:0/cpu:0"](embedding_4/embeddings/read, _recv_a_1_0)]]
推荐答案
问题是,您将嵌入input_dim
定义为4、8和12,而应将其分别为5、9、13.因为嵌入中的input_dim
应该是max_index + 1
. Keras文档:
The problem is, you defined embedding input_dim
as 4, 8 and 12 while it should be is 5, 9, 13. Because input_dim
in embedding should be max_index + 1
. It is also clearly mentioned in Keras docs:
如何解决此问题?
将get_model
方法更改为:
model = get_model(5, 9, 13, 2, [0, 0, 0])
或者将数据索引更改为:
Or alternatively change index of data to:
pos_test = [[0, 0, 2], [3, 7, 7], [2, 5, 4], [0, 0, 0]]
neg_test = [[3, 3, 2], [2, 1, 7], [1, 4, 1], [3, 3, 11]]
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