[tf, keras] trained model 저장 및 사용 - (1)TensorFlow, Keras

Trained model을 어떻게 사용할까? Tensorflow, Keras 버전

How to use your trained model - Deep Learning basics with Python, TensorFlow and Keras

 

Save Model

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import pickle
import time

pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)

pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)

X = X/255.0

dense_layers = [0]
layer_sizes = [64]
conv_layers = [3]

for dense_layer in dense_layers:
    for layer_size in layer_sizes:
        for conv_layer in conv_layers:
            NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
            print(NAME)

            model = Sequential()

            model.add(Conv2D(layer_size, (3, 3), input_shape=X.shape[1:]))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2, 2)))

            for l in range(conv_layer-1):
                model.add(Conv2D(layer_size, (3, 3)))
                model.add(Activation('relu'))
                model.add(MaxPooling2D(pool_size=(2, 2)))

            model.add(Flatten())

            for _ in range(dense_layer):
                model.add(Dense(layer_size))
                model.add(Activation('relu'))

            model.add(Dense(1))
            model.add(Activation('sigmoid'))

            tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))

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

            model.fit(X, y,
                      batch_size=32,
                      epochs=10,
                      validation_split=0.3,
                      callbacks=[tensorboard])

model.save('64x3-CNN.model') #SAVE MODEL

 

Load Model

import cv2
import tensorflow as tf

CATEGORIES = ["Dog", "Cat"]

def prepare(filepath):
   IMG_SIZE = 70
   img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
   new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)
   return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)

model = tf.keras.models.load_model("64x3-CNN.model") #LOAD MODEL

prediction = model.predict([prepare('dog.jpg')])

print(prediction) #[[0.]]

print(CATEGORIES[int(prediction[0][0])]) #Dog

 

 

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출처

https://www.youtube.com/watch?v=A4K6D_gx2Iw