77 lines
2.3 KiB
Python
77 lines
2.3 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, random
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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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BATCH_SIZE = 200
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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 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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def input_generator(nsamples):
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def generate_line():
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inline = []; outline = []
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for _ in range(nsamples):
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c = random.choice(characters)
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expected = c.lower() if c in string.ascii_letters else ' '
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inline.append(c); outline.append(expected)
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return ''.join(inline), ''.join(outline)
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while True:
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input_data, expected = generate_line()
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data_in = encode_one_hot(input_data)
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data_out = encode_one_hot(expected)
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yield data_in, data_out
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def train(model):
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model.compile(loss='categorical_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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input_gen = input_generator(BATCH_SIZE)
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validation_gen = input_generator(BATCH_SIZE)
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model.fit_generator(input_gen,
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epochs = 50, workers=1,
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steps_per_epoch = 20,
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validation_data = validation_gen,
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validation_steps = 10)
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model = build_model()
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model.summary()
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train(model)
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input("Network has been trained. Press <Enter> to run program.")
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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) |