确保所有数组包含相同数量的样本

编程问答 2022-04-23

问题描述

代码：

train_X,val_X,train_y,val_y 
train_X = np.array(train_X)
train_y = np.array(train_y)
val_X  = np.array(val_X)
val_y  = np.array(val_y)

k = 5
num_val_samples = len(train_X) // k
num_epochs = 100
all_scores = []
for i in range(k):
    print(f"Processing fold #{i}")
    val_data = train_X[i * num_val_samples: (i + 1) * num_val_samples]
    val_targets = val_X[i * num_val_samples: (i + 1) * num_val_samples]
    partial_train_X = np.concatenate(
        [train_X[:i * num_val_samples],train_X[(i + 1) * num_val_samples:]],axis=0)
    partial_val_X = np.concatenate(
        [val_X[:i * num_val_samples],val_X[(i + 1) * num_val_samples:]],axis=0)
    model = build_model()
   
    model.fit(partial_train_X,partial_val_X,epochs=num_epochs,batch_size=16,verbose=0)   
    val_mse,val_mae = model.evaluate(val_data,val_targets,verbose=0)
    all_scores.append(val_mae)

错误：

> ...  model.fit(partial_train_X,> 
> --- 26               epochs=num_epochs,verbose=0)   ....
> 
> ValueError: Data cardinality is ambiguous:   x sizes: 876   y sizes:
> 146 
> Make sure all arrays contain the same number of samples.

我尝试了之前提到的解决方案。 （例如，np.array(train_X)。但我无论如何都无法修复它。一直以来，我也无法配置批量大小。请您帮帮我好吗？

解决方法

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