Tensorflow(3)

Chapter 4 딥러닝을 이용한 MNIST

MNIST

• 각 픽셀에서 255값이 최댓값이라 0~1사이 값으로 조정
  

import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(1000, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
              
model.summary()             

import time

start_time = time.time()
hist = model.fit(x_train, y_train, epochs=10,
                 batch_size=100,
                 validation_data=(x_test, y_test))

print("--- %s seconds ---" % (time.time() - start_time))

import matplotlib.pyplot as plt

plot_target = ['loss', 'val_loss', 'accuracy', 'val_accuracy']

plt.figure(figsize=(12,8))

for each in plot_target:
    plt.plot(hist.history[each], label=each)

plt.legend()
plt.grid()
plt.show()

• 틀린 내용 확인

import numpy as np

predicted_result = model.predict(x_test)
predicted_labels = np.argmax(predicted_result, axis=1)
predicted_labels[:10]

wrong_result = []

for n in range(0, len(y_test)):
    if predicted_labels[n] != y_test[n]:
        wrong_result.append(n)

len(wrong_result)

import random

samples = random.choices(population=wrong_result, k=16)
samples

plt.figure(figsize=(12,8))

for index, n in enumerate(samples):
    plt.subplot(4, 4, index+1)
    plt.imshow(x_test[n].reshape(28,28), cmap='Greys')
    plt.title('Label : ' + str(y_test[n]) + ', Prediction : ' + str(predicted_labels[n]))
    plt.axis('off')

plt.show()

MNIST fashion

import tensorflow as tf

fashion_mnist = tf.keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(1000, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

hist = model.fit(x_train, y_train, epochs=10,
                 batch_size=100,
                 validation_data=(x_test, y_test))

import matplotlib.pyplot as plt

plot_target = ['loss', 'val_loss', 'accuracy', 'val_accuracy']

plt.figure(figsize=(12,8))

for each in plot_target:
    plt.plot(hist.history[each], label=each)

plt.legend()
plt.grid()
plt.show()

• 틀린 내용 확인

import numpy as np

predicted_result = model.predict(x_test)
predicted_labels = np.argmax(predicted_result, axis=1)
predicted_labels[:10]

wrong_result = []

for n in range(0, len(y_test)):
    if predicted_labels[n] != y_test[n]:
        wrong_result.append(n)

len(wrong_result)

import random

samples = random.choices(population=wrong_result, k=16)

plt.figure(figsize=(12,8))

for index, n in enumerate(samples):
    plt.subplot(4, 4, index+1)
    plt.imshow(x_test[n].reshape(28,28), cmap='Greys')
    plt.title('Label : ' + str(y_test[n]) + ', Prediction : ' + str(predicted_labels[n]))
    plt.axis('off')

plt.show()

이 글은 제로베이스 데이터 취업 스쿨의 강의 자료 일부를 발췌하여 작성되었습니다