要绘制物体，CPU需要告诉GPU应该绘制什么和如何绘制。通常我们用Mesh来决定绘制什么。而如何绘制是由着色器控制的，着色器实际上就是一组GPU的指令。除了Me...
def depend(batches[i-1]: Batch, batches[i]: Batch) -> None: batches[i-1][0], phony = fork(batches 本来示例代码中是： depend(batches[i-1], batches[i]) 为了和论文中的图对应，我们修改为： depend(batches[i], batches[i+1]) depend 代码也变化为： def depend(batches[i]: Batch, batches[i+1]: Batch) -> None: batches[i][0], phony = fork(batches 重点说明的是： batches[i] 这里是会变化的，比如 batches[0] 在经过 partitions[j] 的计算之后，会变成 batches[0][j]。 因此，在前向计算图上，通过这个赋值操作， batches[i, j+1] 就依赖 batches[i, j]，所以反向计算时候，batches[i, j + 1] 就必须在 batches[i, j]
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batches | lr 4.00000 | ms/batch245.87236|loss 6.12 |ppl 454.27 | epoch 1| 800/ 1452 batches | lr batches | lr 4.00000 | ms/batch247.16889|loss 5.77 |ppl 319.88 | epoch 1| 1400/ 1452 batches | lr batches | lr 4.00000 | ms/batch246.79957|loss 5.70 |ppl 298.97 | epoch 2| 300/ 1452 batches | lr batches | lr 4.00000 | ms/batch245.52330|loss 5.52 |ppl 248.41 | epoch 2| 900/ 1452 batches | lr / 1452 batches | lr 4.00000 | ms/batch246.24137|loss 5.43 |ppl 227.71 | epoch 3| 300/ 1452 batches
4.2,定义访问顺序/ Define Access Sequences to Determine Sending Batches 4.3, 定义条件表/Define Condition Tables to Determine Sending Batches 4.4, 定义发送方的procedure/Define Search Procedures to Determine Sending Batches , 4.5, 定义条件类型/ Define Strategy Types to Determine Receiving Batches 4.6, 定义访问顺序/ Define Access Sequences to Determine Receiving Batches 4.7, 定义条件表/ Define Condition Tables to Determine Receiving Batches 4.8, 定义接收方的procedure/Define Search Procedures to Determine Receiving Batches SAP的条件技术是很有用很好用的功能
, batch_size, device): self.batch_size = batch_size self.batches = batches # data if len(batches) % self.n_batches ! : batches = self.batches[self.index * self.batch_size: len(self.batches)] self.index += 1 batches = self. _to_tensor(batches) return batches elif self.index >= self.n_batches:
实现代码如下： def get_batches(arr, batch_size, n_steps): '''Create a generator that returns batches of # Keep only enough characters to make full batches arr = arr[:n_batches*characters_per_batch 实现代码如下： def get_batches(int_text, batch_size, seq_length): """ Return batches of input and target as a Numpy array """ # numbers of batches arr_int_text = np.array(int_text) n_batches = np.array(list(zip(x,y))) return batches
(tokill, batches_queue, dataset_generator): """Threaded worker for pre-processing input data. () == True: return def threaded_cuda_batches(tokill,cuda_batches_queue,batches_queue): " training_set_list = None #Our train batches queue can hold at max 12 batches at any given time. train_batches_queue = Queue(maxsize=12) #Our numpy batches cuda transferer queue. , \ args=(cuda_transfers_thread_killer, cuda_batches_queue, train_batches_queue)) cudathread.start
4.2,定义访问顺序/ Define Access Sequences to Determine Sending Batches ? ? 4.3, 定义条件表/Define Condition Tables to Determine Sending Batches ? ? 4.5, 定义条件类型/ Define Strategy Types to Determine Receiving Batches ? ? 4.6, 定义访问顺序/ Define Access Sequences to Determine Receiving Batches ? ? 4.7, 定义条件表/ Define Condition Tables to Determine Receiving Batches ? ?
= int(data_loader.num_test_data // batch_size) for batch_index in range(num_batches): start_index = int(data_loader.num_test_data // batch_size) for batch_index in range(num_batches): start_index = int(data_loader.num_test_data // batch_size) for batch_index in range(num_batches): start_index = int(data_loader.num_test_data // batch_size) for batch_index in range(num_batches): start_index = int(data_loader.num_test_data // batch_size) for batch_index in range(num_batches): start_index
#将数据整理成一个维度为[batch_size,num_batches * num_step]的二维数组 data = np.array(id_list[:num_batches * num_batches个batch，存入一个数组 data_batches = np.split(data,num_batches,axis = 1) #重复上述的操作，但是每个位置向右移动一位 + 1]) label = np.reshape(label,[batch_size,num_batches * num_step]) label_batches = np.split( return list(zip(data_batches,label_batches)) batching的流程： 将整个数据存放到一个list中，也就是将整个文档变成一个句子； 设置batch_size ）可以分解成多少个(batch_size, num_step)； 然后将句子分割成num_batches个(batch_size, num_step)。
batches: Seq[Batch]（Batch队列） RuleExecutor 包含了一个 protected def batches: Seq[Batch] 方法，用来获取一系列 Batch（Batch Analyzer 和 Optimizer 中 提供各自己的 batches: ? Optimizer 中的batches略显复杂，Optimizer定义了 三种batches：defaultBatches、excludedRules 、 nonExcludableRules 最终要被执行的 batches为：defaultBatches - (excludedRules - nonExcludableRules) ? throw new TreeNodeException(plan, message, null) } //遍历batches，取出batch batches.foreach { batch
self.batch_size = 64 self.poetry_file = poetry_file self.load() self.create_batches = [] self.y_batches = [] for i in range(self.n_size): batches = self.poetrys_vector r = length - len(batches[row]) batches[row][len(batches[row]): length] = [self.unknow_char ] * r xdata = np.array(batches) ydata = np.copy(xdata) ydata[:, : -1] = xdata[:, 1:] self.x_batches.append(xdata) self.y_batches.append(ydata)
channel；Stop方法执行close(c.quit) run loki/pkg/promtail/client/client.go func (c *client) run() { batches := map[string]*batch{} // Given the client handles multiple batches (1 per tenant) and each batch // can be created at a different point in time, we look for batches whose // max wait time has been reached every 10 times per BatchWait, so that the // maximum delay we have sending batches ok { batches[e.tenantID] = newBatch(e) break }
is greater than the attempt there TrackedBatch tracked = (TrackedBatch) _batches.get (id.getId()); // if(_batches.size() > 10 && _context.getThisTaskIndex() == 0) { // =null) { if(id.getAttemptId() > tracked.attemptId) { _batches.remove(id.getId (id.getId()); // if(_batches.size() > 10 && _context.getThisTaskIndex() == 0) { // =null) { if(id.getAttemptId() > tracked.attemptId) { _batches.remove(id.getId
这里的batches和val_batches用的是vgg里面的函数，test_batches用的是keras中的函数，两者都能用。 batches = vgg.get_batches(path=trn_path, batch_size=batch_size, shuffle=False) val_batches = vgg.get_batches def fit_model(model, batches, val_batches, nb_epoch=1): model.fit_generator(batches, samples_per_epoch =batches.N, nb_epoch=nb_epoch, validation_data=val_batches, nb_val_samples=val_batches.N fit_model(model, batches, val_batches, nb_epoch=2) 到这一步就已经完成fine_tuning的效果，现在保存这个模型的所有参数，以备后期调用。
4 5 words = np.asarray(int_text[:n_batches*(batch_size * seq_length)]) 6 7 8 9 batches = np.zeros 24 25 target_idx = idx // n_batches 26 27 batches[input_idx,0,target_idx,:] = input_sequences batches 定义用于训练的超参数。 1batches = get_batches(int_text, batch_size, seq_length) 2 3 4 5with tf.Session(graph=train_graph > batches 34 35 if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0: 36 37
It modifies the given batches in place. """ batches = self.batches partitions = self.partitions after computation for the previous micro-batches. """ batches = self.batches batches = self.batches partitions = self.partitions devices = self.devices copy_streams = after computation for the previous micro-batches. """ batches = self.batches = 0: depend(batches[i-1], batches[i]) # 设定依赖关系 next_stream = copy_streams
n_batches: (50|1-100) 这个变量决定 DD 生成的静态图片的数量，默认值是 50，也就是说跑一次一下子给你作 50 张画。 在默认设置下，DD 每个步骤执行的切割数量为 cutn_batches x 16。 所以，增加 cutn_batches 会增加渲染时间，因为工作是按顺序完成的。 第一次，我只修改了 n_batches 参数值，将其设为 1，耗时 07:18，得到如下图像： 第二次，我修改的参数 n_batches=1 cutn_batches=1，结果耗时 04:55，得到如下图像 ： 第三次，我修改的参数 n_batches=1 cutn_batches=1 skip_augs=True steps=150，耗时降低到 02:45，得到如下图像： 通过这些尝试，发现适当降低画作的质量
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