def load_trained_model():
net = MultiLayerNet()
net.load_model("dog_cat_f.npz")
print(net.params.keys())
return
for k, v in net.params.items():
print(k, v.shape)
for i in range(5):
img_idx = random.randrange(0, 1000)
transposed_array = np.transpose(x_test[img_idx], (1, 2, 0))
plt.imshow(transposed_array, cmap='gray')
plt.show()
predict_num = np.argmax(net.predict(np.array([x_test[img_idx]]),
train_flg=False),
axis=1)[0]
print(net.predict(np.array([x_test[img_idx]])))
correct_num = t_test[img_idx]
print(f"Predict : {predict_num}, Correct : {correct_num}")
# load_trained_model()
def show_img_predict():
net = MultiLayerNet()
net.load_model("dog_cat_f.npz")
print(net.layers)
print(net.params)
base_dir = 'C:\\Users\\User\\deep-learning-without-tensorflow\\src\\jjy\\jjy\\dataset\\dog_cat'
test_dir = os.path.join(base_dir, 'test_set\\test_set')
# 테스트에 사용되는 고양이/개 이미지 경로
test_cats_dir = os.path.join(test_dir, 'cats')
test_dogs_dir = os.path.join(test_dir, 'dogs')
print(test_cats_dir)
print(test_dogs_dir)
print('Total validation cat images :', len(os.listdir(test_cats_dir)))
print('Total validation dog images :', len(os.listdir(test_dogs_dir)))
test_cat_fnames = os.listdir(test_cats_dir)
test_dog_fnames = os.listdir(test_dogs_dir)
def img_to_array(fname, original=False):
image = Image.open(fname)
if original is False:
image = image.resize((64, 64))
image = np.reshape(image.convert("L"), (1, 64, 64))
# show_img_by_array(np.asarray(image))
return np.asarray(image)
random.shuffle(test_cat_fnames)
random.shuffle(test_dog_fnames)
img_list = []
predict_list = []
for fname in test_dog_fnames[:15]:
fname = os.path.join(test_dogs_dir, fname)
img_original_array = img_to_array(fname, original=True)
img_array = img_to_array(fname)
img_array = img_array / 255.0
img_list.append(img_original_array)
predict_num = np.argmax(net.predict(np.array([img_array]),
train_flg=False),
axis=1)[0]
print(net.predict(np.array([img_array]), train_flg=False))
# print(predict_num)
predict_list.append("CAT!" if predict_num == 0 else "DOG!")
plot_grid(img_list, predict_list, 3, 5)
plt.show()
from jjy.framework.network import MultiLayerNet
import random
from jjy.dataset.mnist import load_mnist
import numpy as np
import matplotlib.pyplot as plt
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
net = MultiLayerNet()
net.load_model("train_weight_2021-04-17 135013.npz")
for i in range(5):
img_idx = random.randrange(0, 10000)
plt.imshow(x_test[img_idx].reshape(28, 28), cmap='gray')
plt.show()
predict_num = np.argmax(net.predict(np.array([x_test[img_idx]]),
train_flg=False),
axis=1)[0]
correct_num = t_test[img_idx]
print(f"Predict : {predict_num}, Correct : {correct_num}")
while True:
try:
data = await websocket.receive_text()
except:
await manager.disconnect(client_id)
return
await manager.broadcast(f"Client {client_id}: {data}")
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")
model_url = "./model/idol_train_weight_2021-06-06 090301_8907_np.npz"
net = MultiLayerNet()
net.load_model("./model/idol_train_weight_2021-06-06 090301_8907_np.npz")
idol_list = ["아이유", "아이린", "아린"]
eng_idol_list = ["iu", "irene", "arin"]
is_local = False
if socket.gethostname()[:4] == "DESK":
is_local = True
def id_generator(size=6,
chars=string.ascii_uppercase + string.digits +
string.ascii_lowercase):
return ''.join(random.choice(chars) for _ in range(size))