72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
from keras.models import Sequential
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from keras.layers import Dense
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import numpy as np
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import sys, os, string
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characters = string.printable
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char_indices = dict((c, i) for i, c in enumerate(characters))
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indices_char = dict((i, c) for i, c in enumerate(characters))
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INPUT_VOCAB_SIZE = len(characters)
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def encode_one_hot(line):
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x = np.zeros((len(line), INPUT_VOCAB_SIZE))
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for i, c in enumerate(line):
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if c in characters:
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index = char_indices[c]
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else:
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index = char_indices[' ']
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x[i][index] = 1
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return x
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def decode_one_hot(x):
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s = []
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for onehot in x:
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one_index = np.argmax(onehot)
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s.append(indices_char[one_index])
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return ''.join(s)
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def normalization_layer_set_weights(n_layer):
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wb = []
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w = np.zeros((INPUT_VOCAB_SIZE, INPUT_VOCAB_SIZE), dtype=np.float32)
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b = np.zeros((INPUT_VOCAB_SIZE), dtype=np.float32)
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# Let lower case letters go through
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for c in string.ascii_lowercase:
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i = char_indices[c]
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w[i, i] = 1
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# Map capitals to lower case
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for c in string.ascii_uppercase:
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i = char_indices[c]
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il = char_indices[c.lower()]
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w[i, il] = 1
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# Map all non-letters to space
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sp_idx = char_indices[' ']
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for c in [c for c in list(string.printable) if c not in list(string.ascii_letters)]:
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i = char_indices[c]
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w[i, sp_idx] = 1
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wb.append(w)
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wb.append(b)
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n_layer.set_weights(wb)
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return n_layer
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def build_model():
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# Normalize characters using a dense layer
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model = Sequential()
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dense_layer = Dense(INPUT_VOCAB_SIZE,
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input_shape=(INPUT_VOCAB_SIZE,),
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activation='softmax')
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model.add(dense_layer)
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return model
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model = build_model()
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model.summary()
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normalization_layer_set_weights(model.layers[0])
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with open(sys.argv[1]) as f:
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for line in f:
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if line.isspace(): continue
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batch = encode_one_hot(line)
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preds = model.predict(batch)
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normal = decode_one_hot(preds)
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print(normal) |