損失函數返回nan tensorflow

我在這裏寫了一個簡單的tensorflow程序，它讀入一個特徵列表並嘗試預測這個類。損失函數返回nan tensorflow

with tf.Session() as sess: 
     sess.run(tf.initialize_all_variables()) 

     for epoch in range (hm_epochs): 
      epoch_loss = 0 
      itere = int(X_train.shape[0]/batch_size) 
      last = 0 
      add = 1 
      for start in range(itere): 
       x_train_epoch = X_train[last: ((start + add) * batch_size),:] 
       y_train_epoch = y_1Hot_train.eval()[last: ((start + add) * batch_size),:] 
#     print("shape of x", x_train_epoch.shape, "shape of y", y_train_epoch.shape) 
       _, c = sess.run([optimizer, cost], feed_dict = {x: x_train_epoch, y: y_train_epoch}) 
       epoch_loss += c 
       last = start * batch_size 
       add = 0 
      print('Epoch', epoch, 'completed out of', hm_epochs, 'loss', epoch_loss) 
     correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Accuracy:', accuracy.eval({x: X_test, y: y_1Hot_test.eval() }))

鏈接：https://gist.github.com/makark/79af6ca53ca27d51abb1d87c9b9bac07

數據文件：https://gist.github.com/makark/eb859f50237edb9343f3ca32aeb3be2b

然而，當我運行我的代碼，我不斷收到「南」的損失。我不知道發生了什麼事......任何幫助將不勝感激！

WARNING:tensorflow:From <ipython-input-149-0164f4af7d52>:46: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. 
Instructions for updating: 
Use `tf.global_variables_initializer` instead. 
Epoch 0 completed out of 10 loss nan 
Epoch 1 completed out of 10 loss nan 
Epoch 2 completed out of 10 loss nan 
Epoch 3 completed out of 10 loss nan 
Epoch 4 completed out of 10 loss nan 
Epoch 5 completed out of 10 loss nan 
Epoch 6 completed out of 10 loss nan 
Epoch 7 completed out of 10 loss nan 
Epoch 8 completed out of 10 loss nan 
Epoch 9 completed out of 10 loss nan 
Accuracy: 0.589097

來源

2017-07-10 rmaka

請寫下您的代碼（相關部分），而不是提供鏈接 – DarkCygnus

輸入有男的，由X[np.isnan(X)] = 0修復它。
輸入未縮放，請使用sklearn的StandardScaler來標準化您的輸入。
將權重設置爲一個小的初始值，在random_normal中使用stddev。
修復輸出計算中的錯誤：output = tf.add(tf.matmul(l3, output_layer['weights']),output_layer['biases'])。

來源

2017-07-11 09:53:48

非常感謝！這些步驟使我的準確度從58.4％提高到71.5％！你會有其他建議來進一步提高準確性嗎？ – rmaka

更新你的代碼在上面的鏈接，我可以看看。 –

謝謝，我已經更新了代碼！ – rmaka

損失函數返回nan tensorflow

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