keras.classifier.cifar10

The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research.[1][2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes.[3] The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.
(https://en.wikipedia.org/wiki/CIFAR-10)

from __future__ import absolute_import, division, print_function, unicode_literals
try:
  # %tensorflow_version only exists in Colab.
  %tensorflow_version 2.x
except Exception:
  pass
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

 

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

 

train_images, test_images = train_images / 255.0, test_imaages / 255.0

 

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']
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)
    # The CIFAR labels happen to be arrays, 
    # which is why you need the extra index
    plt.xlabel(class_names[train_labels[i][0]])
plt.show()

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    tf.keras.layers.MaxPooling2D((2,2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64,activation='relu'),
    tf.keras.layers.Dense(10,activation='softmax')
])

 

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_6 (Conv2D)            (None, 30, 30, 32)        896       
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 13, 13, 64)        18496     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 4, 4, 64)          36928     
_________________________________________________________________
flatten_1 (Flatten)          (None, 1024)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 64)                65600     
_________________________________________________________________
dense_3 (Dense)              (None, 10)                650       
=================================================================
Total params: 122,570
Trainable params: 122,570
Non-trainable params: 0
_________________________________________________________________

 

N : number of image's pixel(이미지의 픽셀 수)
S : stride(필터 이동 간격)
F : filter size(필터 크기 3X3)
convolution [ (N-F)/S+1 ]

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
history = model.fit(train_images, train_labels, epochs=10, 
                    validation_data=(test_images, test_labels))

Train on 50000 samples, validate on 10000 samples
Epoch 1/10
50000/50000 [==============================] - 79s 2ms/step - loss: 1.5278 - acc: 0.4412 - val_loss: 1.3302 - val_acc: 0.5279
Epoch 2/10
50000/50000 [==============================] - 78s 2ms/step - loss: 1.1527 - acc: 0.5915 - val_loss: 1.0880 - val_acc: 0.6099
Epoch 3/10
50000/50000 [==============================] - 75s 2ms/step - loss: 0.9970 - acc: 0.6507 - val_loss: 0.9991 - val_acc: 0.6487
Epoch 4/10
50000/50000 [==============================] - 78s 2ms/step - loss: 0.8947 - acc: 0.6863 - val_loss: 0.9526 - val_acc: 0.6654
Epoch 5/10
50000/50000 [==============================] - 78s 2ms/step - loss: 0.8297 - acc: 0.7093 - val_loss: 0.8807 - val_acc: 0.6954
Epoch 6/10
50000/50000 [==============================] - 75s 2ms/step - loss: 0.7698 - acc: 0.7291 - val_loss: 0.8570 - val_acc: 0.6988
Epoch 7/10
50000/50000 [==============================] - 75s 2ms/step - loss: 0.7261 - acc: 0.7460 - val_loss: 0.8692 - val_acc: 0.6973
Epoch 8/10
50000/50000 [==============================] - 77s 2ms/step - loss: 0.6873 - acc: 0.7596 - val_loss: 0.8995 - val_acc: 0.6901
Epoch 9/10
50000/50000 [==============================] - 77s 2ms/step - loss: 0.6434 - acc: 0.7736 - val_loss: 0.8866 - val_acc: 0.7021
Epoch 10/10
50000/50000 [==============================] - 75s 2ms/step - loss: 0.6075 - acc: 0.7851 - val_loss: 0.8685 - val_acc: 0.7122

 

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

print(test_loss,test_acc)

 

# Retrieve a list of accuracy results on training and validation data
# sets for each training epoch
acc = history.history['acc']
val_acc = history.history['val_acc']

# Retrieve a list of list results on training and validation data
# sets for each training epoch
loss = history.history['loss']
val_loss = history.history['val_loss']

# Get number of epochs
epochs = range(len(acc))

# Plot training and validation accuracy per epoch
plt.plot(epochs, acc)
plt.plot(epochs, val_acc)
plt.title('Training and validation accuracy')

plt.figure()

# Plot training and validation loss per epoch
plt.plot(epochs, loss)
plt.plot(epochs, val_loss)
plt.title('Training and validation loss')