wehub-resource-sync
83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
from __future__ import print_function
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import logging
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import random
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import numpy as np
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import sys
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from mla.datasets import load_nietzsche
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from mla.neuralnet import NeuralNet
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from mla.neuralnet.constraints import SmallNorm
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from mla.neuralnet.layers import Activation, Dense
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from mla.neuralnet.layers.recurrent import LSTM, RNN
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from mla.neuralnet.optimizers import RMSprop
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logging.basicConfig(level=logging.DEBUG)
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# Example taken from: https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py
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def sample(preds, temperature=1.0):
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# helper function to sample an index from a probability array
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preds = np.asarray(preds).astype("float64")
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preds = np.log(preds) / temperature
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exp_preds = np.exp(preds)
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preds = exp_preds / np.sum(exp_preds)
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probas = np.random.multinomial(1, preds, 1)
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return np.argmax(probas)
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X, y, text, chars, char_indices, indices_char = load_nietzsche()
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# Round the number of sequences for batch processing
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items_count = X.shape[0] - (X.shape[0] % 64)
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maxlen = X.shape[1]
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X = X[0:items_count]
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y = y[0:items_count]
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print(X.shape, y.shape)
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# LSTM OR RNN
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# rnn_layer = RNN(128, return_sequences=False)
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rnn_layer = LSTM(128, return_sequences=False)
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model = NeuralNet(
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layers=[
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rnn_layer,
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# Flatten(),
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# TimeStepSlicer(-1),
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Dense(X.shape[2]),
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Activation("softmax"),
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],
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loss="categorical_crossentropy",
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optimizer=RMSprop(learning_rate=0.01),
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metric="accuracy",
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batch_size=64,
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max_epochs=1,
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shuffle=False,
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)
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for _ in range(25):
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model.fit(X, y)
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start_index = random.randint(0, len(text) - maxlen - 1)
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generated = ""
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sentence = text[start_index : start_index + maxlen]
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generated += sentence
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print('----- Generating with seed: "' + sentence + '"')
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sys.stdout.write(generated)
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for i in range(100):
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x = np.zeros((64, maxlen, len(chars)))
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for t, char in enumerate(sentence):
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x[0, t, char_indices[char]] = 1.0
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preds = model.predict(x)[0]
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next_index = sample(preds, 0.5)
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next_char = indices_char[next_index]
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generated += next_char
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sentence = sentence[1:] + next_char
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sys.stdout.write(next_char)
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sys.stdout.flush()
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print()
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