12.Multi-Classification 실습

local에서 테스트 할려고 하니까 안되네..

 

google colab 에서 해야함..

 

 

 

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt


fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()


plt.figure(figsize=(10,10))
for i in range(25):
  plt.subplot(5,5,i+1)
  plt.xticks([])
  plt.yticks([])
  plt.grid(False)
  plt.imshow(train_images[i], cmap=plt.cm.binary)
  plt.xlabel(class_names[train_labels[i]])
plt.show()

train_images = train_images / 255.0
test_images = test_images / 255.0

model = keras.Sequential([
                          keras.layers.Flatten(input_shape=(28,28)),
                          keras.layers.Dense(128, activation='relu'),
                          keras.layers.Dense(10)

])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])


model.fit(train_images, train_labels, epochs=10)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print('Test accuracy:', test_acc)

probability_model = tf.keras.Sequential([model,
                                         tf.keras.layers.Softmax()])
predications = probability_model.predict(test_images)
predications[0]

print(np.argmax(predications[0]))
print(test_labels[0])