vector.set_shape((None, MAX_SEQ_LEN, WORD2VEC_LEN)) return vector, label Notice that in the 2nd for loop above, we limit the representation to the first MAX_SEQ_LEN words in the sequence. We are ready to similar to typical human behavior when making a big decision (a big purchase or an important life event). We discuss with friends and family to decide whether it is a good decision. We rely on their perspectives

” ) train_loop ( train_dataloader , model , loss_function , optimizer ) test_loop ( test_dataloader , model , loss_function ) print ( ”Done ! ” ) 然后就是训练和测试的程序，训练一轮的程序如下： def train_loop ( dataloader 个数据。因为每个 batch 个 数为 64 个数据，因此训练集要训练 938 次，我们每 100 次输出一下。 测试集的程序如下： Chapter 2. 构建神经网络 13 def test_loop ( dataloader , model , loss_function ) : s i z e = len ( dataloader . dataset ) # 10000 print ( ”Epoch␣{ t+1}\n−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−” ) path = ’ ./ model ’ + s t r ( t ) +’ . pth ’ train_loop ( train_dataloader , model , loss_function , optimizer ) state = { ’ model ’ : model . state_dict

Save/load models Prerequisites ● We assume you are already familiar with… 1. Python3 ■ if-else, loop, function, file IO, class, ... ■ refs: link1, link2, link3 2. Deep Learning Basics ■ Prof. Lee’s construct model and move to device (cpu/cuda) set loss function set optimizer Neural Network Training Loop for epoch in range(n_epochs): model.train() for x, y in tr_set: optimizer.zero_grad() compute loss compute gradient (backpropagation) update model with optimizer Neural Network Validation Loop model.eval() total_loss = 0 for x, y in dv_set: x, y = x.to(device), y.to(device) with

take between 30 and 100 iterations. You can either run the loop for a preset maximum number of iterations, or you can decide to terminate the loop when the locations of the means are no longer changing by

with January 2020 updates ‣ Initial support for channel-last layout for convolutions ‣ Support for loop unrolling and vectorized loads and stores in TensorIterator ‣ Support for input activations with MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL

(conceptual or physical) random experiment Event A is a subset of the sample space S P(A) is the probability that event A happens It is a function that maps the event A onto the interval [0, 1]. P(A) is also also called the probability measure of A Kolmogorov axioms Non-negativity: p(A) ≥ 0 for each event A P(S) = 1 σ-additivity: For disjoint events {Ai}i such that Ai � Aj = ∅ for ∀i ̸= j P( ∞ � i=1 Ai) Conditional Probability Definition of conditional probability: Fraction of worlds in which event A is true given event B is true P(A | B) = P(A, B) P(B) , P(A, B) = P(A | B)P(B) Corollary: The chain rule

Experimental support Experimental support Supported planned post 2.0 Supported Custom training loop Experimental support Experimental support Support planned post 2.0 Support planned post

P(B) (1) where P(A | B) is the conditional probability of event A given event B happens, P(B | A) is the conditional probability of event B given A is true, and P(A) and P(B) are probability of observing

we’d like to shuffle it or not. That’s it! • Our loader will behave like an iterator, so we can loop over it and fetch a different mini-batch every time. Dataloader (example) • Sample Code in Practice:

variable at a time. Although it is possible to work without it, you would have to introduce a for-loop either within the function, or outside it. This is crucial for deep learning applications which frequently