0

我有一个回归数据集：

X_train (float64) Size = (1616, 3) -> i.e. 3 predictors
Y_train (float64) Size = (1616, 2) -> i.e. 2 targets

我尝试使用功能 API 做 Hyperas（我的主要目的是loss_weights在编译期间使用该选项）：

inputs1 = Input(shape=(X_train.shape[0], X_train.shape[1]))

x  = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(inputs1)
x  = Dropout({{uniform(0, 1)}})(x)

x  = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(x)
x  = Dropout({{uniform(0, 1)}})(x)

x  = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(x)
x  = Dropout({{uniform(0, 1)}})(x)

if conditional({{choice(['three', 'four'])}}) == 'four':
    x  = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(x)
    x  = Dropout({{uniform(0, 1)}})(x)

output1 = Dense(1,  activation='linear')(x)
output2 = Dense(1,  activation='linear')(x)

model = Model(inputs = inputs1, outputs = [output1,output2])

adam    = keras.optimizers.Adam(lr={{choice([10**-3,10**-2, 10**-1])}})
rmsprop = keras.optimizers.RMSprop(lr={{choice([10**-3,10**-2, 10**-1])}})
sgd     = keras.optimizers.SGD(lr={{choice([10**-3,10**-2, 10**-1])}})

choiceval = {{choice(['adam', 'rmsprop','sgd'])}}
if choiceval == 'adam':
    optimizer = adam
elif choiceval == 'rmsprop':
    optimizer = rmsprop
else:
    optimizer = sgd

model.compile(loss='mae', metrics=['mae'],optimizer=optimizer, loss_weights=[0.5,0.5])

earlyStopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=50, verbose=0, mode='auto')
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=2, save_best_only=True, mode='max')
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.5, cooldown=1, patience=10, min_lr=1e-4,verbose=2)
callbacks_list = [earlyStopping, checkpoint, lr_reducer]

history = model.fit(X_train, Y_train,
          batch_size={{choice([16,32,64,128])}},
          epochs={{choice([20000])}},
          verbose=2,
          validation_data=(X_val, Y_val),
          callbacks=callbacks_list)

但是，在运行它时，我收到以下错误：

ValueError: Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (1616, 3)

如果有人能指出这里出了什么问题的方向，我将不胜感激。我怀疑输入（即X_train, Y_train）以及输入形状可能有问题。将不胜感激这里的任何帮助。

更新

好的，确实故障出在输入行：

我把它改成：inputs1 = Input(shape=(X_train.shape[1],))。

但是，现在我收到另一个错误：

ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [array([[0.19204772, 0.04878049],
   [0.20226056, 0.        ],
   [0.12029842, 0.04878049],
   ...,
   [0.45188627, 0.14634146],
   [0.26942276, 0.02439024],
   [0.12942418, 0....
4

1 回答 1

0

由于您的模型有两个输出层，因此您需要在调用方法时将两个数组的列表作为真正的目标（即）传递。例如像这样：yfit()

model.fit(X_train, [Y_train[:,0:1], Y_train[:,1:]], ...)
于 2018-11-15T14:53:59.877 回答