"DISC性格测试及分析解析课件:了解DISC个性特征,提升个人行为表现"。

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DISC性格是一种人格测验,最早出现在20世纪20年代。该理论的基础源自于威廉马斯顿博士发明测谎仪的经验,并在1928年提出了DISC理论。马斯顿博士将该理论扩大应用于心理健康的普通人群,并构建了一个完整的体系,即“The Emotions of Normal People”。 在古希腊时期,人们认为一个人的行为模式是其整体生理状况的综合反映。他们相信人体有四种基本体液,分别是火、空气、水和土四种元素的组合。当其中某一种体液特性强于其他特性时,它会影响到个体的情绪和行事态度。古希腊人认为血液、黄胆汁、粘液和黑胆汁代表四种不同的行为类型,也被称为多血质、胆汁质、粘液质和抑郁质。 DISC性格测验根据这种理论,将人的性格行为分为四种不同的类型,即支配型(Dominance)、影响型(Influence)、稳定型(Steadiness)和服从型(Compliance)。支配型的人性格特点是果断、决断、自信和坚决,他们通常有强烈的目标驱动力和决心,善于领导和控制。影响型的人天性乐观、充满活力和热情洋溢,能够轻松地与人建立联系,并影响其他人。稳定型的人性格特点是稳重、耐心和可靠,他们通常喜欢按照固定的计划和步骤进行工作,注重细节和正确性。服从型的人善于合作、服从和支持他人,他们通常具有高度的责任感和崇尚规则的倾向。 DISC个性特征的分析和解析在个人的提升和有效运用方面起到了重要的作用。通过了解和分析自己的DISC特征,个人能够更好地了解自己的优势和劣势,并在工作和生活中根据自己的性格优势进行相关的决策和行动。而在团队合作和人际交往方面,了解他人的DISC特征可以帮助个人更好地理解他人的行为和思维方式,从而更好地沟通和协作。 DISC性格分析的课件提供了一种科学的方法和工具,用于评估和解析个人的DISC特征。这些课件不仅包含了有关DISC性格的基本知识和理论,还提供了测试题和测评量表,以帮助个人更深入地了解自己的DISC性格类型。通过参与DISC性格培训课程,个人可以通过测量和解析自己的DISC特征来提高自我认知和个人发展。 总之,DISC性格是一种基于古希腊人的人格理论发展而来的人格测验,通过分析个体的行为特征将人格分为支配型、影响型、稳定型和服从型四种类型。了解和应用DISC性格特征可以帮助个人提升自我认知、改进人际关系、提高团队合作效果。DISC性格培训课件提供了一种有效的方法和工具,用于评估和解析个人的DISC特征,并帮助个人在工作和生活中取得更好的成果。

def train_step(real_ecg, dim): noise = tf.random.normal(dim) for i in range(disc_steps): with tf.GradientTape() as disc_tape: generated_ecg = generator(noise, training=True) real_output = discriminator(real_ecg, training=True) fake_output = discriminator(generated_ecg, training=True) disc_loss = discriminator_loss(real_output, fake_output) gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables) discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables)) ### for tensorboard ### disc_losses.update_state(disc_loss) fake_disc_accuracy.update_state(tf.zeros_like(fake_output), fake_output) real_disc_accuracy.update_state(tf.ones_like(real_output), real_output) ####################### with tf.GradientTape() as gen_tape: generated_ecg = generator(noise, training=True) fake_output = discriminator(generated_ecg, training=True) gen_loss = generator_loss(fake_output) gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables) generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables)) ### for tensorboard ### gen_losses.update_state(gen_loss) ####################### def train(dataset, epochs, dim): for epoch in tqdm(range(epochs)): for batch in dataset: train_step(batch, dim) disc_losses_list.append(disc_losses.result().numpy()) gen_losses_list.append(gen_losses.result().numpy()) fake_disc_accuracy_list.append(fake_disc_accuracy.result().numpy()) real_disc_accuracy_list.append(real_disc_accuracy.result().numpy()) ### for tensorboard ### # with disc_summary_writer.as_default(): # tf.summary.scalar('loss', disc_losses.result(), step=epoch) # tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch) # tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch) # with gen_summary_writer.as_default(): # tf.summary.scalar('loss', gen_losses.result(), step=epoch) disc_losses.reset_states() gen_losses.reset_states() fake_disc_accuracy.reset_states() real_disc_accuracy.reset_states() ####################### # Save the model every 5 epochs # if (epoch + 1) % 5 == 0: # generate_and_save_ecg(generator, epochs, seed, False) # checkpoint.save(file_prefix = checkpoint_prefix) # Generate after the final epoch display.clear_output(wait=True) generate_and_save_ecg(generator, epochs, seed, False)

2023-06-08 上传

请解释此段代码class GATrainer(): def __init__(self, input_A, input_B): self.program = fluid.default_main_program().clone() with fluid.program_guard(self.program): self.fake_B = build_generator_resnet_9blocks(input_A, name="g_A")#真A-假B self.fake_A = build_generator_resnet_9blocks(input_B, name="g_B")#真B-假A self.cyc_A = build_generator_resnet_9blocks(self.fake_B, "g_B")#假B-复原A self.cyc_B = build_generator_resnet_9blocks(self.fake_A, "g_A")#假A-复原B self.infer_program = self.program.clone() diff_A = fluid.layers.abs( fluid.layers.elementwise_sub( x=input_A, y=self.cyc_A)) diff_B = fluid.layers.abs( fluid.layers.elementwise_sub( x=input_B, y=self.cyc_B)) self.cyc_loss = ( fluid.layers.reduce_mean(diff_A) + fluid.layers.reduce_mean(diff_B)) * cycle_loss_factor #cycle loss self.fake_rec_B = build_gen_discriminator(self.fake_B, "d_B")#区分假B为真还是假 self.disc_loss_B = fluid.layers.reduce_mean( fluid.layers.square(self.fake_rec_B - 1))###优化生成器A2B,所以判别器结果越接近1越好 self.g_loss_A = fluid.layers.elementwise_add(self.cyc_loss, self.disc_loss_B) vars = [] for var in self.program.list_vars(): if fluid.io.is_parameter(var) and var.name.startswith("g_A"): vars.append(var.name) self.param = vars lr = 0.0002 optimizer = fluid.optimizer.Adam( learning_rate=fluid.layers.piecewise_decay( boundaries=[ 100 * step_per_epoch, 120 * step_per_epoch, 140 * step_per_epoch, 160 * step_per_epoch, 180 * step_per_epoch ], values=[ lr, lr * 0.8, lr * 0.6, lr * 0.4, lr * 0.2, lr * 0.1 ]), beta1=0.5, name="g_A") optimizer.minimize(self.g_loss_A, parameter_list=vars)

2023-06-07 上传

def calc_gradient_penalty(self, netD, real_data, fake_data): alpha = torch.rand(1, 1) alpha = alpha.expand(real_data.size()) alpha = alpha.cuda() interpolates = alpha * real_data + ((1 - alpha) * fake_data) interpolates = interpolates.cuda() interpolates = Variable(interpolates, requires_grad=True) disc_interpolates, s = netD.forward(interpolates) s = torch.autograd.Variable(torch.tensor(0.0), requires_grad=True).cuda() gradients1 = autograd.grad(outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones(disc_interpolates.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradients2 = autograd.grad(outputs=s, inputs=interpolates, grad_outputs=torch.ones(s.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] if gradients2 is None: return None gradient_penalty = (((gradients1.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) + \ (((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) return gradient_penalty def get_loss(self, net,fakeB, realB): self.D_fake, x = net.forward(fakeB.detach()) self.D_fake = self.D_fake.mean() self.D_fake = (self.D_fake + x).mean() # Real self.D_real, x = net.forward(realB) self.D_real = (self.D_real+x).mean() # Combined loss self.loss_D = self.D_fake - self.D_real gradient_penalty = self.calc_gradient_penalty(net, realB.data, fakeB.data) return self.loss_D + gradient_penalty,return self.loss_D + gradient_penalty出现错误:TypeError: unsupported operand type(s) for +: 'Tensor' and 'NoneType'

2023-05-24 上传

void sl_notify_gap_evt_to_porting_layer(sl_bt_msg_t *evt) { sl_status_t sc; bd_addr address; uint8_t address_type; struct ble_gap_event event; memset(&event, 0, sizeof(event)); switch (SL_BT_MSG_ID(evt->header)) { // ------------------------------- // This event is generated when an advertisement packet or a scan response // is received from a responder case sl_bt_evt_scanner_legacy_advertisement_report_id: memset(&event, 0, sizeof(event)); event.type = BLE_GAP_EVENT_DISC; event.disc.event_type = convert_to_nimble_adv_type(evt->data.evt_scanner_legacy_advertisement_report.event_flags); event.disc.length_data = evt->data.evt_scanner_legacy_advertisement_report.data.len; event.disc.data = evt->data.evt_scanner_legacy_advertisement_report.data.data; event.disc.addr.type = evt->data.evt_scanner_legacy_advertisement_report.address_type; memcpy(event.disc.addr.val, evt->data.evt_scanner_legacy_advertisement_report.address.addr, 6); event.disc.rssi = evt->data.evt_scanner_legacy_advertisement_report.rssi; notify_ble_event_to_listeners(&event); break; // ------------------------------- // This event indicates that a new connection was opened. case sl_bt_evt_connection_opened_id: event.type = BLE_GAP_EVENT_CONNECT; event.connect.conn_handle = evt->data.evt_connection_opened.connection; app_log_info("BLE connected, conn_id:%d\n", event.connect.conn_handle); notify_ble_event_to_listeners(&event); break; // ------------------------------- // This event indicates that a connection was closed. case sl_bt_evt_connection_closed_id: event.type = BLE_GAP_EVENT_DISCONNECT; event.disconnect.conn.conn_handle = evt->data.evt_connection_closed.connection; event.disconnect.reason = evt->data.evt_connection_closed.reason; app_log_info("BLE disconnected, conn_id:%d, reason:0x%02x\n", event.disconnect.conn.conn_handle, event.disconnect.reason); notify_ble_event_to_listeners(&event); break; case sl_bt_evt_gatt_mtu_exchanged_id: event.type = BLE_GAP_EVENT_MTU; event.mtu.conn_handle = evt->data.evt_gatt_mtu_exchanged.connection; event.mtu.value = evt->data.evt_gatt_mtu_exchanged.mtu; app_log_info("exchange mtu req: %d\n", event.mtu.value); notify_ble_event_to_listeners(&event); break; default: break; } }

2023-06-10 上传