Question

我已经使用Yelp-Data-Challenge数据训练了一个模型得到了一个pickle文件399850by50reviews_words_index.pkl但我已经讨论了如何使用这个pickle文件来测试keras中的新数据

这是我训练数据和保存到模型创建的代码

如何将此模型用于测试数据

我在这里使用Keras 1.0.0与Theano

'''
train cnn mode for sentiment classification on yelp data set
author: hao peng
'''
import pickle
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from Word2VecUtility import Word2VecUtility
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.embeddings import Embedding
from keras.layers.convolutional import Convolution1D, MaxPooling1D


def get_volcabulary_and_list_words(data):
    reviews_words = []
    volcabulary = []
    for review in data["text"]:
        review_words = Word2VecUtility.review_to_wordlist(
            review, remove_stopwords=True)
        reviews_words.append(review_words)
        for word in review_words:
            volcabulary.append(word)
    volcabulary = set(volcabulary)
    return volcabulary, reviews_words

def get_reviews_word_index(reviews_words, volcabulary, max_words, max_length):
    word2index = {word: i for i, word in enumerate(volcabulary)}
    # use w in volcabulary to limit index within max_words
    reviews_words_index = [[start] + [(word2index[w] + index_from) for w in review] for review in reviews_words]
    # in word2vec embedding, use (i < max_words + index_from) because we need the exact index for each word, in order to map it to its vector. And then its max_words is 5003 instead of 5000.
    reviews_words_index = [[i if (i < max_words) else oov for i in index] for index in reviews_words_index]
    # padding with 0, each review has max_length now.
    reviews_words_index = sequence.pad_sequences(reviews_words_index, maxlen=max_length, padding='post', truncating='post')
    return reviews_words_index

def vectorize_labels(labels, nums):
    labels = np.asarray(labels, dtype='int32')
    length = len(labels)
    Y = np.zeros((length, nums))
    for i in range(length):
        Y[i, (labels[i]-1)] = 1.
    return Y
# data processing para
max_words = 5000
max_length = 50

# model training parameters
batch_size = 32
embedding_dims = 100
nb_filter = 250
filter_length = 3
hidden_dims = 250
nb_epoch = 2

# index trick parameters
index_from = 3
start = 1
# padding = 0
oov = 2

data = pd.read_csv(
    'review_sub_399850.tsv', header=0, delimiter="\t", quoting=3, encoding='utf-8')
print('get volcabulary...')
volcabulary, reviews_words = get_volcabulary_and_list_words(data)
print('get reviews_words_index...')
reviews_words_index = get_reviews_word_index(reviews_words, volcabulary, max_words, max_length)

print reviews_words_index[:20, :12]
print reviews_words_index.shape

labels = data["stars"]

pickle.dump((reviews_words_index, labels), open("399850by50reviews_words_index.pkl", 'wb'))

(reviews_words_index, labels) = pickle.load(open("399850by50reviews_words_index.pkl", 'rb'))

index = np.arange(reviews_words_index.shape[0])
train_index, valid_index = train_test_split(
    index, train_size=0.8, random_state=100)

labels = vectorize_labels(labels, 5)
train_data = reviews_words_index[train_index]
valid_data = reviews_words_index[valid_index]
train_labels = labels[train_index]
valid_labels = labels[valid_index]
print train_data.shape
print valid_data.shape
print train_labels[:10]

del(labels, train_index, valid_index)

print "start training model..."

model = Sequential()

# we start off with an efficient embedding layer which maps
# our vocab indices into embedding_dims dimensions
model.add(Embedding(max_words + index_from, embedding_dims, \
                    input_length=max_length))
model.add(Dropout(0.25))

# we add a Convolution1D, which will learn nb_filter
# word group filters of size filter_length:

# filter_length is like filter size, subsample_length is like step in 2D CNN.
model.add(Convolution1D(nb_filter=nb_filter,
                        filter_length=filter_length,
                        border_mode='valid',
                        activation='relu',
                        subsample_length=1))
# we use standard max pooling (halving the output of the previous layer):
model.add(MaxPooling1D(pool_length=2))

# We flatten the output of the conv layer,
# so that we can add a vanilla dense layer:
model.add(Flatten())

# We add a vanilla hidden layer:
model.add(Dense(hidden_dims))
model.add(Dropout(0.25))
model.add(Activation('relu'))

# We project onto 5 unit output layer, and activate it with softmax:
model.add(Dense(5))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              class_mode='categorical')
model.fit(train_data, train_labels, batch_size=batch_size,
          nb_epoch=nb_epoch, show_accuracy=True,
          validation_data=(valid_data, valid_labels))

Answer 1

用于测试的输入数据的形状必须与train_data和valid_data完全相同，但第一个维度是批量大小。

因此，您必须使用要测试的输入数据创建一个numpy数组，并确保此数组的结构与train_data完全相同，yourTestArray.shape[1:]与train_data.shape[1:]完全相同，也等于valid_data.shape[1:]。

拥有该数组后，您应该使用results = model.predict(yourTestArray)。

测试新数据预测

1 个答案: