Prince Grover groverpr
groverpr
/ load_base_model
Created
April 13, 2020 04:19
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| def load_base_model(model_path, epoch, ctx, layer_name=None, n_inputs=2): | |
| """ Loads the model from given model path | |
| and returns a subnetwork that gives output from layer_name | |
| """ | |
| net = gluon.nn.SymbolBlock.imports( | |
| model_path + "-symbol.json", | |
| ['data%i' % i for i in range(n_inputs)], | |
| model_path + "-%.4d.params" % epoch, | |
| ctx=ctx, | |
| ) |
groverpr
/ generate_tsne
Created
March 29, 2020 05:47
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| few_words = ['great', 'excellent', 'best', 'perfect', 'wonderful', 'well', | |
| 'fun', 'love', 'amazing', 'also', 'enjoyed', 'favorite', 'it', | |
| 'and', 'loved', 'highly', 'bit', 'job', 'today', 'beautiful', | |
| 'you', 'definitely', 'superb', 'brilliant', 'world', 'liked', | |
| 'still', 'enjoy', 'life', 'very', 'especially', 'see', 'fantastic', | |
| 'both', 'shows', 'good', 'may', 'terrific', 'heart', 'classic', | |
| 'will', 'enjoyable', 'beautifully', 'always', 'true', 'perfectly', | |
| 'surprised', 'think', 'outstanding', 'most', | |
| 'bad', 'worst', 'awful', 'waste', 'boring', 'poor', 'terrible', |
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| def train(network, | |
| train_data, | |
| holdout_data, | |
| loss, | |
| epochs, | |
| ctx, | |
| lr=1e-2, | |
| wd=1e-5, | |
| optimizer='adam'): | |
groverpr
/ use_custom_net
Created
March 22, 2020 02:09
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| # Preparation of network arguments | |
| ctx = [mx.gpu(0)] # use a GPU | |
| tt = transformer_pipe.named_steps['token2index'] # to get token to integer map | |
| max_idx = max(tt.tok2idx.values())+1 # size of vocabulary of all tokens in training data | |
| tok_embed_dim = 64 # embedding size of each token | |
| review_embed_dim = 50 # embedding size of hidden state in GRU | |
| input_output_embed_map = {"token_embed": (max_idx, tok_embed_dim), | |
| "hidden_embed": (None, review_embed_dim)} | |
groverpr
/ custom_net
Created
March 22, 2020 01:52
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| class CustomSeqNet(gluon.nn.HybridBlock): | |
| """ | |
| Custom defined network for sequence data that is used to predict a binary label. | |
| """ | |
| def __init__(self, input_output_embed_map, dense_sizes=[100], dropouts=[0.2], activation="relu"): | |
| """ | |
| input_output_embed_map: {"token_embed": (max_tok_idx, tok_embed_dim), "hidden_embed": (,hidden_embed_dim))} | |
| """ | |
| self.dense_sizes = dense_sizes # list of output dimension of dense layers |
groverpr
/ mxnet_dataset_dataloader
Created
March 12, 2020 08:01
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| def get_dataloader(dataset, | |
| dataset_type="train", # valid/test | |
| batch_size=256, | |
| bucket_num=5, | |
| shuffle=True, # true for training | |
| num_workers=1): | |
| # Batchify function appends the length of each sequence to feed as addtional input | |
| combined_batchify_fn = nlp.data.batchify.Tuple( | |
| nlp.data.batchify.Pad(axis=0, ret_length=True), |
groverpr
/ transfer_learn_mxnet_notebook.ipynb
Created
February 25, 2020 02:02
Full notebook for transfer learning tutorial using MXNet
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groverpr
/ transformers_sklearn_eg
Last active
February 25, 2020 01:46
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| class Tokenize(BaseEstimator, TransformerMixin): | |
| """ | |
| Takes in pandas series and applies tokenization on each row based on given split pattern. | |
| """ | |
| def __init__(self, split_pat=f"([{string.punctuation}])"): | |
| self.split_pat = split_pat # re pattern used to split string to tokens. default splits over any string punctuation | |
| def tokenize(self, s): | |
| """ Tokenize string """ | |
| re_tok = re.compile(self.split_pat) |
groverpr
/ transformers_sklearn
Created
February 25, 2020 01:42
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| # Transformers present in Pipeline work in series. 1->2->3->4 | |
| transformer_pipe = Pipeline(steps=[ | |
| ("null_impute", NullImputer(strategy="constant", fill_value="null")), | |
| ("lower_case", LowerCaser()), | |
| ("tokenize", Tokenize(f"([{string.punctuation}])")), | |
| ("token2index", Tok2Idx()) | |
| ]) | |
| X_train_transformed = transformer_pipe.fit_transform(X_train) # fit and transform on train data | |
| X_valid_transformed = transformer_pipe.transform(X_valid) |
groverpr
/ comparisons.ipynb
Created
July 1, 2019 09:27
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