Keras模型具有较高的准确性，但预测效果较差 [英] Keras model with high accuracy but poor predictions
问题描述
我正在尝试训练神经网络，以对具有预定义段长的机械臂进行逆运动学计算.我没有在神经网络输入中包括段长度，而是通过训练数据.训练数据是一个具有手臂空间映射的熊猫数据框，其中标签是手臂三个部分的旋转角度，特征是最后一个部分的端点在其中的x和y坐标的解结束于.
I am trying to train a neural network to make Inverse Kinematics calculations for a robotic arm with predefined segment lengths. I am not including the segment lengths in neural network inputs but rather through the training data. The training data is a pandas dataframe with the spatial mappings of the arm, with labels being the angles of rotation for the three segments of the arm and the features being the solutions of the x and y coordinates of where the endpoint of the last segment would end up in.
我正在将Theano作为后端使用Keras.
I am using Keras with Theano as the Backend.
model = Sequential([
Dense(3, input_shape=(2,), activation="relu"),
Dense(3, activation="relu"),
Dense(3)
])
model.summary()
model.compile(Adam(lr=0.001), loss='mean_squared_error', metrics=['accuracy'])
model.fit(samples, labels, validation_split=0.2, batch_size=1000, epochs=10,shuffle=True, verbose=1)
score = model.evaluate(samples, labels, batch_size=32, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])
weights = model.get_weights()
predictions = model.predict(samples, verbose=1)
print predictions
model.save("IK_NN_7-4-3_keras.h5")
OUTPUT===============================================================
Train on 6272736 samples, validate on 1568184 samples
Epoch 1/10
- 5s - loss: 10198.7558 - acc: 0.9409 - val_loss: 12149.1703 - val_acc: 0.9858
Epoch 2/10
- 5s - loss: 4272.9105 - acc: 0.9932 - val_loss: 12117.0527 - val_acc: 0.9858
Epoch 3/10
- 5s - loss: 4272.7862 - acc: 0.9932 - val_loss: 12113.3804 - val_acc: 0.9858
Epoch 4/10
- 5s - loss: 4272.7567 - acc: 0.9932 - val_loss: 12050.8211 - val_acc: 0.9858
Epoch 5/10
- 5s - loss: 4272.7271 - acc: 0.9932 - val_loss: 12036.5538 - val_acc: 0.9858
Epoch 6/10
- 5s - loss: 4272.7350 - acc: 0.9932 - val_loss: 12103.8665 - val_acc: 0.9858
Epoch 7/10
- 5s - loss: 4272.7553 - acc: 0.9932 - val_loss: 12175.0442 - val_acc: 0.9858
Epoch 8/10
- 5s - loss: 4272.7282 - acc: 0.9932 - val_loss: 12161.4815 - val_acc: 0.9858
Epoch 9/10
- 5s - loss: 4272.7213 - acc: 0.9932 - val_loss: 12101.4021 - val_acc: 0.9858
Epoch 10/10
- 5s - loss: 4272.7909 - acc: 0.9932 - val_loss: 12152.4966 - val_acc: 0.9858
Test score: 5848.549130022683
Test accuracy: 0.9917127071823204
[[ 59.452095 159.26912 258.94424 ]
[ 59.382706 159.41936 259.25183 ]
[ 59.72419 159.69777 259.48584 ]
...
[ 59.58721 159.33467 258.9603 ]
[ 59.51745 159.69331 259.62595 ]
[ 59.984367 160.5533 260.7689 ]]
测试准确性和验证准确性都不错，但它们并不能完全反映现实.预测应该看起来像这样
Both the test accuracy and validation accuracy are seem good but they don't exactly reflect the reality. The predictions should have looked something like this
[[ 0 0 0]
[ 0 0 1]
[ 0 0 2]
...
[358 358 359]
[358 359 359]
[359 359 359]]
由于我反馈了期望获得相同标签的相同功能.相反，出于某些原因，我得到了这个数字:
Since I fed back the same features expecting to get the same labels. Instead I'm getting this numbers for some reason:
[[ 59.452095 159.26912 258.94424 ]
[ 59.382706 159.41936 259.25183 ]
[ 59.72419 159.69777 259.48584 ]
...
[ 59.58721 159.33467 258.9603 ]
[ 59.51745 159.69331 259.62595 ]
[ 59.984367 160.5533 260.7689 ]]
谢谢您的时间.
推荐答案
首先，您的指标是准确性，并且您要预测连续值.您得到了预测，但是它们没有任何意义.您的问题是回归，而指标则用于分类.您可以只使用"MSE"，R²"或其他回归指标
First of all your metric is accuracy and you are predicting continuous values. You get predictions, but they don´t make any sense. Your problem is a regression and your metric is for classification. You could just use "MSE" "R²" or other regression metrics
from keras import metrics
model.compile(loss='mse', optimizer='adam', metrics=[metrics.mean_squared_error, metrics.mean_absolute_error])
此外，您应该考虑增加神经元的数量，如果输入数据实际上仅是二维的，请考虑一些浅层模型，而不是ANN. (例如具有高斯内核的SVM)
Additionally you should consider increasing the number of neurons and if your input data is really only 2 dimensions, think about some shallow models, not ANNs. (SVM with gauss kernel e.g.)
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