udemy ANN 的深度学习代码出错

Question

*我正在参加关于 Udemy 的深度学习课程。我写的代码和老师说的完全一样。但是classifier.fit(X_train, y_train, batch_size = 10,epochs = 100)之后出现问题，错误如下

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

# Encoding categorical data


from sklearn.preprocessing import OneHotEncoder, LabelEncoder

from sklearn.compose import ColumnTransformer

label_encoder_x_1 = LabelEncoder()
X[: , 2] = label_encoder_x_1.fit_transform(X[:,2])
transformer = ColumnTransformer(
    transformers=[
        ("OneHot",        # Just a name
         OneHotEncoder(), # The transformer class
         [1]              # The column(s) to be applied on.
         )
    ],
    remainder='passthrough' # donot apply anything to the remaining columns
)
X = transformer.fit_transform(X.tolist())
X = X.astype('float64')
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)

#importing keras 
import keras
from keras.models import Sequential
from keras.layers import Dense
# Fitting classifier to the Training set
# Create your classifier here
classifier = Sequential()
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

classifier.fit(X_train, y_train, batch_size = 10, epochs = 100) 
# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

文件“C:\Anaconda3\envs\py37\lib\site-packages\sklearn\metrics_classification.py”，第 268 行，位于混淆矩阵中 y_type, y_true, y_pred = _check_targets(y_true, y_pred)

文件“C:\Anaconda3\envs\py37\lib\site-packages\sklearn\metrics_classification.py”，第 90 行，在 _check_targets "和 {1} 个目标".format(type_true, type_pred))

ValueError：分类指标无法处理二进制和连续目标的混合

如何解决*

Answer 1

问题似乎出在y_pred = classifier.predict(X_test) 这一行上。根据文档predict_classes 用于获取类预测，https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#predict_classes。 Predict 正在返回不是类标签的连续值。我对你的代码做了一些小的调整，

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

#print(X)
#print(y)



# Encoding categorical data


from sklearn.preprocessing import OneHotEncoder, LabelEncoder

from sklearn.compose import ColumnTransformer

label_encoder_x_1 = LabelEncoder()
X[: , 2] = label_encoder_x_1.fit_transform(X[:,2])
transformer = ColumnTransformer(
    transformers=[
        ("OneHot",        # Just a name
         OneHotEncoder(), # The transformer class
         [1]              # The column(s) to be applied on.
         )
    ],
    remainder='passthrough' # donot apply anything to the remaining columns
)
X = transformer.fit_transform(X.tolist())
X = X.astype('float64')
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)

#print(sum(y_train))
#print(sum(y_test))

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#importing keras 
import keras
from keras.models import Sequential
from keras.layers import Dense
# Fitting classifier to the Training set
# Create your classifier here
classifier = Sequential()
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

classifier.fit(X_train, y_train, batch_size = 10, epochs = 10) 
# Predicting the Test set results
y_pred = classifier.predict_classes(X_test)
#print(classifier.predict(X_test))
#print(y_pred)


# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
#cm = confusion_matrix(y_test, y_pred)

print(confusion_matrix(y_test, y_pred, labels=[0, 1]))
print(classification_report(y_test, y_pred, target_names=['0', '1']))