嘗試使用Keras / Theano運行LSTM多標簽文本分類 .

我有一個文本/標簽csv . 文本是純文本，標簽是數字，總共9個，從1到9 .

我想我沒有為這個問題正確配置模型 . 我的代碼到目前為止：

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)

我收到此錯誤：

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])

總結

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