尝试使用Keras / Theano运行LSTM多标签文本分类 .

我有一个文本/标签csv . 文本是纯文本，标签是数字，总共9个，从1到9 .

我想我没有为这个问题正确配置模型 . 我的代码到目前为止：

import keras.preprocessing.text

import numpy as np

Using Theano backend.

from keras.preprocessing import sequence

from keras.models import Sequential

from keras.layers.core import Dense, Activation

from keras.layers.embeddings import Embedding

from keras.layers.recurrent import LSTM

import pandas

data = pandas.read_csv("for_keras_text_label.csv", sep = ',', quotechar = '"', header = 0)

x = data['text']

y = data['label']

x = x.iloc[:].values

y = y.iloc[:].values

tk = keras.preprocessing.text.Tokenizer(nb_words=2000, filters=keras.preprocessing.text.base_filter(), lower=True, split=" ")

tk.fit_on_texts(x)

x = tk.texts_to_sequences(x)

max_len = 80

print "max_len ", max_len

print('Pad sequences (samples x time)')

x = sequence.pad_sequences(x, maxlen=max_len)

# the model

max_features = 20000

model = Sequential()

model.add(Embedding(max_features, 128, input_length=max_len, dropout=0.2))

model.add(LSTM(128, dropout_W=0.2, dropout_U=0.2))

model.add(Dense(9))

model.add(Activation('softmax'))

model.compile(loss='sparse_categorical_crossentropy', optimizer='rmsprop', metrics=["accuracy"])

# run

model.fit(x, y=y, batch_size=200, nb_epoch=1, verbose=1, validation_split=0.2, shuffle=True)

我收到此错误：

IndexError: index 9 is out of bounds for axis 1 with size 9 Apply node that caused the error:

AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True}(Alloc.0, TensorConstant{1}, ARange{dtype='int64'}.0, Elemwise{Cast{int32}}.0)

Toposort index: 213

Inputs types: [TensorType(float32, matrix), TensorType(int8, scalar), TensorType(int64, vector), TensorType(int32, vector)]

Inputs shapes: [(200, 9), (), (200,), (200,)]

Inputs strides: [(36, 4), (), (8,), (4,)]

Inputs values: ['not shown', array(1, dtype=int8), 'not shown', 'not shown']

Outputs clients: [[Reshape{2}(AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True}.0, MakeVector{dtype='int64'}.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):

File "/home/ubuntu/anaconda3/envs/theano/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes

if self.run_code(code, result):

File "/home/ubuntu/anaconda3/envs/theano/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code

exec(code_obj, self.user_global_ns, self.user_ns)

File "", line 7, in

model.compile(loss='sparse_categorical_crossentropy', optimizer='rmsprop', metrics=["accuracy"])

File "/home/ubuntu/anaconda3/envs/theano/lib/python2.7/site-packages/keras/models.py", line 578, in compile

**kwargs)

File "/home/ubuntu/anaconda3/envs/theano/lib/python2.7/site-packages/keras/engine/training.py", line 604, in compile

sample_weight, mask)

File "/home/ubuntu/anaconda3/envs/theano/lib/python2.7/site-packages/keras/engine/training.py", line 303, in weighted

score_array = fn(y_true, y_pred)

File "/home/ubuntu/anaconda3/envs/theano/lib/python2.7/site-packages/keras/objectives.py", line 45, in sparse_categorical_crossentropy

return K.sparse_categorical_crossentropy(y_pred, y_true)

File "/home/ubuntu/anaconda3/envs/theano/lib/python2.7/site-packages/keras/backend/theano_backend.py", line 1079, in sparse_categorical_crossentropy

target = T.extra_ops.to_one_hot(target, nb_class=output.shape[-1])