lec04 file읽어서 tf.model에 집어넣기

# Lab 4 Multi-variable linear regression
import tensorflow as tf
import numpy as np

x_data = [[73., 80., 75.],
          [93., 88., 93.],
          [89., 91., 90.],
          [96., 98., 100.],
          [73., 66., 70.]]
y_data = [[152.],
          [185.],
          [180.],
          [196.],
          [142.]]

tf.model = tf.keras.Sequential()

tf.model.add(tf.keras.layers.Dense(units=1, input_dim=3))  # input_dim=3 gives multi-variable regression
tf.model.add(tf.keras.layers.Activation('linear'))  # this line can be omitted, as linear activation is default
# advanced reading https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6

tf.model.compile(loss='mse', optimizer=tf.keras.optimizers.SGD(lr=1e-5)) # 10의 -5승
tf.model.summary()
history = tf.model.fit(x_data, y_data, epochs=100)

y_predict = tf.model.predict(np.array([[72., 93., 90.]]))
print(y_predict)

up  basic code 

 

 

아래는 data-01-test-score.csv

73,80,75,152
93,88,93,185
89,91,90,180
96,98,100,196
73,66,70,142
53,46,55,101
69,74,77,149
47,56,60,115
87,79,90,175
79,70,88,164
69,70,73,141
70,65,74,141
93,95,91,184
79,80,73,152
70,73,78,148
93,89,96,192
78,75,68,147
81,90,93,183
88,92,86,177
78,83,77,159
82,86,90,177
86,82,89,175
78,83,85,175
76,83,71,149
96,93,95,192

 

import tensorflow as tf
import numpy as np

xy = np.loadtxt('./data-01-test-score.csv', delimiter=',', dtype=np.float32)
x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]

# Make sure the shape and data are OK
print(x_data, "\nx_data shape:", x_data.shape)
print(y_data, "\ny_data shape:", y_data.shape)

# data output
'''
[[ 73.  80.  75.]
 [ 93.  88.  93.]
 ...
 [ 76.  83.  71.]
 [ 96.  93.  95.]]
x_data shape: (25, 3)
[[152.]
 [185.]
 ...
 [149.]
 [192.]]
y_data shape: (25, 1)
'''
tf.model = tf.keras.Sequential()
# activation function doesn't have to be added as a separate layer. Add it as an argument of Dense() layer
tf.model.add(tf.keras.layers.Dense(units=1, input_dim=3, activation='linear'))
# tf.model.add(tf.keras.layers.Activation('linear'))
tf.model.summary()

tf.model.compile(loss='mse', optimizer=tf.keras.optimizers.SGD(lr=1e-5))
history = tf.model.fit(x_data, y_data, epochs=2000)

# Ask my score
print("Your score will be ", tf.model.predict([[100, 70, 101]]))
print("Other scores will be ", tf.model.predict([[60, 70, 110], [90, 100, 80]]))

 

Epoch 1998/2000

1/1 [==============================] - 0s 737us/step - loss: 11.8143

Epoch 1999/2000

1/1 [==============================] - 0s 692us/step - loss: 11.8100

Epoch 2000/2000

1/1 [==============================] - 0s 808us/step - loss: 11.8057

Your score will be  [[172.88719]]

Other scores will be  [[179.6662 ]

 [181.46442]]