TensorFlow情绪分析：使用自己的数据

Question

我一直在使用TFLearn示例中的以下代码：

"""
Simple example using LSTM recurrent neural network to classify IMDB
sentiment dataset.
References:
    - Long Short Term Memory, Sepp Hochreiter & Jurgen Schmidhuber, Neural
    Computation 9(8): 1735-1780, 1997.
    - Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng,
    and Christopher Potts. (2011). Learning Word Vectors for Sentiment
    Analysis. The 49th Annual Meeting of the Association for Computational
    Linguistics (ACL 2011).
Links:
    - http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
    - http://ai.stanford.edu/~amaas/data/sentiment/
"""
from __future__ import division, print_function, absolute_import
import os
import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb

# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=100,
                                valid_portion=0.1)

trainX, trainY = train
testX, testY = test

# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)

# Network building
net = tflearn.input_data([None, 100])
net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
                         loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True,
          batch_size=32)

该模型成功训练并设法在测试集上获得83％的准确度。我现在要做的是将这个训练有素的模型应用于新的看不见的数据。

例如，model.predict("I am very happy today")之类的内容应返回正面的情绪值。

我是TensorFlow和Python的新手，因为我一直在使用R来进行情绪分析，看起来在TensorFlow中使用LSTM模型的准确性要比我过去使用过的任何东西都高得多。

非常感谢任何帮助。