尝试使用 tensorflow 特征列时损失 nan

Question

我有这个 dataframe 。

我试图按照这个例子。

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，以便结合纬度和经度：

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 model：

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 nan 损失：

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 dataframe 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

损失nan的原因是您的目标值处于极端。 它们的范围从 e^-32 到 e^31。 这个你很容易看出来。

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

这样做之后，下面是训练 model 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
'''