Keras 序列模型训练的 Nan 损失

Question

I have a Tensorflow Sequential Network which is returning a loss value of Nan consistently during training. I am using pandas and keras.

An example of the data is:

Actual_GP1  Budgeted_GP_Value_Cleanup  Budgeted_GP_Value_New  \
0          2.0                        2.0                  95.00   
1          2.0                        2.0               63684.55   
3          2.0                        2.0               26022.57   
4          2.0                        2.0              440759.17   
6          2.0                        2.0                  95.00   
7          2.0                        2.0             3519120.00   
9          2.0                        2.0                   4.00   
12         2.0                        2.0                   4.00   
13         2.0                        2.0              355960.00   
14         2.0                        2.0               62745.00  



Costing_Date  Created_Time  Date_Time_16  Delivery_Date  Engineering_Date  \
0              4      1.579523           4.0            4.0                 4   
1              4      1.575390           4.0            4.0                 4   
3              4      1.575471           4.0            4.0                 4   
4              4      1.575020           4.0            4.0                 4   
6              4      1.579508           4.0            4.0                 4   
7              4      1.578304           4.0            4.0                 4   
9              4      1.574600           4.0            4.0                 4   
12             4      1.570805           4.0            4.0                 4   
13             4      1.573831           4.0            4.0                 4   
14             4      1.576153           4.0            4.0                 4   

Exchange_Rate     GP  ...  Last_Activity_Time  Modified_Time  \
0             2.0  100.0  ...            4.000000       1.579523   
1             2.0   30.0  ...            1.579519       1.579519   
3             2.0   44.0  ...            1.579516       1.579516   
4             2.0   37.0  ...            1.579516       1.579516   
6             2.0  100.0  ...            4.000000       1.579508   
7             2.0   44.0  ...            1.579507       1.579507   
9             2.0  100.0  ...            1.579506       1.579506   
12            2.0   32.0  ...            1.579506       1.579506   
13            2.0   44.0  ...            1.579506       1.579506   
14            2.0   44.5  ...            1.579506       1.579506   

Next_step_actioned_by   PO_Date   PO_Week  Production_End_Date  \
0                     4.0  1.580429  4.000000                    4   
1                     4.0  1.579824  1.579478                    4   
3                     4.0  1.575850  1.575850                    4   
4                     4.0  1.575418  1.575245                    4   
6                     4.0  1.580429  4.000000                    4   
7                     4.0  1.583798  1.583798                    4   
9                     4.0  1.579219  1.578874                    4   
12                    4.0  1.580429  1.580083                    4   
13                    4.0  1.585613  1.585526                    4   
14                    4.0  1.580429  1.580083                    4   

Production_Start_Date  Project_Value  Prototype_Date  \
0                       4          95.00               4   
1                       4      212281.82               4   
3                       4           3.00               4   
4                       4           4.00               4   
6                       4          95.00               4   
7                       4     7998000.00               4   
9                       4           4.00               4   
12                      4           4.00               4   
13                      4      809000.00               4   
14                      4      141000.00               4   

Revenue_Forecast_Probability_Weighting  
0                                      1.0  
1                                      2.0  
3                                      3.0  
4                                      4.0  
6                                      1.0  
7                                      5.0  
9                                      4.0  
12                                     4.0  
13                                     7.0  
14                                     8.0  

I understand some of the dates in this sample are categorically labelled, but that is due to missing values. The target value for this model is a probability of success, which is based on historical data, and i have left that out of this question. It's a value [0,100]. and the network configuration is:

dataset=tf.data.Dataset.from_tensor_slices((df.values, target.values))
train_dataset=dataset.shuffle(len(df)).batch(1)
print(df.shape)
def get_compiled_model():
    model = tf.keras.Sequential([
    tf.keras.layers.Dense(24, activation='relu', input_shape=(df.shape[-1],)),
    tf.keras.layers.Dense(16, activation='relu'),
    tf.keras.layers.Dense(8, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
    ])


    model.compile(optimizer='adam', loss='mse',metrics=['accuracy'])

    return model
model=get_compiled_model()
model.fit(train_dataset, epochs=20)
model.save("keras_saved_model.h5")

with an output of

(574, 24)
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Train on 574 steps

Epoch 1/20
574/574 [==============================] - 2s 3ms/step - loss: nan - acc: 0.3275
Epoch 2/20
574/574 [==============================] - 1s 1ms/step - loss: nan - acc: 0.6655
Epoch 3/20
574/574 [==============================] - 1s 1ms/step - loss: nan - acc: 0.6655
Epoch 4/20
574/574 [==============================] - 1s 1ms/step - loss: nan - acc: 0.6655
Epoch 5/20
574/574 [==============================] - 1s 1ms/step - loss: nan - acc: 0.6655
Epoch 7/20
574/574 [==============================] - 1s 1ms/step - loss: nan - acc: 0.6655
and so on. 

Could someone please point me in the right direction regarding this consistent accuracy and these null loss values.

EDIT: The solution was to divide the target value by 100 so it would fit in the range [0,1], since the final activation layer is a sigmoid function. Thanks to Matias Valdenegro for pointing this out

Answer 1

Providing answer here for the community even if the answer is provided in the comment section.

Since the target value ranges from [0,100] the user has normalized the value by diving it by 100, and used the sigmoid activation function, which resolved the issue.
You can apply the normalize function for a feature using the below code.

To get min and max value of a numerical column:

def _z_score_params(column):
    mean = traindf[column].min()
    std = traindf[column].max()
    return {'min': min, 'max': max}


def zscore(col):

  min_value = _z_score_params(col)[min]
  max_value = _z_score_params(col)[max]
  return (col - min_value)/max_value 

feature_name = ‘column_name_to_normalize’
normalized_feature = tf.feature_column.numeric_column(
  feature_name,
  normalizer_fn=zscore)