Adobe After Effects CS4 完整教程指南

AE
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"after_effects_cs4_help.pdf 是一个完整的Adobe After Effects CS4教程，涵盖了该软件的使用方法和技巧。这个指南受版权保护，由Adobe Systems Incorporated创作并提供，仅供信息使用，可能会随时更新，不作为任何承诺。尽管如此，Adobe并不对其中可能出现的错误或不准确承担任何责任。用户可以依据Creative Commons Attribution Non-Commercial 3.0 License的条款使用此用户指南，允许非商业目的的复制、分发和传输，但需标明Adobe为用户指南的所有者，并通知任何重用或分布都应遵守这些条款。" 在Adobe After Effects CS4（简称AE CS4）这款强大的视觉特效和动态图形编辑软件中，用户可以创建复杂的动画、合成和视觉效果。以下是AE CS4的一些核心知识点： 1. **工作界面**：AE CS4的界面包括时间线、项目面板、合成面板、预览窗口、效果和预设面板等，每个部分都有特定的功能，帮助用户组织和操作媒体资产。 2. **合成创建**：合成是AE的基础，用户可以导入视频、图像和音频素材，然后在合成中进行编辑。用户可以选择不同的分辨率和帧率来创建新合成。 3. **层管理**：在时间线上，每项元素都表示为一个层，用户可以通过调整层的顺序、透明度、运动路径等属性来实现多层次的叠加效果。 4. **关键帧动画**：通过设置关键帧，用户可以定义某个属性在不同时间点的值，After Effects会自动计算中间帧，创建平滑的动画过渡。 5. **效果应用**：AE CS4包含大量内置特效，如模糊、扭曲、颜色校正、粒子系统等，用户可以将这些效果应用于层或合成，增强视觉效果。 6. **脚本与表达式**：AE支持使用JavaScript语法的表达式，可以编程控制层属性，实现更高级的自动化效果。 7. **3D工作流程**：AE CS4支持3D合成，用户可以创建和编辑3D层，添加深度到他们的项目中，还可以与其他3D应用程序进行交互。 8. **渲染与输出**：完成编辑后，用户可以设置输出模块，选择合适的编码器和格式，导出最终的视频文件。 9. **协作与模板**：AE支持团队项目和预设，方便团队成员共享工作和模板，提高工作效率。 10. **学习资源**：由于AE CS4功能强大，官方提供的用户指南是学习的重要资源，此外还有在线社区、教程和插件供用户进一步提升技能。 这个完全版教程将深入讲解以上知识点，帮助用户从初学者成长为熟练的After Effects使用者，掌握专业级别的视觉特效制作技术。

After_Effects_CS6_chs_3987.rar

2020-04-01 上传
AE CS6资源，完整的AE CS6版本 After_Effects_CS6_chs_3987.rar

After_Effects_CS5.5注册机

2012-08-13 上传
After_Effects_CS5.5的注册机

SELECT orders.id,orders.user_id,orders.cityarea_id,orders.batchcode,orders.status, orders.pay_status,orders.real_pay,orders.total,orders.money,orders.pay_type, orders.premium,orders.createtime,orders.paytime,orders.confirmtime, orders.is_evaluate,orders.pay_method,orders.exp_user_id, orders.return_score, orders.is_change,orders.reward_money,orders.isvalid,orders.after_sale_status,orders.remark, orders.use_coupon_detail,orders.block_chain_price, users.name,users.weixin_name,users.weixin_fromuser, users.phone,wcoe.diy_area_name ,writes.ActivityStatus,writes.NewMoney,records.Commission,records.ReturnRadio from wsy_o2o.weixin_cityarea_orders orders left join wsy_o2o.weixin_cityarea_supply supply on orders.cityarea_id = supply.id left join wsy_o2o.weixin_cityarea_payonline_orders wcco on orders.batchcode=wcco.batchcode left join wsy_user.weixin_users users on users.id = orders.user_id LEFT JOIN wsy_o2o.weixin_cityarea_orders_extend wcoe ON orders.batchcode = wcoe.batchcode LEFT JOIN wsy_dream.ShopWrite writes on orders.batchcode = writes.Batchcode LEFT JOIN wsy_o2o.weixin_cityarea_supply_commissons_record records ON orders.cityarea_id = records.SupplyId and records.CreateTime < orders.createtime ORDER BY records.CreateTime DESC LIMIT 1 WHERE orders.types=3 and orders.customer_id = 41 AND orders.isvalid=true GROUP BY orders.batchcode ORDER BY orders.id DESC limit 0,20; 以上代码报错，怎么解决

2023-07-11 上传
orders.is_change, orders.reward_money, orders.isvalid, orders.after_sale_status, orders.remark, orders.use_coupon_detail, orders.block_chain_price, users.name, users.weixin_name, users.weixin_fromuser...

def get_lr(self): if self.last_epoch > self.total_epoch: if self.after_scheduler: if not self.finished: self.after_scheduler.base_lrs = [base_lr * self.multiplier for base_lr in self.base_lrs] self.finished = True return self.after_scheduler.get_lr() return [base_lr * self.multiplier for base_lr in self.base_lrs] if self.multiplier == 1.0: return [base_lr * (float(self.last_epoch) / self.total_epoch) for base_lr in self.base_lrs] else: return [base_lr * ((self.multiplier - 1.) * self.last_epoch / self.total_epoch + 1.) for base_lr in self.base_lrs]

2023-06-01 上传
具体来说，该方法首先判断当前轮次是否超过总的训练轮数（total_epoch），如果超过了，就表示开始使用 after_scheduler（如果有），并将每个参数的初始学习率乘以 multiplier 倍数。如果没有 after_scheduler，则...

def step(self, epoch=None, metrics=None): if type(self.after_scheduler) != ReduceLROnPlateau: if self.finished and self.after_scheduler: if epoch is None: self.after_scheduler.step(None) else: self.after_scheduler.step(epoch - self.total_epoch) else: return super(GradualWarmupScheduler, self).step(epoch) else: self.step_ReduceLROnPlateau(metrics, epoch)

2023-06-01 上传
首先，它会检查 after_scheduler 是否为 ReduceLROnPlateau 类型。如果不是，它会根据当前的训练 epoch 和总的训练 epoch 数量来判断是否需要调用 after_scheduler.step() 函数来进一步调整学习率。如果已经训练完成...

修改 错误 SELECT pr.PRODUCT_ID, pr.PRODUCT_NAME, pr.ORIGINAL_PRICE, pr.ONLINE_PRICE, pr.COMMISSION_PERCENT, pr.TOTAL_SALES, pr.SHOP, dict.label AS COMMERCE_TYPE_LABEL, pr.SERVICE_RATE, ord.ORDER_ID, date.label AS SEND_FLG_LABEL, pro.CONNECT_BUSINESS_FLG, address.APPLY_REMARK, address.CREATE_TIME, users.nicknameFROM t_product AS prLEFT JOIN t_order AS ord ON ord.PRODUCT_ID = pr.IDLEFT JOIN ( SELECT label, `value` FROM system_dict_data WHERE dict_type = 'COMMERCE_TYPE' ) AS dict ON dict.`value` = pr.COMMERCE_TYPE LEFT JOIN ( SELECT label, `value` FROM system_dict_data WHERE dict_type = 'trade_after_sample_type' ) AS date ON date.`value` = ord.SEND_FLGLEFT JOIN t_apply_pro AS pro ON pro.PRODUCT_ID = pr.IDLEFT JOIN t_apply_pro_user_address AS address ON address.t_apply_pro_id = pro.IDLEFT JOIN system_users AS users ON users.id = pro.OPERATE_USER_ID WHERE users.id = '1'

2023-06-02 上传
WHERE dict_type = 'trade_after_sample_type' ) AS date ON date.`value` = ord.SEND_FLG LEFT JOIN t_apply_pro pro ON pro.PRODUCT_ID = pr.ID LEFT JOIN t_apply_pro_user_address address ON address.t_...

CREATE TRIGGER update_invoice_complex AFTER UPDATE ON t_sfi_inter_invoice FOR EACH ROW BEGIN INSERT INTO t_sfi_inter_bus_complex (OPER_TIME, OPERATION_TYPE, OLD_OBJECT,NEW_OBJECT) VALUES (NOW(), 'update',JSON_OBJECT(OLD.ID,OLD_OINVOICE_NO,OLD.SUM,OLD.FACT_SUM,OLD.STATUS,OLD.BUSINESS_ID,OLD.REINVOICE,OLD.IS_PRINTED,OLD.PRINT_USER_ID,OLD.PRINT_USER_NAME,OLD.PRINT_DATE,OLD.INVOICE_TYPE,OLD.INVOICE_STATUS,OLD.TOMB_CERTIFICATE),JSON_OBJECT(NEW.ID,OINVOICE_NO,NEW.SUM,NEW.FACT_SUM,NEW.STATUS,NEW.BUSINESS_ID,NEW.REINVOICE,NEW.IS_PRINTED,NEW.PRINT_USER_ID,NEW.PRINT_USER_NAME,NEW.PRINT_DATE,NEW.INVOICE_TYPE,NEW.INVOICE_STATUS,NEW.TOMB_CERTIFICATE)); END;优化一下

2023-06-13 上传
AFTER UPDATE ON t_sfi_inter_invoice FOR EACH ROW BEGIN DECLARE old_invoice_json JSON; DECLARE new_invoice_json JSON; SET old_invoice_json = JSON_OBJECT('id', OLD.ID, 'oinvoice_no', OLD....

优化一下CREATE TRIGGER update_invoice_complex AFTER UPDATE ON t_sfi_inter_invoice FOR EACH ROW BEGIN INSERT INTO t_sfi_inter_bus_complex (OPER_TIME, OPERATION_TYPE, OLD_OBJECT,NEW_OBJECT) VALUES (NOW(), 'update',JSON_OBJECT(OLD.ID,OLD.INVOICE_NO,OLD.SUM,OLD.FACT_SUM,OLD.STATUS,OLD.BUSINESS_ID,OLD.REINVOICE,OLD.IS_PRINTED,OLD.PRINT_USER_ID,OLD.PRINT_USER_NAME,OLD.PRINT_DATE,OLD.INVOICE_TYPE,OLD.INVOICE_STATUS,OLD.TOMB_CERTIFICATE),JSON_OBJECT(NEW.ID,NEW.INVOICE_NO,NEW.SUM,NEW.FACT_SUM,NEW.STATUS,NEW.BUSINESS_ID,NEW.REINVOICE,NEW.IS_PRINTED,NEW.PRINT_USER_ID,NEW.PRINT_USER_NAME,NEW.PRINT_DATE,NEW.INVOICE_TYPE,NEW.INVOICE_STATUS,NEW.TOMB_CERTIFICATE)); END;

2023-06-13 上传
1. 由于该触发器在每次更新t_sfi_inter_invoice表的一行数据时都会被触发，因此为了提高效率，可以将该触发器改为AFTER UPDATE ON t_sfi_inter_invoice FOR EACH STATEMENT，这样该触发器只会在更新语句执行完毕后被...

CREATE TRIGGER insert_order_trigger AFTER INSERT ON order_info EACH ROW BEGIN UPDATE order_info SET order_info.ord_price = ( SELECT food_info.food_price FROM food_info WHERE food_info.food_name = NEW.food_name ) WHERE food_info.food_no = NEW.food_no; END > 1064 - You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'EACH ROW BEGIN UPDATE order_info SET order_info.ord_price = ( SELECT food_i' at line 1

2023-06-11 上传
4. WHERE 子句应该是 "order_info.food_no = NEW.food_no" 而不是 "food_info.food_no = NEW.food_no"。 根据上述修正，以下是修正后的 SQL 语句： ``` CREATE TRIGGER insert_order_trigger AFTER INSERT ON ...

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
# Generate after the final epoch # display.clear_output(wait=True) # generate_and_save_ecg(generator, epochs, seed, False) ``` 注意：上述代码仅作为示例，可能需要根据实际情况进行调整和修改。
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