Text Generation LSTM

# Small LSTM Network to Generate Text for Alice in Wonderland
import numpy
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import LSTM
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.utils import to_categorical
# load ascii text and covert to lowercase
filename = "wonderland.txt"
raw_text = open(filename, 'r', encoding='utf-8').read()
raw_text = raw_text.lower()
# create mapping of unique chars to integers
chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
# summarize the loaded data
n_chars = len(raw_text)
n_vocab = len(chars)
print "Total Characters: ", n_chars
print "Total Vocab: ", n_vocab
# prepare the dataset of input to output pairs encoded as integers
seq_length = 100
prediction_length = 2
dataX = []
dataY = []
encoded_text = [char_to_int[char] for char in raw_text]
for i in range(0, n_chars - seq_length, 1):
    seq_in = encoded_text[i:i + seq_length]
    seq_out = encoded_text[i + seq_length: i + seq_length + prediction_length]
    dataX.append(seq_in)
    dataY.append(seq_out)
n_patterns = len(dataX)
print "Total Patterns: ", n_patterns
# reshape X to be [samples, time steps, features]
X = numpy.reshape(dataX, (-1, seq_length, prediction_length))
# normalize
X = X / float(n_vocab)
# one hot encode the output variable
y = to_categorical(dataY)
# define the LSTM model
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
# define the checkpoint
filepath="weights-improvement-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(X, y, epochs=20, batch_size=128, callbacks=callbacks_list)