where their relative closeness in the euclidean space on the plot denotes their similarity. We can verify this ourselves, by noting that the dog and cat are represented close to each other. A snake is closer input. This can be useful to verify that the embedding table captures semantic relationships between the words as intended. For example, in Figure 4-10, we visually verify that the closest points to ‘king’ tensor should be (vocab_size, embedding_dim), i.e., (5000, 250). The following code snippet will verify that. embedding_dim = 250 # The shape of the word2vec_embeddings would be (vocabulary_size, 250)

much at once. We would recommend the readers to try out various pruning schedules and empirically verify the schedule that works the best. Finally, our approach towards sparsity so far has been to operate can rely on clustering to put its centroids where the data is. Next, we ran some calculations to verify how the reconstruction error changes as we increase the number of clusters ( ). Figure 5-7 (b) shows our model. The clustered model gets an accuracy similar to the baseline model (and we’ll shortly verify the compression gains). However, given that we computed the clusters earlier from first principles

e., α (and thus ω and b which are calculated according to α), satisfy all the KKT conditions. To verify if the KKT conditions holds for these parameters, we introduce some corollaries according to the > H α+ 2 , L ≤ α+ 2 ≤ H L, α+ 2 < L In each iteration, we have to update b accordingly so as to verify if the convergence criterion is satisfied. According to Corollary 3, when 0 < α+ 1 < C, we have

successfully installed, then you could see the version of PyTorch by the following command: 5. Verify PyTorch is able to use GPUs. The output should be True if PyTorch is able to use GPUs. cat ~/

H L, α+ 2 < L Feng Li (SDU) SVM December 28, 2021 78 / 82 SMO Algorithm (Contd.) Updating b to verify if the convergence criterion is satisfied When 0 < α+ 1 < C, b+ 1 = −E1 − y (1)K11(α+ 1 − α1) −

the quantize() function with the parameters wmin, wmax and b = 8. Let’s print out the weights to verify it worked as expected. Note that the quantized weights are 0 and 255 respectively for the smallest

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sha1_hash: return fname # 命中缓存 print(f'正在从{url}下载{fname}...') r = requests.get(url, stream=True, verify=True) with open(fname, 'wb') as f: f.write(r.content) return fname 我们还需实现两个实用函数：一个将下载并解压缩一个z