[텐서플로2] 회귀분석 예제 regression.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
class SeqModel:
    def __init__(self):
        self.model = None
 
    @staticmethod
    @tf.function
    def simple_func():
        a = tf.constant(1)
        b = tf.constant(2)
        c = tf.constant(3)
        z = a + b + c
        return z
 
    def create_model(self):
        input = tf.keras.Input(shape=(1,))
        output = tf.keras.layers.Dense(1)(input)
        self.model = tf.keras.Model(input, output)
        """
        Total params: 2
        Trainable params: 2
        Non-trainable params: 0
        """
        print(self.model.summary())
 
    @staticmethod
    def make_random_data():
        x = np.random.uniform(low = -2, high= 2, size = 200)
        y = []
        for t in x:
            r = np.random.normal(loc = 0.0,
                                 scale=(0.5 + t*t/3),
                                 size = None)
            y.append(r)
        return x, 1.726*x - 0.84 + np.array(y)
 
    def execute(self):
        (x, y) = self.make_random_data()
        x_train, y_train = x[:150], y[:150]
        x_test, y_test = x[:150], y[:150]
        self.model = tf.keras.Sequential()
        self.model.add(tf.keras.layers.Dense(units=1, input_dim=1))
        self.model.compile(optimizer='sgd', loss='mse')
        self.model.save('./data/simple_model.h5')
 
    def load_model(self):
        (x, y) = self.make_random_data()
        x_train, y_train = x[:150], y[:150]
        x_train, y_train = x[:150], y[:150]
        load_model = tf.keras.models.load_model('./data/simple_model.h5')
        history = load_model.fit(x_train, y_train, epochs = 300, validation_split = 0.3)
        epochs = np.arange(1, 300 +1)
        plt.plot(epochs, history.history['loss'], label = 'Training loss')
        plt.xlabel('Epoch')
        plt.ylabel('Loss')
        plt.legend()
        plt.show()
 
 
if __name__ == '__main__':
    m = SeqModel()
    m.create_model()
    m.execute()
    m.load_model()