Python - Keras LSTM Network

import numpy as np
import numpy
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
from keras.layers.core import Dense, Activation,Dropout  
from keras.callbacks import ModelCheckpoint
import sys

filename = "quantum.txt"
raw_text = open(filename).read()
raw_text = raw_text.lower()
chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
n_chars = len(raw_text)
n_vocab = len(chars)
print("Total Characters: ", n_chars)
print("Total Vocab: ", n_vocab)

seq_length = 100
dataX = []
dataY = []
for i in range(0, n_chars - seq_length, 1):
	seq_in = raw_text[i:i + seq_length]
	seq_out = raw_text[i + seq_length]
	dataX.append([char_to_int[char] for char in seq_in])
	dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)
print("Total Patterns: ", n_patterns)

X = numpy.reshape(dataX, (n_patterns, 1, 100))
X = X / float(n_vocab)
y = np_utils.to_categorical(dataY)
y=dataY
y = np_utils.to_categorical(dataY)

model = Sequential()
model.add(LSTM(100, 10, return_sequences=True))  
model.add(LSTM(10, 10))  
model.add(Dense(10, 42))
model.add(Activation("softmax"))  
model.compile(loss="categorical_crossentropy", optimizer="rmsprop")  
model.fit(X, y, nb_epoch=10, batch_size=600)

int_to_char = dict((i, c) for i, c in enumerate(chars))

pattern = dataX[1001]
data2=pattern[0:1000]
print("Seed:")
print("\"", ''.join([int_to_char[value] for value in data2]), "\"")

s=[]
for i in range(40):
    pattern = dataX
    f=20+i
    g=f+300
    len(pattern[f:g])
    h=np.array(pattern[f:g])
    x=np.reshape(h,(300,1,100))
    x = np.array(x) / float(n_vocab)
    prediction = model.predict(x, verbose=0)
    index = numpy.argmax(prediction[i])
    result = int_to_char[index]
    s.append(result)
    print("".join(s))

### Code adapted from: http://machinelearningmastery.com for keras 0.2.0