def load_model_network():
X_test, Y_test = load_test_data()
model = CNN('digitos.hdf5')
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer=optimizer,
loss="categorical_crossentropy",
metrics=["accuracy"])
Y_pred = model.predict(X_test)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred, axis=1)
# Convert validation observations to one hot vectors
Y_true = np.argmax(Y_test, axis=1)
# compute the confusion matrix
confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)
print(" ACURÀCIA = %.2f" % (accuracy_score(Y_true, Y_pred_classes) * 100),
"%")
# plot the confusion matrix
plot_confusion_matrix(confusion_mtx, classes=range(10))
def test():
with torch.no_grad():
net = CNN(1, 10)
net.load_state_dict(torch.load('D:/bd/model.tar'))
eval_acc = 0
for img, label in test_loader:
img = img.to(device)
label = label.to(device)
out = net(img)
_, pred = torch.max(out, 1)
num_correct = (pred == label).sum()
eval_acc += num_correct.item()
print('Acc: {:.2f}%'.format(eval_acc / len(test_dataset) * 100))
def predict():
log = '程序开始时间' + str(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')) + '\n'
print('程序开始时间', datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
print('载入模型')
cnn = CNN()
if constant.USE_GPU:
cnn = cnn.cuda()
cnn.load_state_dict(torch.load(constant.LAST_MODEL_PATH))
test_x, test_y, images_name = predict_data_preprocessing([path.get()])
left = 0
mid = 0
right = 0
delete_file_folder(path2.get())
log = '开始检测时间' + str(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')) + '\n'
print('开始检测时间', datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
for i, x in enumerate(test_x):
x.unsqueeze_(0)
x.unsqueeze_(0)
if constant.USE_GPU:
x = x.cuda()
test_output = cnn(x)[0]
pre_y = torch.max(test_output, 1)[1].data.cpu().squeeze().numpy()
if pre_y == 0:
if (i % 3 == 0):
left = 1
elif i % 3 == 1:
mid = 1
else:
right = 1
# print("Failed:" + images_name[i])
if i % 3 == 2:
if left | right | mid:
draw_image(left, right, mid, path.get() + images_name[i][0:-4])
log += '不合格 '
if left: log += '1,'
if mid: log += '2,'
if right: log += '3,'
log=log[:-1]+'\n'
else:
log += '合格\n'
left = 0
mid = 0
right = 0
print('结束时间', str(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
log += '结束时间' + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n'
f = open(path2.get() + '/result.txt', 'w')
f.write(log)
f.close()
subprocess.Popen('explorer "%s"' % path2.get().replace('/', '\\'))
def __init__(self):
print("3--------------")
latest = tf.train.latest_checkpoint('./ckpt')
print("4--------------")
self.cnn = CNN()
print("5--------------")
# 恢复网络权重
self.cnn.model.load_weights(latest)
def main():
train, test = datasets.get_mnist(ndim=3)
model = L.Classifier(CNN())
chainer.serializers.load_npz("result/CNN.model", model)
for i in range(10):
index = random.randrange(0, len(train))
data, label = train[index]
x = chainer.Variable(data.reshape(1, 1, 28, 28))
result = F.softmax(model.predictor(x))
print(" input: {}, result: {}".format(label, result.data.argmax()))
def __init__(self):
latest = tf.train.latest_checkpoint('/content/ckpt/')
self.cnn = CNN()
# 恢复网络权重
self.cnn.model.load_weights(latest)
def __init__(self): self.cnn = CNN() self.data = DataSource()
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets
import numpy as np
from PIL import Image
from train import CNN
net = CNN(1, 10)
net.load_state_dict(torch.load('D:/bd/model.tar'))
input_image = 'D:/4.png'
im = Image.open(input_image).resize((28, 28)) #取图片数据
im = im.convert('L') #灰度图
im_data = np.array(im)
im_data = torch.from_numpy(im_data).float()
im_data = im_data.view(1, 1, 28, 28)
out = net(im_data)
_, pred = torch.max(out, 1)
print('预测的数字是：{}。'.format(pred))
def __init__(self):
latest = tf.train.latest_checkpoint(
'D:/Learn_Files/TensorFlowL/Mnist/model/')
self.cnn = CNN()
# 恢复网络权重
self.cnn.model.load_weights(latest)
def __init__(self):
latest = tf.train.latest_checkpoint('model')
self.cnn = CNN()
# restore model parameters
self.cnn.model.load_weights(latest)
def __init__(self):
latest = tf.train.latest_checkpoint('D:/ckpt')
self.cnn = CNN()
self.cnn.model.load_weights(latest)
def __init__(self):
latest = tf.train.latest_checkpoint('./ckpt')
self.cnn = CNN()
# 恢复网络权重
# self.cnn.model.load_weights(latest)
self.cnn.model.load_weights("newWeight.hdf5")
def __init__(self):
print('【正在读入网络】')
latest = tf.train.latest_checkpoint('./check')
self.cnn = CNN()
self.cnn.model.load_weights(latest)
def init(self):
latest = tf.train.latest_checkpoint('./ckpt')
self.cnn = CNN()
# 恢复网络权重
self.cnn.model.load_weights(latest)
def __init__(self):
self.cnn = CNN()
