tensorflow 입문 학습(一)

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR) #        
import numpy as np


celsius_q = np.array([-40,-10,0,8,15,22,38], dtype = float)#      
fahrenheit_a = np.array([-40,14,32,46,59,72,100], dtype = float)#      

for i, c in enumerate(celsius_q):
    print("{} degress Celsius = {} degrees Fahrenhet".format(c, fahrenheit_a[i]))
    
``

  ：
-40.0 degress Celsius = -40.0 degrees Fahrenhet
-10.0 degress Celsius = 14.0 degrees Fahrenhet
0.0 degress Celsius = 32.0 degrees Fahrenhet
8.0 degress Celsius = 46.0 degrees Fahrenhet
15.0 degress Celsius = 59.0 degrees Fahrenhet
22.0 degress Celsius = 72.0 degrees Fahrenhet
38.0 degress Celsius = 100.0 degrees Fahrenhet

    ：

l0 = tf.keras.layers.Dense(units = 1, input_shape=[1])
model = tf.keras.Sequential([l0])

model.compile(loss = 'mean_squared_error',optimizer = tf.keras.optimizers.Adam(0.1))

history = model.fit(celsius_q, fahrenheit_a, epochs = 500, verbose = False)
print("Finshed training the model")

  ：Finshed training the model


import matplotlib.pyplot as plt
plt.xlabel("Epoch   Number")
plt.ylabel("Loss Magnitude")
plt.plot(history.history["loss"])
plt.show()

![         ](https://img-blog.csdnimg.cn/20190314152345260.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3hpYW5namlhb3Bpbnhpbmc=,size_16,color_FFFFFF,t_70)
print(model.predict([100.0]))

  ：[[211.33841]]


    ：
print(l0.get_weights())
[array([[1.8202447]], dtype=float32), array([29.313948], dtype=float32)]
    ：f = c * 1.8 + 32

hidden  = keras.layers.Dense(units = 2, input_shape=[3])
output = keras.layers.Dense(units = 1)
model = tf.keras.layers.Sequential([hidden, output])

inputs:      
units:      （  )
output:   
hidden：