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我有一个包含1700万观察值的数据集，我试图用它来训练DNNRegressor模型。但是，培训根本不起作用。损失大约10^15，这真是令人震惊。我已经尝试了几个星期的不同事情，无论我做什么，我都无法承受损失。TensorFlow培训不起作用：模型没有学习数据
例如，训练后我进行测试predition与用于训练数据相同的观察结果中的一个。预期的结果是140944.00，但预测产品-169532.5，这是很荒谬的。训练数据中甚至没有任何负面的价值，我不明白它如何可以如此。
下面是一些示例训练数据：
Amount Contribution ServiceType Percentile Time Result
214871.00 3501.00 SM23 high 50 17807828.00
214871.00 3501.00 SM23 high 51 19216520.00
214871.00 3501.00 SM23 high 52 19676064.00
214871.00 3501.00 SM23 high 53 21038840.00
214871.00 3501.00 SM23 high 54 22248295.00
214871.00 3501.00 SM23 high 55 22412713.00
28006.00 83.00 SM0 i_low 0 28006.00
28006.00 83.00 SM0 i_low 1 28804.00
28006.00 83.00 SM0 i_low 2 30140.00
28006.00 83.00 SM0 i_low 3 31598.00
28006.00 83.00 SM0 i_low 4 33130.00
28006.00 83.00 SM0 i_low 5 34663.00
这是我的代码：
feature_columns = [
tf.feature_column.numeric_column('Amount', dtype=dtypes.float32),
tf.feature_column.numeric_column('Contribution', dtype=dtypes.float32),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
'ServiceType',
[
'SM0', 'SM1', 'SM2', 'SM3',
'SM4', 'SM5', 'SM6', 'SM7',
'SM8', 'SM9', 'SM10', 'SM11',
'SM12', 'SM13', 'SM14', 'SM15',
'SM16', 'SM17', 'SM18', 'SM19',
'SM20', 'SM21', 'SM22', 'SM23'
],
dtype=dtypes.string
),
dimension=16
),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
'Percentile',
['i_low', 'low', 'mid', 'high'],
dtype=dtypes.string
),
dimension=16
),
tf.feature_column.numeric_column('Time', dtype=dtypes.int8)
]
model = tf.estimator.DNNRegressor(
hidden_units=[64, 32],
feature_columns=feature_columns,
model_dir=os.getcwd() + "\job",
label_dimension=1,
weight_column=None,
optimizer='Adagrad',
activation_fn=tf.nn.elu,
dropout=None,
input_layer_partitioner=None,
config=RunConfig(
master=None,
num_cores=4,
log_device_placement=False,
gpu_memory_fraction=1,
tf_random_seed=None,
save_summary_steps=100,
save_checkpoints_secs=0,
save_checkpoints_steps=None,
keep_checkpoint_max=5,
keep_checkpoint_every_n_hours=10000,
log_step_count_steps=100,
evaluation_master='',
model_dir=os.getcwd() + "\job",
session_config=None
)
)
print('Training...')
model.train(input_fn=get_input_fn('train'), steps=100000)
print('Evaluating...')
model.evaluate(input_fn=get_input_fn('test'), steps=4000)
print('Predicting...')
prediction = model.predict(input_fn=get_input_fn('predict'))
print(list(prediction))
的input_fn计算如下：
def split_input():
data = pd.read_csv('C:\\all_data.txt', sep='\t')
x = data.drop('Result', axis=1)
y = data.Result
return train_test_split(x, y, test_size=0.2, random_state=123)
def get_input_fn(input_fn_type):
train_x, test_x, train_y, test_y = split_input()
if input_fn_type == 'train':
return tf.estimator.inputs.pandas_input_fn(
x=train_x,
y=train_y,
num_epochs=None,
shuffle=True
)
elif input_fn_type == 'test':
return tf.estimator.inputs.pandas_input_fn(
x=test_x,
y=test_y,
num_epochs=1,
shuffle=False
)
elif input_fn_type == 'predict':
return tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame(
{
'Amount': 52050.00,
'Contribution': 1394.00,
'ServiceType': 'SM0',
'Percentile': 'i_low',
'Time': 5
},
index=[0]
),
num_epochs=1,
shuffle=False
)
的输出如下：
Training...
INFO:tensorflow:loss = 6.30944e+15, step = 1
INFO:tensorflow:global_step/sec: 457.091
INFO:tensorflow:loss = 3.28245e+15, step = 101 (0.219 sec)
INFO:tensorflow:global_step/sec: 533.271
INFO:tensorflow:loss = 2.65647e+15, step = 201 (0.188 sec)
INFO:tensorflow:global_step/sec: 533.274
...
INFO:tensorflow:loss = 1.06601e+15, step = 99701 (0.203 sec)
INFO:tensorflow:global_step/sec: 533.289
INFO:tensorflow:loss = 2.12652e+15, step = 99801 (0.188 sec)
INFO:tensorflow:global_step/sec: 533.273
INFO:tensorflow:loss = 1.31647e+15, step = 99901 (0.203 sec)
INFO:tensorflow:Saving checkpoints for 100000 into C:\projection_model\job\model.ckpt.
INFO:tensorflow:Loss for final step: 2.88956e+15.
Evaluating...
INFO:tensorflow:Evaluation [1/4000]
INFO:tensorflow:Evaluation [2/4000]
INFO:tensorflow:Evaluation [3/4000]
...
INFO:tensorflow:Evaluation [3998/4000]
INFO:tensorflow:Evaluation [3999/4000]
INFO:tensorflow:Evaluation [4000/4000]
INFO:tensorflow:Finished evaluation at 2017-08-30-19:04:03
INFO:tensorflow:Saving dict for global step 100000: average_loss = 1.37941e+13, global_step = 100000, loss = 1.76565e+15
Predicting...
[{'predictions': array([-169532.5], dtype=float32)}] # Should be somewhere around 140944.00
为什么模型没有学习数据？我试过不同的回归器和输入规范化，没有任何工作。
，应该是比较快一个建议尝试：作为测试，这只是一个测试，请尝试使用每万分之一的数据点，使得数据集大小更小，故障排除相应速度更快。 –