def lstm_raw():
print('Loading data...')
folder_path = r'H:\network_diagnosis_data\cut-1000'
X_t, y_t,dicc=ReadData.ReadRaw2HierData(folder_path,N)
nb_classes = np.max(y_t)+1
X_t = ReadData.to_num(X_t,max_features)
X_train, X_test, y_train, y_test = train_test_split(X_t,y_t, test_size=0.2, random_state= 42 )
print('Pading sequences ')
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)#padding = 'post'
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)#truncating = 'post'
y_train = to_categorical (y_train,nb_classes)
y_test = to_categorical (y_test,nb_classes)
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
print('Building model...')
model = Sequential()
model.add(Embedding(max_features, Embedding_Dim, dropout=0.2))
model.add(LSTM(Embedding_Dim, dropout_W=0.2, dropout_U=0.2)) #
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# try using different optimizers and different optimizer configs
model.compile(loss='categorical_crossentropy', #binary_crossentropy
optimizer='adam',
metrics=['accuracy'])
from keras.utils.visualize_util import plot
plot(model, to_file=r'.\data\lstm-model.png')
print('Training...')
# model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=num_epoch,
# validation_data=(X_test, y_test))
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=num_epoch,
validation_split=0.1,verbose=1)
score, acc = model.evaluate(X_test, y_test,
batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
from keras.utils.visualize_util import plot
data_today=time.strftime('%Y-%m-%d',time.localtime(time.time()))
plot(model, to_file=r'.\data\lstm-model'+data_today+'.png')
json_string = model.to_json() #等价于 json_string = model.get_config()
open('.\data\lstm-model'+data_today+'.json','w+').write(json_string)
model.save_weights('.\data\keras-lstm'+data_today+'.h5', overwrite=True)
print('model saved')
print('Loading data...')
folder_path = r'H:\corpus_trained_model\nltk_data\corpora\movie_reviews'
'''
共读 400 个文件
max_features = 1000
maxlen = 1000 # cut texts after this number of words (among top max_features most common words)
batch_size = 32
num_epoch = 10
data set movie review
Test score: 0.753259503841
Test accuracy: 0.5375
'''
#folder_path = r'H:\network_diagnosis_data\new_cut'
X_t, y_t, dicc = ReadData.ReadRaw2HierData(folder_path, 200)
X_t = ReadData.to_num(X_t, max_features)
X_train, X_test, y_train, y_test = train_test_split(X_t,
y_t,
test_size=0.2,
random_state=42)
print('Pading sequences ')
X_train = sequence.pad_sequences(X_train, maxlen=maxlen) #padding = 'post'
X_test = sequence.pad_sequences(X_test, maxlen=maxlen) #truncating = 'post'
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
print('Building model...')
Embedding_Dim = 50
model = Sequential()
print type(X_t), type(y_t)
X_train, X_test, y_train, y_test = train_test_split(X_t,
y_t,
test_size=0.2,
random_state=42)
print(len(X_train), 'train samples')
print(len(X_test), 'test samples')
print 'Fitting'
classifier = LogisticRegression(C=1.0,
class_weight=None,
dual=False,
fit_intercept=True,
intercept_scaling=1,
penalty='l2',
random_state=None,
tol=0.001)
classifier.fit(X_train, y_train)
print classifier.score(X_test, y_test)
if __name__ == '__main__':
model_path = r'.\data\movie_review-50D-word-vector'
model_path = r'.\data\cut1000-all-50D-w2v'
file_path = r'H:\network_diagnosis_data\cut-500'
x, y, d = ReadData.ReadRaw2HierData(file_path, 5000)
new_x = average_word_vec(model_path, x)
test_log(new_x, y)
new_data = [new_x, y]
print new_x[0]
pickle.dump(new_data, open(r'.\data\w2v_replaced-500samples.pkl', 'wb'))
def mlp():
max_words = 10000 #1000
max_feature = 300
batch_size = 32
nb_epoch = 5
vec_dim = 50
print('Loading data...')
folder_path = r'H:\network_diagnosis_data\cut-500'
X_t, y_t, dicc = ReadData.ReadRaw2HierData(folder_path, 3000)
X_t, Y_t = ReadData.shuffle_X_Y(X_t, y_t)
X_t = ReadData.to_num(X_t, max_feature)
X_train, X_test, y_train, y_test = train_test_split(X_t,
y_t,
test_size=0.2,
random_state=42)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
nb_classes = np.max(y_train) + 1
print(nb_classes, 'classes')
print('Vectorizing sequence data...')
# 参考 http://keras-cn.readthedocs.io/en/latest/preprocessing/text/
tokenizer = Tokenizer(
nb_words=max_words
) #Tokenizer是一个用于向量化文本，或将文本转换为序列（即单词在字典中的下标构成的列表，从1算起）的类。
X_train = tokenizer.sequences_to_matrix(X_train, mode='binary')
X_test = tokenizer.sequences_to_matrix(X_test, mode='binary')
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
print(
'Convert class vector to binary class matrix (for use with categorical_crossentropy)'
)
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
print('Y_train shape:', Y_train.shape)
print('Y_test shape:', Y_test.shape)
print('Building model...')
model = Sequential()
model.add(Dense(
512, input_shape=(max_words, ))) # 全连接层 ，输入（，max_words）；输出（，512）
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Dense(nb_classes)) # 只需要定义输出层个数，分类类别个数
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(X_train,
Y_train,
nb_epoch=nb_epoch,
batch_size=batch_size,
verbose=1,
validation_split=0.1)
score = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=1)
print('Saving model...')
from keras.utils.visualize_util import plot
data_today = time.strftime('%Y-%m-%d', time.localtime(time.time()))
plot(model, to_file=r'.\data\mlp-model' + data_today + '.png')
json_string = model.to_json() #等价于 json_string = model.get_config()
open('.\data\mlp-model' + data_today + '.json', 'w+').write(json_string)
model.save_weights('.\data\keras-mlp' + data_today + '.h5', overwrite=True)
print('Test score:', score[0])
print('Test accuracy:', score[1])
# print (model.predict_classes(X_test,batch_size=batch_size))
print('---------------------------------')
