................................................................................ 7 3. Keras ― Backend Configuration .................................................................................. .......................................................................................... 20 backend module ......................................................................................... features:  Consistent, simple and extensible API.  Minimal structure - easy to achieve the result without any frills.  It supports multiple platforms and backends.  It is user friendly framework

process group for the distributed backend. Users can experiment with it by creating UCC as the default process group via: torch.distributed.init_process_group(backend="ucc", kwargs) or a side process group with any default via: torch.distributed.init_process_group(backend=any_backend, default_pg_kwargs) ucc_pg = torch.distributed.new_group(backend="ucc", ucc_pg_kwargs) Announcements ‣ Starting with the `antialiasing` argument for resizing operations in DALI 1.16.0 was changed to `True`, which could result in performance regressions on CPU-limited use cases. Set this argument to `False` to restore the

9 NASNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 14 后端 Backend 171 14.1 什么是「后端」？ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 metrics=['accuracy']) # 均方误差回归问题 model.compile(optimizer='rmsprop', loss='mse') # 自定义评估标准函数 import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) model.compile(optimizer='rmsprop', intermediate_layer_model.predict(data) 或者，你也可以构建一个 Keras 函数，该函数将在给定输入的情况下返回某个层的输出，例 如： from keras import backend as K # 以 Sequential 模型为例 get_3rd_layer_output = K.function([model.layers[0].input], [model.layers[3]

Example dummy function hard coded to return the same weather # In production, this could be your backend API or an external API def get_current_weather(location, unit='fahrenheit'): """Get the current

DPE White-box Distort-and-Recover Our result Expert-retouched Visual Comparison: MIT-Adobe FiveK Input JieP HDRNet DPE White-box Distort-and-Recover Our result Expert-retouched More Comparison Results: HDRNet DPE White-Box Distort-and-Recover Our result Limitaion Input Our result More Results Input JieP HDRNet DPE White-box Distort-and-Recover Our result Expert-retouched More Results Input JieP JieP HDRNet DPE White-box Distort-and-Recover Our result Expert-retouched More Results Input JieP HDRNet DPE White-box Distort-and-Recover Our result Expert-retouched More Results Input JieP HDRNet DPE

sine wave is a low precision representation which takes integer values in the range [0, 5]. As a result, the quantized wave requires low transmission bandwidth. Figure 2-3: Quantization of sine waves the low precision domain, because we are losing precision when going to a b-bit integer and as a result values which were close in the high precision domain might end up being mapped to the same value values, with the starting and endpoint defined, along with a step value. This returns the following result. [-10. -7.5 -5. -2.5 0. 2.5 5. 7.5 10. ] Now let’s quantize x. # Quantize the

10 份，每份长度为 1 result = torch.split(x, split_size_or_sections=1, dim=0) len(result) # 返回的列表为 10 个张量的列表 Out[8]: 10 可以查看切割后的某个张量的形状，它应是某个班级的所有成绩册数据，shape 为[35,8]， 例如： In [9]: result[0] # 查看第一个班级的成绩册张量 [10]: x = torch.randn([10,35,8]) # 自定义长度的切割，切割为 4 份，返回 4 个张量的列表 result result = torch.split(x, [4,2,2,2] , dim=0) len(result) Out[10]: 4 查看第一个张量的 shape，根据上述的切割方案，它应该包含了 4 个班级的成绩册，shape 预览版202112 预览版202112 5.2 数据统计 5 应为[4,35,8]，验证一下： In [10]: result[0] Out[10]: # torch.Size([4, 35, 8]) tensor([[[-6.95693314e-01, 3.01393479e-01, 1.33964568e-01, ...,]]]) 除了 split 函数可以实现张量分割外，PyTorch

which translates Spanish to English. This model translates “Estoy muy bien” to “I am fine”. This result can be used to train our original English to Spanish translation model. Let’s dig deeper into each on every sample results in a dataset 2x the original size. Two transformations applied separately result in a dataset 3x the original size. Can we apply N transformations to create a dataset Nx the size A 50 pixel point shift moves a pixel with initial coordinates to the final coordinates . As a result, the image is vertically shifted by 50px as shown in the top middle image in figure 3-6. Such a

in this example we have only one feature, being able to plot this gives a nice sanity-check on our result. (3) Finally, we’d like to make some predictions using the learned hypothesis. Use your model to about 50 iterations at your initial learning rate. In each iteration, calculate J(θ) and store the result in a vector J. After the last iteration, plot the J values against the number of the iteration. In num iterations = 1:50 J ( num iterations ) = %% Calculate your cost function here %% theta = %% Result of gradient descent update %% end % now p l o t J % t e c h n i c a l l y , the f i r s t J s

because it controls the number of unique words for which we learn embeddings. A small value for would result in loss of information because most of the words would get mapped to the OOV token. However, if information of the words. The words are all averaged to compute , and we would have got the same result for any other permutation of the words in the context. Hence the name Bag of Words for this family model averages all the embeddings in the input sequence to reduce each input to a single vector. The result is passed through a few dense layers and a softmax activation to generate an output tensor of size