问损失:为共享单车构建模型时出现nan

EN

import tensorflow as tf
import pandas as pd
# Reading the csv file from local drive as a dataframe
bike_df = pd.read_csv('C:\\Users\\HOME\\MLPythonPractice\\Data sets\\Bike-Sharing-Dataset\\day.csv')
bike_result_df = pd.read_csv('C:\\Users\\HOME\\MLPythonPractice\\Data sets\\Bike-Sharing-Dataset\\day.csv')

# Remove unwanted columns from the data frame
bike_df = bike_df.drop(columns=['instant','dteday','cnt'])
# shape of the dataframe
print(bike_df.shape)
# Exact attribute to see the columns of the dataframe
print(bike_df.columns)
# To know the type 
print(type(bike_df))
# To see the information of the dataframe
print(bike_df.info())
# Converting from dataframe to ndarray
bike_s = bike_df.values
print(type(bike_s))
print(bike_s.shape)
# Remove all the columns except cnt column which is result set
bike_result_df['cnt'] = bike_result_df['cnt'].values.astype(np.float64)  #converting to float
bike_result_df = bike_result_df['cnt']  # Removing all columns except cnt column
bike_result_s = bike_result_df.values   # Converting dataframe to ndarray
print(type(bike_result_s))
print(bike_result_s)
import numpy as np
print(type(bike_df))
print(bike_df.shape)
print(bike_result_df.shape)
#As the data frame is available, we will build the graph using keras (## are part of build graph)

## Initialise the sequential model
model = tf.keras.models.Sequential()
## Normalize the input data by creating a normalisation layer
model.add(tf.keras.layers.BatchNormalization(input_shape = (13,)))
## Add desnse layer for predition -- Keras declares weights and bias - dense(1) 1 here is expected value
model.add(tf.keras.layers.Dense(1))
# Compile the model - add loss and gradient descen optimiser
model.compile(optimizer='sgd',loss='mse')
print(type(bike_s))
print(type(bike_result_s))
print(bike_s.shape)
print(bike_result_s.shape)
print(bike_result_s)
# Execute the graph
model.fit(bike_s,bike_result_s,epochs=10)
model.save('models/bike_sharing_lr.h5')

Epoch 1/10
731/731 [==============================] - 1s 895us/step - loss: nan     
Epoch 2/10
731/731 [==============================] - 0s 44us/step - loss: nan
Epoch 3/10
731/731 [==============================] - 0s 46us/step - loss: nan
Epoch 4/10
731/731 [==============================] - 0s 44us/step - loss: nan
Epoch 5/10
731/731 [==============================] - 0s 39us/step - loss: nan
Epoch 6/10
731/731 [==============================] - 0s 39us/step - loss: nan
Epoch 7/10
731/731 [==============================] - 0s 47us/step - loss: nan
Epoch 8/10
731/731 [==============================] - 0s 40us/step - loss: nan
Epoch 9/10
731/731 [==============================] - 0s 43us/step - loss: nan
Epoch 10/10
731/731 [==============================] - 0s 42us/step - loss: nan