import tensorflow as tf
import pandas as pd
import matplotlib.pylab as plt
print('Tensorflow Version: {}'.format(tf.__version__))
data = pd.read_csv(r'C:\Users\lihuanyu\Desktop\数据集\Income1.csv')
plt.scatter(data.Education, data.Income)
plt.show()
data.head()

data1 =  data.to_numpy()
x = data1[:,1]
y = data1[:,2]
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
#查看模型
model.summary()
model.compile(optimizer='adam',loss="mse")

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 1)                 2         
=================================================================
Total params: 2
Trainable params: 2
Non-trainable params: 0
_________________________________________________________________

import tensorflow as tf
print('Tensorflow Version: {}'.format(tf.__version__))
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_csv(r'C:\Users\lihuanyu\Desktop\数据集\Advertising.csv')   
data1 = data.to_numpy()
data.head()

Tensorflow Version: 2.0.0

x = data.iloc[:,1:-1].to_numpy()
y = data.iloc[:,-1].to_numpy()
model = tf.keras.Sequential([tf.keras.layers.Dense(10,input_shape=(3,),activation="relu"),
                             tf.keras.layers.Dense(1)]
                           )
model.compile(optimizer="adam",loss="mse")
model.summary()
model.fit(x,y,epochs=100)

Model: "sequential_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_10 (Dense)             (None, 10)                40        
_________________________________________________________________
dense_11 (Dense)             (None, 1)                 11        
=================================================================
Total params: 51
Trainable params: 51
Non-trainable params: 0
_________________________________________________________________
Train on 200 samples
Epoch 1/100
200/200 [==============================] - 0s 2ms/sample - loss: 759.2462
.......省略
200/200 [==============================] - 0s 122us/sample - loss: 2.1529
Epoch 97/100
200/200 [==============================] - 0s 130us/sample - loss: 2.1491
Epoch 98/100
200/200 [==============================] - 0s 100us/sample - loss: 2.1935
Epoch 99/100
200/200 [==============================] - 0s 170us/sample - loss: 2.1596
Epoch 100/100
200/200 [==============================] - 0s 145us/sample - loss: 2.1212

print(model.predict(x[0:5,:]),"\n",y[0:5].reshape(-1,1))

[[21.708485 ]
 [11.457169 ]
 [ 9.0395775]
 [18.80669  ]
 [12.999142 ]] 
 [[22.1]
 [10.4]
 [ 9.3]
 [18.5]
 [12.9]]
# 3 . softmax多分类任务