唐宇迪TensorFlow2.0入门到实战：使用TensorFlow预测天气_tensorflow 天气预报

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow.keras
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
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文件下载地址：https://github.com/DevCHyderabad/Data-Science/blob/master/ml_algorithms/randomForrest/temps.csv

features = pd.read_csv('data/temps.csv')

# 在特征中去掉标签
features = features.drop(['forecast_noaa', 'forecast_acc','forecast_under'], axis=1)

# 看看数据长什么样子
features.head()
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数据表中

• year,month,day,week 分别表示具体的时间

• temp_2：前天的最高气温

• temp_1：昨天的最高气温

• average：在历史中，每年这一天的平均最高温度值

• actual：这就是我们的标签值了，当天的真实最高温度

• friend：这一列可能是凑热闹的，你的朋友猜测的可能值，咱们不管它就好

print('数据维度:', features.shape)
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数据维度: (348, 9)
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# 处理时间数据
import datetime

# 分别得到年，月，日
years = features['year']
months = features['month']
days = features['day']
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# datetime 格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)] 

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dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

dates[:5]
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[datetime.datetime(2016, 1, 1, 0, 0),
 datetime.datetime(2016, 1, 2, 0, 0),
 datetime.datetime(2016, 1, 3, 0, 0),
 datetime.datetime(2016, 1, 4, 0, 0),
 datetime.datetime(2016, 1, 5, 0, 0)]
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# 准备画图
# 指定默认风格
plt.style.use('fivethirtyeight')

# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10, 10))
fig.autofmt_xdate(rotation = 45)

# actual标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel('');ax1.set_ylabel('Temperature');ax1.set_title('MAX Temp')

# 昨天的温度
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel('');ax2.set_ylabel('Temperature');ax2.set_title('Previous Max Temp')

# 前天
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date');ax3.set_ylabel('Temperature');ax3.set_title('Two Days Prior Max Temp')

# 我的逗逼朋友
ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date');ax4.set_ylabel('Temperature');ax4.set_title('Friend Estimate')

plt.tight_layout(pad=2)

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# 独热编码
features = pd.get_dummies(features, dtype=int)
features.head(5)
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labels = np.array(features['actual'])

# 在特征中去掉标签
features = features.drop('actual', axis=1)

# 名字单独保存一下，以备后患
feature_list = list(features.columns)

# 转换成合适的格式
features = np.array(features)

features.shape
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(348, 14)
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from sklearn import preprocessing
input_features = preprocessing.StandardScaler().fit_transform(features)
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input_features
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array([[ 0.        , -1.5678393 , -1.65682171, ..., -0.40482045,
        -0.41913682, -0.40482045],
       [ 0.        , -1.5678393 , -1.54267126, ..., -0.40482045,
        -0.41913682, -0.40482045],
       [ 0.        , -1.5678393 , -1.4285208 , ..., -0.40482045,
        -0.41913682, -0.40482045],
       ...,
       [ 0.        ,  1.5810006 ,  1.53939107, ...,  2.47023092,
        -0.41913682, -0.40482045],
       [ 0.        ,  1.5810006 ,  1.65354153, ..., -0.40482045,
        -0.41913682, -0.40482045],
       [ 0.        ,  1.5810006 ,  1.76769198, ..., -0.40482045,
        -0.41913682, -0.40482045]])
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基于Keras构建网络模型

一些常用参数已经列出，如下所示：

• activation：激活函数的选择，一般常用relu

• kernel_initializer, bias_initializer：权重与偏置参数的初始化方法，有时候不收敛换种初始化突然好使了…玄学

• kernel_regularizer, bias_regularizer：要不要加入正则化

• inputs：输入，可以自己制定，也可以让网络自动选

• units：神经元个数

按顺序构造网络模型

model = tf.keras.Sequential()
model.add(layers.Dense(16))
model.add(layers.Dense(32))
model.add(layers.Dense(1))
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compile 相当于对网络进行配置，指定好优化器和损失函数等

# SGD 梯度下降
model.compile(optimizer=tf.keras.optimizers.SGD(0.001),
			loss='mean_squared_error')
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model.fit(input_features, labels, validation_split=0.25, epochs=10, batch_size=64)
# x, y, 验证集占25%, 10轮, 优化器每次迭代64
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Epoch 1/10


[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 42ms/step - loss: 4293.1636 - val_loss: 3535.2651
Epoch 2/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 2143.3159 - val_loss: 3589.7532
Epoch 3/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 238.6194 - val_loss: 2675.4365
Epoch 4/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - loss: 111.1695 - val_loss: 1886.3639
Epoch 5/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 127.0813 - val_loss: 1077.0658
Epoch 6/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - loss: 53.9116 - val_loss: 1039.5237
Epoch 7/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - loss: 96.2071 - val_loss: 680.0672
Epoch 8/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 41.3098 - val_loss: 453.6753
Epoch 9/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 94.2487 - val_loss: 455.6355
Epoch 10/10
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - loss: 67.2740 - val_loss: 264.8182





<keras.src.callbacks.history.History at 0x123d1362850>
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似乎存在一些问题，模型还没有完全收敛，能不能调些参数呢

model.summary()
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Model: "sequential_23"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_69 (Dense)                     │ (None, 16)                  │             240 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_70 (Dense)                     │ (None, 32)                  │             544 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_71 (Dense)                     │ (None, 1)                   │              33 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 819 (3.20 KB)

 Trainable params: 817 (3.19 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 2 (12.00 B)

input_features.shape
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(348, 14)
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更改初始化方法后

model = tf.keras.Sequential()
model.add(layers.Dense(16, kernel_initializer='random_normal'))
model.add(layers.Dense(32, kernel_initializer='random_normal'))
model.add(layers.Dense(1, kernel_initializer='random_normal'))
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model.compile(optimizer=tf.keras.optimizers.SGD(0.001),
			loss='mean_squared_error')
model.fit(input_features, labels, validation_split=0.25, epochs=100, batch_size=64)	
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Epoch 1/100


[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - loss: 4368.7993 - val_loss: 2869.5989
Epoch 2/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 4293.7646 - val_loss: 2786.7593
Epoch 3/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 4165.1357 - val_loss: 2670.0818
Epoch 4/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 3991.5771 - val_loss: 2449.0737
Epoch 5/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 3592.8672 - val_loss: 2002.1527
Epoch 6/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 1123.8270 - val_loss: 1296.3181
Epoch 7/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 61.2757 - val_loss: 1059.9176
Epoch 8/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 163.2804 - val_loss: 620.4620
Epoch 9/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 44.6145 - val_loss: 393.5599
Epoch 10/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 74.9996 - val_loss: 396.7385
Epoch 11/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 33.9426 - val_loss: 307.0163
Epoch 12/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 65.3238 - val_loss: 281.3852
Epoch 13/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 46.6259 - val_loss: 240.9706
Epoch 14/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 33.1187 - val_loss: 194.7957
Epoch 15/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 26.4251 - val_loss: 134.7485
Epoch 16/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 39.3029 - val_loss: 148.6135
Epoch 17/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 28.9137 - val_loss: 192.1431
Epoch 18/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 32.8826 - val_loss: 174.5544
Epoch 19/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 62.2047 - val_loss: 113.8408
Epoch 20/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 32.4159 - val_loss: 101.6268
Epoch 21/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 44.8149 - val_loss: 138.4605
Epoch 22/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 36.1919 - val_loss: 101.1899
Epoch 23/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 33.6516 - val_loss: 96.3278
Epoch 24/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 24.6191 - val_loss: 78.0890
Epoch 25/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 23.6171 - val_loss: 127.2975
Epoch 26/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 78.7954 - val_loss: 50.2254
Epoch 27/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.7817 - val_loss: 50.6552
Epoch 28/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 25.3836 - val_loss: 57.6026
Epoch 29/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 26.3525 - val_loss: 58.6611
Epoch 30/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 34.0787 - val_loss: 61.2929
Epoch 31/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 34.7299 - val_loss: 85.5075
Epoch 32/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 66.5403 - val_loss: 74.8792
Epoch 33/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 44.8056 - val_loss: 57.6639
Epoch 34/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 27.2398 - val_loss: 58.2288
Epoch 35/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 31.8331 - val_loss: 37.7633
Epoch 36/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.2777 - val_loss: 45.4487
Epoch 37/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 22.2437 - val_loss: 51.2756
Epoch 38/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 36.4086 - val_loss: 66.9983
Epoch 39/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 52.4292 - val_loss: 53.2812
Epoch 40/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 29.6176 - val_loss: 38.6083
Epoch 41/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 25.4210 - val_loss: 48.8720
Epoch 42/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 30.1500 - val_loss: 51.2028
Epoch 43/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 38.0579 - val_loss: 39.8835
Epoch 44/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.5666 - val_loss: 64.8734
Epoch 45/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - loss: 35.3083 - val_loss: 45.6486
Epoch 46/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 40.7417 - val_loss: 45.9886
Epoch 47/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - loss: 26.1866 - val_loss: 51.1810
Epoch 48/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 27.6621 - val_loss: 69.8772
Epoch 49/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 51.3823 - val_loss: 42.5597
Epoch 50/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 26.9525 - val_loss: 42.2013
Epoch 51/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.2767 - val_loss: 72.3962
Epoch 52/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 97.8388 - val_loss: 56.8788
Epoch 53/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 33.3245 - val_loss: 41.7663
Epoch 54/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.4839 - val_loss: 50.8914
Epoch 55/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 24.8976 - val_loss: 55.3096
Epoch 56/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 31.3844 - val_loss: 40.7323
Epoch 57/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 25.6870 - val_loss: 39.5035
Epoch 58/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 35.0476 - val_loss: 40.1320
Epoch 59/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 24.2658 - val_loss: 47.4662
Epoch 60/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 25.1361 - val_loss: 41.5076
Epoch 61/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 23.7325 - val_loss: 46.9274
Epoch 62/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - loss: 24.9606 - val_loss: 45.6629
Epoch 63/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 30.2027 - val_loss: 42.5596
Epoch 64/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 30.4292 - val_loss: 42.9390
Epoch 65/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 25.3275 - val_loss: 37.7184
Epoch 66/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 32.9851 - val_loss: 78.8565
Epoch 67/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 57.0028 - val_loss: 64.6032
Epoch 68/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 39.3346 - val_loss: 59.4840
Epoch 69/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - loss: 49.9364 - val_loss: 40.7967
Epoch 70/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.4895 - val_loss: 39.4774
Epoch 71/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 34.0240 - val_loss: 46.4978
Epoch 72/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 35.7356 - val_loss: 40.4752
Epoch 73/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 33.3258 - val_loss: 39.4771
Epoch 74/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 26.7905 - val_loss: 40.5044
Epoch 75/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.1334 - val_loss: 58.0956
Epoch 76/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 48.5187 - val_loss: 41.4135
Epoch 77/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 31.2629 - val_loss: 37.7269
Epoch 78/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 24.1390 - val_loss: 35.2987
Epoch 79/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 26.4805 - val_loss: 42.8379
Epoch 80/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.8565 - val_loss: 37.6946
Epoch 81/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 40.4653 - val_loss: 36.8633
Epoch 82/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.7675 - val_loss: 47.5671
Epoch 83/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 41.8275 - val_loss: 44.3226
Epoch 84/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - loss: 28.0287 - val_loss: 38.9376
Epoch 85/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 25.5245 - val_loss: 42.3016
Epoch 86/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 27.0931 - val_loss: 39.9974
Epoch 87/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.4755 - val_loss: 39.8841
Epoch 88/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 28.4916 - val_loss: 41.0811
Epoch 89/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - loss: 28.7822 - val_loss: 51.6084
Epoch 90/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 35.9541 - val_loss: 43.7493
Epoch 91/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 31.2829 - val_loss: 42.2934
Epoch 92/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 35.1214 - val_loss: 64.9260
Epoch 93/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 84.9164 - val_loss: 38.1734
Epoch 94/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 23.1115 - val_loss: 57.1945
Epoch 95/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 62.6148 - val_loss: 35.5297
Epoch 96/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 22.2531 - val_loss: 45.4069
Epoch 97/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 42.2842 - val_loss: 44.1553
Epoch 98/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.4819 - val_loss: 37.9086
Epoch 99/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.6191 - val_loss: 45.0827
Epoch 100/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 31.7428 - val_loss: 36.7290





<keras.src.callbacks.history.History at 0x123d15d3190>
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加入正则化惩罚项

model = tf.keras.Sequential()
model.add(layers.Dense(16, kernel_initializer='random_normal',
                        kernel_regularizer=tf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(32, kernel_initializer='random_normal', 
                        kernel_regularizer=tf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(1, kernel_initializer='random_normal', 
                        kernel_regularizer=tf.keras.regularizers.l2(0.03)))

model.compile(optimizer=tf.keras.optimizers.SGD(0.001),
			loss='mean_squared_error')
model.fit(input_features, labels, validation_split=0.25, epochs=100, batch_size=64)	
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Epoch 1/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - loss: 4416.4448 - val_loss: 2870.6919
Epoch 2/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 4363.5566 - val_loss: 2788.0947
Epoch 3/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 4203.2598 - val_loss: 2672.4597
Epoch 4/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - loss: 3951.0793 - val_loss: 2473.7844
Epoch 5/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - loss: 2440.8638 - val_loss: 2167.3281
Epoch 6/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - loss: 98.5526 - val_loss: 1579.0359
Epoch 7/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 60.8955 - val_loss: 1240.2740
Epoch 8/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 79.9701 - val_loss: 1118.0626
Epoch 9/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - loss: 86.3771 - val_loss: 860.3555
Epoch 10/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 42.9139 - val_loss: 657.6273
Epoch 11/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 69.9081 - val_loss: 645.1999
Epoch 12/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - loss: 38.9944 - val_loss: 455.9460
Epoch 13/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 40.6661 - val_loss: 432.8222
Epoch 14/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 32.3687 - val_loss: 329.2526
Epoch 15/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 42.3445 - val_loss: 405.3529
Epoch 16/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 38.2314 - val_loss: 240.9814
Epoch 17/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 28.9866 - val_loss: 183.0723
Epoch 18/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - loss: 41.5155 - val_loss: 213.0789
Epoch 19/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 38.2092 - val_loss: 185.2979
Epoch 20/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - loss: 27.5062 - val_loss: 155.2072
Epoch 21/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 37.3787 - val_loss: 127.9377
Epoch 22/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 29.8084 - val_loss: 105.5078
Epoch 23/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 33.5834 - val_loss: 150.7845
Epoch 24/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 63.8227 - val_loss: 119.4120
Epoch 25/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 32.2373 - val_loss: 117.0641
Epoch 26/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.9073 - val_loss: 94.2724
Epoch 27/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 22.5740 - val_loss: 82.7034
Epoch 28/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 42.6764 - val_loss: 129.7826
Epoch 29/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 55.9826 - val_loss: 69.3660
Epoch 30/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.4297 - val_loss: 101.5603
Epoch 31/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 153.8240 - val_loss: 101.3329
Epoch 32/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 40.8292 - val_loss: 51.9974
Epoch 33/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 31.8792 - val_loss: 90.1593
Epoch 34/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 42.8228 - val_loss: 53.3058
Epoch 35/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - loss: 32.8746 - val_loss: 111.8890
Epoch 36/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - loss: 133.2832 - val_loss: 56.0949
Epoch 37/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 25.2988 - val_loss: 52.5813
Epoch 38/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 37.2227 - val_loss: 52.4974
Epoch 39/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - loss: 27.8274 - val_loss: 65.4723
Epoch 40/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 40.0796 - val_loss: 48.4441
Epoch 41/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - loss: 42.1947 - val_loss: 53.0788
Epoch 42/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 64.7438 - val_loss: 42.7688
Epoch 43/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 27.6699 - val_loss: 59.0437
Epoch 44/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 57.7695 - val_loss: 39.4156
Epoch 45/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 33.5784 - val_loss: 47.0080
Epoch 46/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 34.2292 - val_loss: 59.5289
Epoch 47/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 45.7918 - val_loss: 63.5306
Epoch 48/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 78.9908 - val_loss: 52.0136
Epoch 49/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 26.5390 - val_loss: 52.5462
Epoch 50/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 31.1325 - val_loss: 49.7524
Epoch 51/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 36.4966 - val_loss: 45.8742
Epoch 52/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 25.2637 - val_loss: 44.5731
Epoch 53/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - loss: 36.4297 - val_loss: 45.4402
Epoch 54/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 39.1696 - val_loss: 59.9441
Epoch 55/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 30.6268 - val_loss: 41.0833
Epoch 56/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 23.9201 - val_loss: 45.8624
Epoch 57/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 44.8162 - val_loss: 52.6416
Epoch 58/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 37.1201 - val_loss: 39.6247
Epoch 59/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 26.1614 - val_loss: 42.8098
Epoch 60/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 27.1879 - val_loss: 53.2812
Epoch 61/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 47.2898 - val_loss: 37.5520
Epoch 62/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.9763 - val_loss: 42.5022
Epoch 63/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 29.7016 - val_loss: 45.5520
Epoch 64/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 30.4745 - val_loss: 47.6255
Epoch 65/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 37.4186 - val_loss: 41.0830
Epoch 66/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 25.5378 - val_loss: 40.4347
Epoch 67/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 34.4923 - val_loss: 40.3242
Epoch 68/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 26.5409 - val_loss: 46.0373
Epoch 69/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 25.7036 - val_loss: 43.5233
Epoch 70/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - loss: 33.9939 - val_loss: 44.7467
Epoch 71/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 30.3728 - val_loss: 93.4269
Epoch 72/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 97.9213 - val_loss: 48.0735
Epoch 73/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 30.2927 - val_loss: 43.5673
Epoch 74/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 35.4804 - val_loss: 50.1731
Epoch 75/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 41.3685 - val_loss: 37.4454
Epoch 76/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 31.8862 - val_loss: 35.4503
Epoch 77/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 26.1613 - val_loss: 35.9018
Epoch 78/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 23.0868 - val_loss: 43.0509
Epoch 79/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 32.4755 - val_loss: 36.5396
Epoch 80/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 26.7133 - val_loss: 41.4007
Epoch 81/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 32.3298 - val_loss: 42.8944
Epoch 82/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 38.3876 - val_loss: 37.2009
Epoch 83/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 26.8752 - val_loss: 42.3623
Epoch 84/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 29.6118 - val_loss: 40.8342
Epoch 85/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - loss: 28.6810 - val_loss: 49.7644
Epoch 86/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 34.0479 - val_loss: 45.3099
Epoch 87/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 35.0423 - val_loss: 43.2801
Epoch 88/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 30.1759 - val_loss: 38.7874
Epoch 89/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 23.5527 - val_loss: 42.6318
Epoch 90/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - loss: 31.6622 - val_loss: 39.5522
Epoch 91/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 29.0962 - val_loss: 95.1242
Epoch 92/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 88.2771 - val_loss: 92.6585
Epoch 93/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - loss: 114.2095 - val_loss: 62.0893
Epoch 94/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 36.6870 - val_loss: 48.3573
Epoch 95/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 36.5325 - val_loss: 64.9291
Epoch 96/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 41.5538 - val_loss: 46.8775
Epoch 97/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 28.9018 - val_loss: 39.1529
Epoch 98/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 24.6989 - val_loss: 43.4444
Epoch 99/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - loss: 30.5935 - val_loss: 45.8457
Epoch 100/100
[1m5/5[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - loss: 30.0764 - val_loss: 41.4303





<keras.src.callbacks.history.History at 0x123dd432410>
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加入正则项，可以使W更加平滑

预测模型结果

predict = model.predict(input_features)

predict.shape
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[1m11/11[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step 





(348, 1)
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测试结果并进行展示

# 转换日期
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)] 
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

# 创建一个表格来存日期和其对应的标签数值
true_data = pd.DataFrame(data = {'date':dates, 'actual':labels})

# 同理，再创建一个来存日期和其对应的模型预测值
months = features[:, feature_list.index('month')]
days = features[:, feature_list.index('day')]
year = features[:, feature_list.index('year')]

test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)] 
test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]
predictions_data = pd.DataFrame(data = {'date':test_dates, 'prediction':predict.reshape(-1)})

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# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label='actual')

# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label ='prediction')
plt.xticks(rotation = 45)
plt.legend()

# 图名
plt.xlabel('Date');plt.ylabel('Maximum Temperature (F)');plt.title('Actual and Predicted Values');
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