[딥러닝] 딥러닝 활용하기(MNIST 선형회귀)

MNIST 선형 회귀

import tensorflow as tf

mnist = tf.keras.datasets.mnist

(X, YT), (x, yt) = mnist.load_data() #60000개의 학습데이터, 10000개의 검증데이터

X, x = X/255, x/255 # 60000x28x28, 10000x28x28
X, x = X.reshape((60000,784)), x.reshape((10000,784))

model = tf.keras.Sequential([
    tf.keras.Input(shape=(784,)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(256, activation='relu'), # 은닉화
    tf.keras.layers.Dense(128, activation='relu'), # 은닉화
    tf.keras.layers.Dense(1, activation='linear')
]) # 신경망 모양 결정(W, B 내부적 준비)

model.compile(optimizer='adam',
              loss='mse')

model.fit(X,YT,epochs=20)

y=model.predict(x)
print(y[0])

import matplotlib.pyplot as plt

x=x.reshape(10000,28,28)

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.imshow(x[i], cmap=plt.cm.binary)
    plt.xlabel(f'y{y[i]} yt{yt[i]}')
plt.show()