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tormozzzz
Asked:2020-08-19 03:01:33 +0000 UTC2020-08-19 03:01:33 +0000 UTC

检查目标时出错：预期 dense_2 的形状为 (1,) 但得到的数组的形状为 (3,)

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import numpy as np
x_train,y_train = np.load('datsx.npy'),np.load('datsy.npy')
wb = None
from keras.models import Sequential
from keras.layers import Dense

# Среднее значение
mean = x_train.mean(axis=0)
# Стандартное отклонение
std = x_train.std(axis=0)
x_train -= mean

x_train /= std
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(x_train.shape[1],)))#shape 1
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.fit(x_train, y_train, epochs=100, batch_size=1, verbose=2)

检查目标时出错：预期 dense_2 的形状为 (1,) 但得到的数组的形状为 (3,)

如何修复这里的错误？

样本数据：

x_train = array(
    [[1.3590000e+03, 1.3180000e+03, 1.7082020e+07, 1.2000000e+03],
     [4.0380000e+03, 4.6170000e+03, 1.7082020e+07, 1.2000000e+03], [2.6300000e+03,
      3.9840000e+03, 1.7082020e+07, 1.0540000e+03], [3.4460000e+03, 4.5310000e+03, 
      1.8102014e+07, 2.1610000e+03], [9.1500000e+02, 4.5310000e+03, 1.8102014e+07,
      2.1610000e+03], [3.4460000e+03, 4.4570000e+03, 1.8102014e+07, 2.1610000e+03]]) 

y_train = array(
    [[ 1., 2., 2.], [ 1., 2., 2.], [ 1., 2., 2.], 
     [16., 2., 1.], [16., 4., 1.], [16., 0., 1.]])
python

1 个回答

  1. Best Answer

    您已经构建了一个具有一个输出列的 ANN Dense(1)，并且为了训练您将一个y_train具有三列的张量传递给它。因此错误expected dense_2 to have shape (1,) but got array with shape (3,)。

    如果您期望输出中有三列，则必须相应地配置 ANN 的最后 / 输出层：

    model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(x_train.shape[1],)))#shape 1
model.add(Dense(3)) # <----- NOTE !!!
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.fit(x_train, y_train, epochs=100, batch_size=1, verbose=2)

    例子：

    In [371]: x_train.shape
Out[371]: (6, 4)

In [372]: y_train.shape
Out[372]: (6, 3)

In [373]: model = Sequential()
     ...: model.add(Dense(128, activation='relu', input_shape=(x_train.shape[1],)))#shape 1
     ...: model.add(Dense(3)) # <----- NOTE !!!
     ...: model.compile(optimizer='adam', loss='mse', metrics=['mae'])
     ...: model.fit(x_train, y_train, epochs=10, batch_size=1, verbose=2)
Epoch 1/10
 - 0s - loss: 44.4637 - mae: 4.0030
Epoch 2/10
 - 0s - loss: 43.2938 - mae: 3.9162
Epoch 3/10
 - 0s - loss: 42.2873 - mae: 3.8323
Epoch 4/10
 - 0s - loss: 41.3176 - mae: 3.7532
Epoch 5/10
 - 0s - loss: 40.2755 - mae: 3.6764
Epoch 6/10
 - 0s - loss: 39.4588 - mae: 3.5963
Epoch 7/10
 - 0s - loss: 38.4322 - mae: 3.5167
Epoch 8/10
 - 0s - loss: 37.5529 - mae: 3.4401
Epoch 9/10
 - 0s - loss: 36.6613 - mae: 3.3579
Epoch 10/10
 - 0s - loss: 35.7188 - mae: 3.2772
Out[373]: <keras.callbacks.callbacks.History at 0x20714861388>
    • 3

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