我无法在下面挑选我的模型。
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
script_dir = os.path.dirname(__file__)
abs_file_path = os.path.join(script_dir, 'Churn_Modelling.csv')
# Importing the dataset
dataset = pd.read_csv(abs_file_path)
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features=[1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Part 2 - Now let's make the ANN!
# Importing the Keras libraries and packages
from tensorflow.contrib.keras.api.keras.models import Sequential
from tensorflow.contrib.keras.api.keras.layers import Dense
from tensorflow.contrib.keras import backend
# Initialising the ANN
classifier = Sequential()
# Adding the input layer and the first hidden layer
classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))
# Adding the second hidden layer
classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))
# Adding the output layer
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
# Compiling the ANN
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size=10, epochs=100, validation_split=0.1)
# Part 3 - Making predictions and evaluating the model
# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
# Predicting a single new observation
new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
new_prediction = (new_prediction > 0.5)
我尝试过使用
from sklearn.externals import joblib
joblib.dump(classifier, 'model.pkl')
和
import pickle
with open('classifier.pkl', 'wb') as fid:
pickle.dump(classifier, fid,2)
对于两者，我得到 PicklingError：无法pickle：内置函数上的属性查找模块失败
我做错了什么？你的见解很受欢迎。