我正在尝试使用keras构建分类器,下面是我的代码:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.preprocessing import MinMaxScaler
from collections import Counter
from imblearn.over_sampling import SMOTE
from keras.models import Sequential
from keras.layers import Dense
import pickle
import joblib
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('bankloan.csv')
df = df.dropna()
df.isna().any()
df = df.drop('Loan_ID', axis=1)
df['LoanAmount'] = (df['LoanAmount']*1000).astype(int)
pre_y = df['Loan_Status']
pre_X = df.drop('Loan_Status', axis=1)
dm_X = pd.get_dummies(pre_X)
dm_y = pre_y.map(dict(Y=1, N=0))
smote = SMOTE()
X1, y = smote.fit_sample(dm_X, dm_y)
sc = MinMaxScaler()
X = sc.fit_transform(X1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)
classifier = Sequential()
classifier.add(Dense(200, activation='relu', input_dim=X_test.shape[1]))
classifier.add(Dense(400, activation='relu'))
classifier.add(Dense(4, activation='relu'))
classifier.add(Dense(1, activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifier.fit(X_train, y_train, batch_size=20, epochs=100, verbose=0)
filename = 'loan_model.pkl'
joblib.dump(classifier, filename)
这是我正在尝试做的,模型非常好,但是在最后一步,当我尝试保存模型时显示此错误:
TypeError:无法腌制_thread.RLock对象
答案 0 :(得分:0)
在对模型进行酸洗之前运行以下代码:
import types
import tempfile
import keras.models
def make_keras_picklable():
def __getstate__(self):
model_str = ""
with tempfile.NamedTemporaryFile(suffix='.hdf5', delete=True) as fd:
keras.models.save_model(self, fd.name, overwrite=True)
model_str = fd.read()
d = { 'model_str': model_str }
return d
def __setstate__(self, state):
with tempfile.NamedTemporaryFile(suffix='.hdf5', delete=True) as fd:
fd.write(state['model_str'])
fd.flush()
model = keras.models.load_model(fd.name)
self._dict_ = model._dict_
cls = keras.models.Model
cls.__getstate__ = __getstate__
cls.__setstate__ = __setstate__
make_keras_picklable()