NVIDIA 딥러닝 기초 9. 이미지개선

from tensorflow import keras

base_model = keras.applications.VGG16(
    weights='imagenet',
    input_shape=(224, 224, 3),
    include_top= False)

# Freeze base model
base_model.trainable = False

# Create inputs with correct shape
inputs = keras.Input(shape=(224, 224, 3))

x = base_model(inputs, training=False)

# Add pooling layer or flatten layer
x = keras.layers.GlobalAveragePooling2D()(x)

# Add final dense layer
outputs = keras.layers.Dense(1, activation = 'softmax')(x)


# Combine inputs and outputs to create model
model = keras.Model(inputs, outputs)

model.compile(loss='categorical_crossentropy', metrics=['accuracy'])

from tensorflow.keras.preprocessing.image import ImageDataGenerator
# create a data generator
datagen = ImageDataGenerator(
        samplewise_center=True,  # set each sample mean to 0
        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
        zoom_range = 0.1, # Randomly zoom image
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False) # we don't expect Bo to be upside-down so we will not flip vertically

# load and iterate training dataset
train_it = datagen.flow_from_directory('data/fruits/train/',
                                       target_size=(224, 224),
                                       color_mode='rgb',
                                       class_mode="categorical")
# load and iterate validation dataset
valid_it = datagen.flow_from_directory('data/fruits/valid/',
                                      target_size=(224, 224),
                                      color_mode='rgb',
                                      class_mode="categorical")

model.fit(train_it,
          validation_data=valid_it,
          steps_per_epoch=train_it.samples/train_it.batch_size,
          validation_steps=valid_it.samples/valid_it.batch_size,
          epochs=10)