Question

我的代码在泰坦尼克号数据集上，这是我的代码。执行预测后的形状，我什至试图在密函数的第一层中更改input_dim。而且有必要对Y_train进行缩放，因为很有可能也会对预测中的数据进行缩放。

  import numpy as np
  import pandas as pd 
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.wrappers.scikit_learn import   KerasClassifier
  from keras.wrappers.scikit_learn import KerasRegressor
  from sklearn.preprocessing import StandardScaler

 import time
 from datetime import timedelta#combination of date and time 

 dataset_train=pd.read_csv("train.csv")
 dataset_test=pd.read_csv("test.csv")

       #Filling the missing values 
         def data(df):
         df=df.drop(["Name","Ticket","Cabin"],axis=1)
         df['Age']=df['Age'].fillna(value=df['Age'].mean())
         df['Fare']=df['Fare'].fillna(value=df['Fare'].mean())
         df['Embarked'] = 
         df['Embarked'].fillna(value=df['Embarked'].
         value_counts().idxmax())

 #Mapping male and female 
 df['Sex']=df['Sex'].map({'female':0,'male':1}).astype(int
 one_hot_vector=pd.get_dummies(df['Embarked'],prefix='Embarked')
 df=df.drop('Embarked',axis=1)
 df=df.join(one_hot_vector)
 return 
 dataset_train=dat

  a(dataset_train)

#Splitting the dataset
 X=dataset_train.drop(['Survived'],axis=1).values
 scale=StandardScaler()
 X=scale.fit_transform(X)
 Y=dataset_train['Survived'].values

 def create_model(optimizer="adam",init="uniform"):
 model=Sequential()
 model.add(Dense(20,input_dim=X.shape[1],
 kernel_initializer='normal',activation="relu"))
 model.add(Dense(8,kernel_initializer='normal',activation="relu"))
 model.add(Dense(4,kernel_initializer='normal',activation="relu"))
 model.add(Dense(1,kernel_initializer='normal',
 activation="sigmoid"))

 #Compiling the model 
   model.compile(loss="binary_crossentropy",
   optimizer=optimizer,metric=["accuracy"])
   return model
   estimator=KerasClassifier(build_fn=create_model,
   epochs=200,batch_size=5,verbose=1,
   init='glorot_uniform',optimizer='rmsprop')
   estimator.fit(X,Y)

   test_df=data(dataset_test)
   validation_df=scale.fit_transform(test_df)
   data=validation_df[:,0]

   prediction=estimator.predict(data)

ValueError：检查错误：预期density_4_input具有形状（10，），但数组的形状为（1，）

0 个答案: