NormalisingAndReshaping.py

# No. of examples
n_patterns = len(network_input)
print(n_patterns)

# Desired shape for LSTM
network_input = np.reshape(network_input, (n_patterns, sequence_length, 1))
print(network_input.shape)

normalised_network_input = network_input/float(n_vocab)

# Network output are the classes, encode into one hot vector
network_output = np_utils.to_categorical(network_output)

network_output.shape   #Output: (60398, 359)

print(normalised_network_input.shape)  #Output: (60398, 100, 1)
print(network_output.shape)            #Output: (60398, 359)