from models import LeNet, AlexNet, VGG13, ResNet34, TestNet
from keras.models import load_model
from keras import optimizers
from prepare_data import load_data
from utils import CLASS_NUM, IMG_SIZE
import os
train_X, test_X, train_y, test_y = load_data(class_num=CLASS_NUM,
img_size=IMG_SIZE)
if not os.path.exists('autogta.h5'):
autogta = AlexNet(train_X[0].shape)
autogta.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
autogta.fit(x=train_X, y=train_y, epochs=10, batch_size=16)
autogta.save('autogta.h5')
else:
autogta = load_model('autogta.h5')
loss, accu = autogta.evaluate(x=test_X, y=test_y)
print('loss\t{}\naccuracy\t{}'.format(loss, accu))
import tensorflow.keras as K
from models import AlexNet
model = AlexNet("fusion", 8, (160, 120, 4)).get_model()
model.compile(optimizer="sgd",
loss="categorical_crossentropy",
metrics=["accuracy"])
K.utils.plot_model(model, to_file="asdf.png")
