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[High-Level-API] Rewrite Chapter 5 Personalized Recommendation in Book to use new Flui… #526
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daming-lu
reviewed
May 31, 2018
05.recommender_system/README.md
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| Our program starts with importing necessary packages and initializes some global variables: |
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starts with importing necessary packages and initializing
05.recommender_system/README.md
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| ``` | ||
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| Movie title, a sequence of words represented by an integer word index sequence, will be feed into a `sequence_conv_pool` layer, which will apply convolution and pooling on time dimension. Because pooling is done on time dimension, the output will be a fixed-length vector regardless the length of the input sequence. | ||
| Movie title, which is a sequence of words represented by an integer word index sequence, will be feed into a `sequence_conv_pool` layer, which will apply convolution and pooling on time dimension. Because pooling is done on time dimension, the output will be a fixed-length vector regardless the length of the input sequence. |
05.recommender_system/README.md
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| Finally, we can use cosine similarity to calculate the similarity between user characteristics and movie features. | ||
| Finally, we can define a `inference_program` that use cosine similarity to calculate the similarity between user characteristics and movie features. |
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an inference_program that uses
05.recommender_system/README.md
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| Before jumping into creating a training module, algorithm setting is also necessary. Here we specified Adam optimization algorithm via `paddle.optimizer`. | ||
| Next we define data feeders for test and train. The feeder reads a `BATCH_SIZE` of data each time and feed them to the training/testing process. | ||
| `paddle.dataset.movielens.train` will yield records during each pass, after shuffling, a batch input of `buf_size` is generated for training. |
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the sentence is not clear. Plus, buf_size is larger than BATCH_SIZE. I think the logic is reversed...
05.recommender_system/README.md
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| `paddle.dataset.movielens.train` will yield records during each pass, after shuffling, a batch input is generated for training. | ||
| Create a trainer that takes `train_program` as input and specifies optimizer. |
05.recommender_system/README.md
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| if step % 100 == 0: # every 100 batches, update cost plot | ||
| cost_ploter.plot() | ||
| Use create_lod_tensor(data, lod, place) API to generate LoD Tensor, where `data` is a list of sequences of index numbers, `lod` is the level of detail (lod) info associated with `data`. | ||
| For example, data = [[10, 2, 3], [2, 3]] means that it contains two sequences of indexes, of length 3 and 2, respectively. |
05.recommender_system/README.md
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| Finally, we can invoke `trainer.train` to start training: | ||
| ### Infer | ||
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| Now we can infer with inputs that matched with the yield records that we provide in `feed_order` during training. |
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This sentence is not clear. Maybe break it into two?
jetfuel
reviewed
May 31, 2018
| @@ -98,13 +98,13 @@ Figure 4. A hybrid recommendation model. | |||
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| We use the [MovieLens ml-1m](http://files.grouplens.org/datasets/movielens/ml-1m.zip) to train our model. This dataset includes 10,000 ratings of 4,000 movies from 6,000 users to 4,000 movies. Each rate is in the range of 1~5. Thanks to GroupLens Research for collecting, processing and publishing the dataset. | |||
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| `paddle.v2.datasets` package encapsulates multiple public datasets, including `cifar`, `imdb`, `mnist`, `moivelens` and `wmt14`, etc. There's no need for us to manually download and preprocess `MovieLens` dataset. | |||
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I told Nicki the same. He really has a keen sight 👀
| ``` | ||
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| Finally, we can invoke `trainer.train` to start training: | ||
| ### Infer |
jetfuel
changed the title
daming-lu
approved these changes
Jun 5, 2018
| }, | ||
| return_numpy=False) | ||
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| print("infer results: ", np.array(results[0])) |
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Can we show a comparison between prediction and the real data? For example, user 23::M::35::0::90049 rated movie 2278::Ronin (1998)::Action|Crime|Thriller a 4.0 score. Our prediction is 3.458
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Good suggestion, i think it would be helpful
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…d API
I will add plot in next PR