import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
#https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/


# load ascii text and covert to lowercase
filename = "wonderland.txt"
raw_text = open(filename).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))
n_chars = len(raw_text)
n_vocab = len(chars)
print ("Total Characters: "+ str(n_chars))
print ("Total Vocab: "+str(n_vocab))





# prepare the dataset of input to output pairs encoded as integers
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: "+ str(n_patterns))





# [samples, time steps, features] expected by an LSTM network.
# reshape X to be [samples, time steps, features]
X = numpy.reshape(dataX, (n_patterns, seq_length, 1))
# normalize
X = X / float(n_vocab)
# one hot encode the output variable
y = np_utils.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'))





filename = "weights-improvement-03-2.6934.hdf5"#file name needs to be replaced
model.load_weights(filename)
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]

model.fit(X, y, epochs=20, batch_size=128, callbacks=callbacks_list)



































import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
#https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/


# load ascii text and covert to lowercase
filename = "wonderland.txt"
raw_text = open(filename).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))
n_chars = len(raw_text)
n_vocab = len(chars)
print ("Total Characters: "+ str(n_chars))
print ("Total Vocab: "+str(n_vocab))





# prepare the dataset of input to output pairs encoded as integers
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: "+ str(n_patterns))





# [samples, time steps, features] expected by an LSTM network.
# reshape X to be [samples, time steps, features]
X = numpy.reshape(dataX, (n_patterns, seq_length, 1))
# normalize
X = X / float(n_vocab)
# one hot encode the output variable
y = np_utils.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'))





filename = "weights-improvement-03-2.6934.hdf5"#file name needs to be replaced
model.load_weights(filename)
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]

model.fit(X, y, epochs=20, batch_size=128, callbacks=callbacks_list)



































#https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ # load ascii text and covert to lowercase # create mapping of unique chars to integers # prepare the dataset of input to output pairs encoded as integers # [samples, time steps, features] expected by an LSTM network. # reshape X to be [samples, time steps, features] # normalize # one hot encode the output variable # define the LSTM model # define the checkpoint