seq_predictor_LSTM.txt

 LSTM to learn three-char time steps to one-char mapping
import numpy
from keras.models import Sequential
27.4. LSTM for a Time Step Window to One-Char Mapping 219
from keras.layers import Dense
from keras.layers import LSTM
from keras.utils import np_utils
# fix random seed for reproducibility
numpy.random.seed(7)
# define the raw dataset
alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# create mapping of characters to integers (0-25) and the reverse
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
int_to_char = dict((i, c) for i, c in enumerate(alphabet))
# prepare the dataset of input to output pairs encoded as integers
seq_length = 3
dataX = []
dataY = []
for i in range(0, len(alphabet) - seq_length, 1):
  seq_in = alphabet[i:i + seq_length]
  seq_out = alphabet[i + seq_length]
  dataX.append([char_to_int[char] for char in seq_in])
  dataY.append(char_to_int[seq_out])
  print(seq_in, '->', seq_out)
# reshape X to be [samples, time steps, features]
X = numpy.reshape(dataX, (len(dataX), seq_length, 1))
# normalize
X = X / float(len(alphabet))
# one hot encode the output variable
y = np_utils.to_categorical(dataY)
# create and fit the model
model = Sequential()
model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2])))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=500, batch_size=1, verbose=2)
# summarize performance of the model
scores = model.evaluate(X, y, verbose=0)
print("Model Accuracy: %.2f%%" % (scores[1]*100))
# demonstrate some model predictions
for pattern in dataX:
  x = numpy.reshape(pattern, (1, len(pattern), 1))
  x = x / float(len(alphabet))
  prediction = model.predict(x, verbose=0)
  index = numpy.argmax(prediction)
  result = int_to_char[index]
  seq_in = [int_to_char[value] for value in pattern]
  print(seq_in, "->", result)