我有 3 个输入和 3 个输出。我正在尝试使用 KerasRegressor 和 cross_val_score 来获得我的预测分数。
我的代码是:
# Function to create model, required for KerasClassifier
def create_model():
# create model
# #Start defining the input tensor:
input_data = layers.Input(shape=(3,))
#create the layers and pass them the input tensor to get the output tensor:
layer = [2,2]
hidden1Out = Dense(units=layer[0], activation='relu')(input_data)
finalOut = Dense(units=layer[1], activation='relu')(hidden1Out)
u_out = Dense(1, activation='linear', name='u')(finalOut)
v_out = Dense(1, activation='linear', name='v')(finalOut)
p_out = Dense(1, activation='linear', name='p')(finalOut)
#define the model's start and end points
model = Model(input_data,outputs = [u_out, v_out, p_out])
model.compile(loss='mean_squared_error', optimizer='adam')
return model
#load data
...
input_var = np.vstack((AOA, x, y)).T
output_var = np.vstack((u,v,p)).T
# evaluate model
estimator = KerasRegressor(build_fn=create_model, epochs=num_epochs, batch_size=batch_size, verbose=0)
kfold = KFold(n_splits=10)
我试过:
results = cross_val_score(estimator, input_var, [output_var[:,0], output_var[:,1], output_var[:,2]], cv=kfold)
和
results = cross_val_score(estimator, input_var, [output_var[:,0:1], output_var[:,1:2], output_var[:,2:3]], cv=kfold)
和
results = cross_val_score(estimator, input_var, output_var, cv=kfold)
我收到错误消息,如:
细节:
ValueError:检查模型目标时出错:您传递给模型的 Numpy 数组列表不是模型预期的大小。预计会看到 3 个数组,但得到了以下 1 个数组的列表: [array([[ 0.69945297, 0.13296847, 0.06292328],
或者
ValueError:发现样本数量不一致的输入变量:[72963, 3]
那么我该如何解决这个问题呢?
谢谢。
最佳答案
问题是层的输入维度Input
不是 3
,但是 3*feature_dim
.下面是一个工作示例
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input,Dense,Concatenate
from sklearn.model_selection import cross_val_score,KFold
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
def create_model():
feature_dim = 10
input_data = Input(shape=(3*feature_dim,))
#create the layers and pass them the input tensor to get the output tensor:
layer = [2,2]
hidden1Out = Dense(units=layer[0], activation='relu')(input_data)
finalOut = Dense(units=layer[1], activation='relu')(hidden1Out)
u_out = Dense(1, activation='linear', name='u')(finalOut)
v_out = Dense(1, activation='linear', name='v')(finalOut)
p_out = Dense(1, activation='linear', name='p')(finalOut)
output = Concatenate()([u_out,v_out,p_out])
#define the model's start and end points
model = Model(inputs=input_data,outputs=output)
model.compile(loss='mean_squared_error', optimizer='adam')
return model
x_0 = np.random.rand(100,10)
x_1 = np.random.rand(100,10)
x_2 = np.random.rand(100,10)
input_val = np.hstack([x_0,x_1,x_2])
u = np.random.rand(100,1)
v = np.random.rand(100,1)
p = np.random.rand(100,1)
output_val = np.hstack([u,v,p])
estimator = KerasRegressor(build_fn=create_model,nb_epoch=3,batch_size=8,verbose=False)
kfold = KFold(n_splits=3, random_state=0)
results = cross_val_score(estimator=estimator,X=input_val,y=output_val,cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
如您所见,由于输入维度是 10
, 内部 create_model
, 我指定了 feature_dim
.
关于python - KerasRegressor 和多输出问题,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/61030977/