正如在this问题中提到的,我们需要顺序模型来使用.predict_classes
我正在使用这个模型,但仍然得到
AttributeError: 'function' object has no attribute 'predict_classes'
错误。我正在使用以下代码
def Build_Model_RNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):
model = Sequential()
hidden_layer = 3
gru_node = 32
embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
if len(embedding_matrix[i]) != len(embedding_vector):
print("could not broadcast input array from shape", str(len(embedding_matrix[i])),
"into shape", str(len(embedding_vector)), " Please make sure your"
" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,")
exit(1)
embedding_matrix[i] = embedding_vector
model.add(Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True))
print(gru_node)
for i in range(0,hidden_layer):
model.add(GRU(gru_node,return_sequences=True, recurrent_dropout=0.2))
model.add(Dropout(dropout))
model.add(GRU(gru_node, recurrent_dropout=0.2))
model.add(Dropout(dropout))
model.add(Dense(256, activation='relu'))
model.add(Dense(nclasses, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
即使在使用.predict
而不是.predict_classes
get时,我也会得到相同的错误
编辑:我正在使用以下代码调用方法
predicted = Build_Model_RNN_Text.predict_classes(X_test_Glove)
错误是由于您没有调用函数以获取其输出。干脆
您需要用函数所需的参数替换
<<args>>
。看起来你是想让Build_Model_RNN_Text
变成class
吗不管是哪种方式,由于您没有提供所需的参数
word_index
、embeddings_index
和nclasses
,因此您到底希望它如何工作相关问题 更多 >
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