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<p>我有3个输入和3个输出。我正在尝试使用KerasRegressor和cross_val_分数来获得我的预测分数</p>
<p>我的代码是:</p>
<pre><code># 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)
</code></pre>
<p>我试过:</p>
<pre><code>results = cross_val_score(estimator, input_var, [output_var[:,0], output_var[:,1], output_var[:,2]], cv=kfold)
</code></pre>
<p>及</p>
<pre><code>results = cross_val_score(estimator, input_var, [output_var[:,0:1], output_var[:,1:2], output_var[:,2:3]], cv=kfold)
</code></pre>
<p>及</p>
<pre><code>results = cross_val_score(estimator, input_var, output_var, cv=kfold)
</code></pre>
<p>我得到的错误消息如下:</p>
<p>详情:
ValueError:检查模型目标时出错:传递给模型的Numpy数组列表的大小不是模型预期的大小。预期会看到3个数组,但得到了以下1个数组的列表:[array([[0.69945297,0.13296847,0.06292328]</p>
<p>或</p>
<p>ValueError:找到样本数不一致的输入变量:[72963,3]</p>
<p>那么我该如何解决这个问题呢</p>
<p>谢谢</p>