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ddf3da3
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Parent(s):
6547b74
Update app.py
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app.py
CHANGED
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@@ -55,7 +55,6 @@ IMG_CHANNELS = 3
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ATTENTION_DIM = 512 # size of dense layer in Attention
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VOCAB_SIZE = 20000
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-
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# We will override the default standardization of TextVectorization to preserve
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# "<>" characters, so we preserve the tokens for the <start> and <end>.
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def standardize(inputs):
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@@ -64,7 +63,6 @@ def standardize(inputs):
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inputs, r"[!\"#$%&\(\)\*\+.,-/:;=?@\[\\\]^_`{|}~]?", ""
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)
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# Choose the most frequent words from the vocabulary & remove punctuation etc.
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tokenizer = TextVectorization(
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max_tokens=VOCAB_SIZE,
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@@ -77,11 +75,6 @@ word_to_index = StringLookup(
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mask_token="", vocabulary=tokenizer.get_vocabulary()
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)
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# Lookup table: Index -> Word
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index_to_word = StringLookup(
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mask_token="", vocabulary=tokenizer.get_vocabulary(), invert=True
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)
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## Probabilistic prediction using the trained model
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def predict_caption(file):
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ATTENTION_DIM = 512 # size of dense layer in Attention
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VOCAB_SIZE = 20000
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# We will override the default standardization of TextVectorization to preserve
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# "<>" characters, so we preserve the tokens for the <start> and <end>.
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def standardize(inputs):
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inputs, r"[!\"#$%&\(\)\*\+.,-/:;=?@\[\\\]^_`{|}~]?", ""
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)
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# Choose the most frequent words from the vocabulary & remove punctuation etc.
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tokenizer = TextVectorization(
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max_tokens=VOCAB_SIZE,
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mask_token="", vocabulary=tokenizer.get_vocabulary()
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)
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## Probabilistic prediction using the trained model
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def predict_caption(file):
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