if key in [b'x', b'X', b'y', b'Y', b'z', b'Z']: if key == b'x': # 瞄准参考点 x 减小 LOOK_AT[0] -= 0.01 elif key == b'X': # 瞄准参考 x 增大 LOOK_AT[0] += 0.01 elif key == b'y': # 瞄准参考点 y 减小 LOOK_AT[1] -= 0.01 elif key == b'Y': #...
dA_prev_pad=zero_pad(dA_prev, pad)foriinrange(m):#loop over the training examples#select ith training example from A_prev_pad and dA_prev_pada_prev_pad =A_prev_pad[i] da_prev_pad=dA_prev_pad[i]forhinrange(n_H):#loop over vertical axis of the output volumeforwinrange(n_W):...
A convolution layer transforms an input volume into an output volume of different size, as shown below. In this part, you will build every step of the convolution layer. You will first implement two helper functions: one for zero padding and the other for computing the convolution function ...
from .checkloss_hook import CheckInvalidLossHook from .ema import ExpMomentumEMAHook, LinearMomentumEMAHook(导入了构造的HOOKS) from .memory_profiler_hook import MemoryProfilerHook from .set_epoch_info_hook import SetEpochInfoHook from .sync_norm_hook import SyncNormHook from .sync_random_size_hook...
The simplest way to build a training loop is to use two nested for-loops, one for epochs and one for batches: 1 2 3 4 5 6 7 8 9 10 11 12 13 n_epochs = 100 batch_size = 10 for epoch in range(n_epochs): for i in range(0, len(X), batch_size): Xbatch = X[i:i+ba...
Although programming frameworks make convolutions easy to use, they remain one of the hardest concepts to understand in Deep Learning. A convolution layer transforms an input volume into an output volume of different size, as shown below.
The method described in this book for training and developing your own small-but-functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT. In addition, this book includes code for loading the weights of larger pretra...
FontSize ForceDirectedLayout ForEach ForEachLoop ForegroundColor ForeignKey ForeignKeyConstraintError ForeignKeyConstraintWarning ForeignKeyError ForeignKeyRelationship ForeignKeyWarning 分支 ForkNode FormatDocument FormatPageLeft FormatPageRight FormatSelection FormattingToolbar FormDigest FormInstance FormPostBodyParamete...
on_run_start() with suppress(StopIteration): # no loop to break at this level self.advance(kwargs) self._restarting = False return self.on_run_end() def on_run_start(self) -> None: # inject logic around the optimizer step for lightning_optimizer in self.trainer.strategy._lightning_...
A more detailed overview of the time integration loop is given below in Algorithm 2, discussed after introducing the step size control mechanisms. 2.2CFL-Based Step Size Control Explicit time integration methods for first-order hyperbolic conservation laws are subject to a CFL time step restriction ...