python - TFLearn 对每个预测产生相同的结果

Question

我的网络为每个预测生成相同的输出。我在 pandas 数据框中有大约 49,000 个数据样本。
我怎样才能解决这个问题？

# Input data X.as_matrix() => 8 dimensional array  
# One example: [1.50000000e+00,3.00000000e+00,6.00000000e+00,2.40000000e+01,9.50000000e+01,3.00000000e+03,5.00000000e+00,1.50000000e+00]  
import tensorflow as tf  
import tflearn  

with tf.Graph().as_default():  
    net = tflearn.input_data([None, 8])  

    net = tflearn.fully_connected(net, 20, activation='softmax',weights_init='normal',regularizer='L2', weight_decay=0.001)   
    net = tflearn.fully_connected(net, 3, activation='softmax',weights_init='normal')  
    sgd = tflearn.Adam(learning_rate=0.01)  
    net = tflearn.regression(net, optimizer=sgd,loss='categorical_crossentropy')  
    model = tflearn.DNN(net)  
    model.fit(X.as_matrix(), Y, show_metric=True, batch_size=10, n_epoch=2, snapshot_epoch=False)  
print(model.predict([X.as_matrix()[1]]))  
print(model.predict([X.as_matrix()[2]]))  
print(model.predict([X.as_matrix()[3]]))    

Result:  
[0.6711940169334412,0.24268993735313416,0.08611597120761871]  
[0.6711940169334412,0.24268993735313416,0.08611597120761871]  
[0.6711940169334412,0.24268993735313416,0.08611597120761871]  

Actual:  
[ 0,  1,  0]  
[ 1,  0,  0]  
[ 0,  0,  1]  

Answer 1

尝试使用 sigmoid 或 relu 而不是 softmax。我对那些 2 有了更好的预测。也许你想对第一层使用 sigmoid，对第二层使用 relu。只需与它们一起玩并将它们组合起来，以便您有更好的预测。也可以尝试其他损失函数。