尝试使用 tensorflow 特征列时损失 nan

Question

我有this dataframe。

我正在尝试关注this example。

我要预测的目标值是zg500。我想使用的另一个功能是tas。

我想创建特征列，以结合经纬度：

import numpy as np
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import feature_column

df = pd.read_csv('./df.csv')
# if unamed column exists
#df.drop(['Unnamed: 0'],
#          axis=1,
#          inplace=True)

df.dropna(inplace=True)

# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
  dataframe = dataframe.copy()
  labels = dataframe.pop('zg500')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  return ds

batch_size = 16 
train_ds = df_to_dataset(df, batch_size=batch_size)

feature_columns = []
tas = feature_column.numeric_column("tas")
latitude = feature_column.numeric_column("lats")
longitude = feature_column.numeric_column("lons")
bucketized_lat = feature_column.bucketized_column(latitude, boundaries=[0, 20, 40, 70])
bucketized_lon = feature_column.bucketized_column(longitude, boundaries=[-45, -20, 0, 20, 60])

feature_columns.append(tas)
feature_columns.append(bucketized_lat)
feature_columns.append(bucketized_lon)
lat_lon = feature_column.crossed_column([bucketized_lat, bucketized_lon], 1000)
lat_lon = feature_column.indicator_column(lat_lon)
feature_columns.append(lat_lon)

feature_layer = tf.keras.layers.DenseFeatures(feature_columns)

创建模型：

model = tf.keras.Sequential([
  feature_layer,
  tf.keras.layers.Dense(10, activation='relu'),
  tf.keras.layers.Dense(1)
])


model.compile(optimizer='adam',
              loss='mse')

 
history = model.fit(train_ds, epochs=2)

现在，我收到了 nan loss：

10918/10918 [==============================] - 10s 861us/step - loss: nan
Epoch 2/2
10918/10918 [==============================] - 10s 857us/step - loss: nan

另外，我想知道为什么使用df 数据框而不是train_ds：

history = model.fit(df.iloc[:, [0, 2, 3]].values,
                    df.iloc[:, 1].values,
                    epochs=2)

产生：

  ValueError: ('We expected a dictionary here. Instead we got: ', <tf.Tensor 'IteratorGetNext:0' shape=(32, 3) dtype=float32>)

Answer 1

在损失中获得nan 的原因是您的目标值处于极端状态。它们的范围从 e^-32 到 e^31。你可以很容易地看到这一点。

df['zg500']
'''
0      -3.996248e-29
1       2.476790e+11
2      -1.010202e+08
3      -1.407987e-02
4       2.240596e-32
            ...     
1742   -1.682389e+11
1743   -4.802401e+00
1744   -3.480795e+31
1745    1.026754e+21
1746    1.790822e+23
Name: zg500, Length: 1739, dtype: float64
'''

我们缩放目标的解决方法。虽然不建议这样做，但我们别无选择。下面是使用Standard Scaler 来缩放目标的轻微修改。

ss = StandardScaler()
# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
    dataframe = dataframe.copy()
    labels = ss.fit_transform(dataframe['zg500'].values.reshape(-1,1))
    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
    if shuffle:
        ds = ds.shuffle(buffer_size=len(dataframe))
    ds = ds.batch(batch_size)
    return ds

完成此操作后，以下是训练模型的结果。

history = model.fit(train_ds, epochs=2)
'''
Consider rewriting this model with the Functional API.
109/109 [==============================] - 1s 804us/step - loss: 27.0520
Epoch 2/10
109/109 [==============================] - 0s 769us/step - loss: 1.0166
Epoch 3/10
109/109 [==============================] - 0s 753us/step - loss: 1.0148
Epoch 4/10
109/109 [==============================] - 0s 779us/step - loss: 1.0115
Epoch 5/10
109/109 [==============================] - 0s 775us/step - loss: 1.0107
Epoch 6/10
109/109 [==============================] - 0s 915us/step - loss: 1.0107
Epoch 7/10
109/109 [==============================] - 0s 1ms/step - loss: 1.0034
Epoch 8/10
109/109 [==============================] - 0s 784us/step - loss: 1.0092
Epoch 9/10
109/109 [==============================] - 0s 735us/step - loss: 1.0151
Epoch 10/10
109/109 [==============================] - 0s 803us/step - loss: 1.0105
'''