def op2(): """ generate rotation data :return: """ data = common.Data("mnist/mnist_train/train_data.npy", "mnist/mnist_train/mnist_train_label", "mnist/mnist_test/test_data.npy", "mnist/mnist_test/mnist_test_label", 1, 28) fig = 28 d = [] l = [] for i in range(len(data.train_x)): data.train_x[i] = data.train_x[i].astype(np.uint8) img = Image.fromarray(data.train_x[i].reshape(fig, fig).astype(np.uint8)) img_r = img.rotate(45) img_l = img.rotate(315) matrix = np.asarray(img_r) matrix = matrix.reshape(fig * fig, ) d.append(data.train_x[i]) d.append(matrix) matrix = np.asarray(img_l) matrix = matrix.reshape(fig * fig, ) d.append(matrix) l.append(data.train_y_no_one_hot[i]) l.append(data.train_y_no_one_hot[i]) l.append(data.train_y_no_one_hot[i]) np.save('mnist/mnist_train/rotate_train.npy', d) np.save('mnist/mnist_train/rotate_label.npy', l)
def main(): data = common.Data(common_path+"/mnist_train/train_data.npy", common_path+"/mnist_train/mnist_train_label", common_path+"/mnist_test/test_data.npy", common_path+"/mnist_test/mnist_test_label", fig) train_x = data.train_x test_x = data.test_x train_y = data.train_y test_y = data.test_y SVM(train_x, test_x, train_y, test_y,('rbf',1.3))
def main(): common.configure_logging('korred') data = common.Data() handler = Handler(data=data) NativeMessageInterface( callback=handler.handle, interactive=os.getenv('INTERACTIVE', False), ).run()
def main(): data = common.Data(common_path + "/mnist_train/train_data.npy", common_path + "/mnist_train/mnist_train_label", common_path + "/mnist_test/test_data.npy", common_path + "/mnist_test/mnist_test_label", fig) train_x = data.train_x test_x = data.test_x train_y = data.train_y test_y = data.test_y Autoencoder(train_x, test_x, train_y, test_y)
def op0(): """ generate lowest 15000 confident images :return: none """ data = common.Data("mnist/mnist_train/train_data.npy", "mnist/mnist_train/mnist_train_label", "mnist/mnist_test/test_data.npy", "mnist/mnist_test/mnist_test_label", 1, 28) res = common.predict('model/1.4.0', 60000, data.train_x, 28) common.gen_data(res, data.train_x, data.train_y_no_one_hot, 15000)
def main(): data = common.Data() with Daemon(data): # TODO: Check for configuration validity and ask user if the old configuration should be kept NativeMessagingConfiguration().write() launchctl_manager = LaunchctlManager() app = App(launchctl_manager) menu_item = rumps.MenuItem(title='Launch at Login') menu_item.state = launchctl_manager.is_loaded() app.menu = [ 'Install Firefox Extension...', menu_item, 'View Logs', ] app.run()
def op1(): """ generate fc2 output for svm :return: none """ data = common.Data("mnist/mnist_train/train_data.npy", "mnist/mnist_train/mnist_train_label", "mnist/mnist_test/test_data.npy", "mnist/mnist_test/mnist_test_label", 1, 28) res = common.predict('CNN/model/SVM1', 60000, data.test_x, 28, "out1") data_fc = [] for i in range(len(res)): data_fc.append(res[i][0][0][0]) data_fc = np.array(data_fc) np.save('mnist/mnist_test/fc1_5.npy', data_fc)
Reparameterization trick by sampling fr an isotropic unit Gaussian. :param z_mean: mean of Gaussian variable z :param z_log_var: covariance matrix of Gaussian variable z :return z sampled from z_mean and z_log_var """ batch = K.shape(z_mean)[0] dim = K.int_shape(z_mean)[1] # by default, random_normal has mean=0 and std=1.0 epsilon = K.random_normal(shape=(batch, dim)) return z_mean + K.exp(0.5 * z_log_var) * epsilon # MNIST dataset data = common.Data(common_path + "/mnist_train/train_data.npy", common_path + "/mnist_train/mnist_train_label", common_path + "/mnist_test/test_data.npy", common_path + "/mnist_test/mnist_test_label", fig) x_train = data.train_x x_test = data.test_x y_train = data.train_y y_test = data.test_y image_size = x_train.shape[1] original_dim = image_size * image_size x_train = np.reshape(x_train, [-1, original_dim]) x_test = np.reshape(x_test, [-1, original_dim]) # network parameters input_shape = (original_dim, ) intermediate_dim = 512 batch_size = 128
# print(x.shape) # print(self.line.weight.shape) nn.init.normal_(self.out.weight) def forward(self, x): re = self.line(x) re = self.active(re) re = self.line2(re) re = self.active2(re) re = self.line3(re) re = self.active3(re) re = self.out(re) return re dataset = c.Data(x, y) dataLoader = tud.DataLoader(dataset, batch_size=20, shuffle=True) model = NetWork3() loss_fn = nn.MSELoss() optimer = torch.optim.Adam(model.parameters(), lr=1e-4) # schuler = torch.optim.lr_scheduler.ExponentialLR(optimer, 0.5) loss_arr = [] def train(epochs): time = 0 last_loss = -1 for epoch in range(1, epochs + 1): model.train() losses = []
""" Read data from CNN_SVM """ import common import numpy as np from sklearn.preprocessing import scale from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import accuracy_score data = common.Data("../mnist/mnist_train/train_data.npy", "../mnist/mnist_train/mnist_train_label", "../mnist/mnist_test/test_data.npy", "../mnist/mnist_test/mnist_test_label", 1, 28) train = np.load('../mnist/mnist_train/fc1_5.npy') test = np.load('../mnist/mnist_test/fc1_5.npy') train = scale(train) test = scale(test) clf = SVC(kernel='rbf') clf.fit(train, data.train_y_no_one_hot) y_pred = clf.predict(test) print(classification_report(data.test_y_no_one_hot, y_pred)) print(accuracy_score(data.test_y_no_one_hot, y_pred))
import common """ Variable Definition batch_size: batch size fig: image size max_epoch: max iteration common_path = common path of input data """ n = 5 batch_size = 500 fig = 45 max_epoch = 200 common_path = "../mnist" data = common.Data(common_path + "/mnist_train/mnist_train_data", common_path + "/mnist_train/mnist_train_label", common_path + "/mnist_test/mnist_test_data", common_path + "/mnist_test/mnist_test_label", 1, fig) X = data.train_x.reshape(data.size, fig, fig, 1) Y = data.train_y testX = data.test_x.reshape(data.size_test, fig, fig, 1) testY = data.test_y img_prep = tflearn.ImagePreprocessing() img_prep.add_featurewise_zero_center(per_channel=True) img_aug = tflearn.ImageAugmentation() net = tflearn.input_data(shape=[None, 45, 45, 1], data_preprocessing=img_prep, data_augmentation=img_aug)
common_path = "../mnist" if os.path.exists(log_dir): os.remove(log_dir) if os.path.exists(model_dir): os.remove(model_dir) os.makedirs(log_dir, exist_ok=True) os.makedirs(model_dir, exist_ok=True) starttime = datetime.datetime.now() logger = common.create_logger( 'CNN', log_format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') data = common.Data(common_path + "/mnist_train/rotate_train.npy", common_path + "/mnist_train/rotate_label.npy", common_path + "/mnist_test/test_data.npy", common_path + "/mnist_test/mnist_test_label", batch_size, fig) def weight_variable(shape, name): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial, name=name) def bias_variable(shape, name): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial, name=name) def conv2d(x, W):