rnn_midi.py

import numpy as np

data = open('raw_music.txt', 'r').read()
chars = list(set(data)) 
data_size, vocab_size = len(data), len(chars)

char_to_ix = { ch:i for i,ch in enumerate(chars)}
ix_to_char = { i:ch for i, ch in enumerate(chars)}
vector_for_char_a = np.zeros((vocab_size, 1))
vector_for_char_a[char_to_ix['a']] = 1

hidden_size = 100
seq_length = 25
learning_rate = 1e-1

Wxh = np.random.randn(hidden_size, vocab_size) * 0.01
Whh = np.random.randn(hidden_size, hidden_size) * 0.01
Why = np.random.randn(vocab_size, hidden_size) * 0.01
bh = np.zeros((hidden_size, 1))
by = np.zeros((vocab_size, 1))

def lossFun(inputs, targets, hprev):
  xs, hs, ys, ps, = {}, {}, {}, {} 
  xs, hs, ys, ps = {}, {}, {}, {}
  hs[-1] = np.copy(hprev)
  loss = 0
  for t in xrange(len(inputs)):
    xs[t] = np.zeros((vocab_size,1))                                                                                                               
    xs[t][inputs[t]] = 1
    hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh)                                                                                                     
    ys[t] = np.dot(Why, hs[t]) + by                                                                                                      
    ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t]))                                                                                          
    loss += -np.log(ps[t][targets[t],0])                                                                                            
  dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)
  dbh, dby = np.zeros_like(bh), np.zeros_like(by)
  dhnext = np.zeros_like(hs[0])
  for t in reversed(xrange(len(inputs))):
    dy = np.copy(ps[t])
    dy[targets[t]] -= 1 
    dWhy += np.dot(dy, hs[t].T)
    dby += dy
    dh = np.dot(Why.T, dy) + dhnext                                                                                                            
    dhraw = (1 - hs[t] * hs[t]) * dh                                                                                     
    dbh += dhraw 
    dWxh += np.dot(dhraw, xs[t].T)
    dWhh += np.dot(dhraw, hs[t-1].T)
    dhnext = np.dot(Whh.T, dhraw) 
  for dparam in [dWxh, dWhh, dWhy, dbh, dby]:
    np.clip(dparam, -5, 5, out=dparam) 
  return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]

def sample(h, seed_ix, n):
  x = np.zeros((vocab_size, 1))
  x[seed_ix] = 1
  ixes = []
  for t in xrange(n):
    h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)
    y = np.dot(Why, h) + by
    p = np.exp(y) / np.sum(np.exp(y))
    ix = np.random.choice(range(vocab_size), p=p.ravel())
    x = np.zeros((vocab_size, 1))
    x[ix] = 1
    ixes.append(ix)

  txt = ''.join(ix_to_char[ix] for ix in ixes)
hprev = np.zeros((hidden_size,1)) 

p=0  
inputs = [char_to_ix[ch] for ch in data[p:p+seq_length]]
targets = [char_to_ix[ch] for ch in data[p+1:p+seq_length+1]]

n, p = 0, 0
mWxh, mWhh, mWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)
mbh, mby = np.zeros_like(bh), np.zeros_like(by)                                                                                                             
smooth_loss = -np.log(1.0/vocab_size)*seq_length                                                                                                             
while n<=200000:
  if p+seq_length+1 >= len(data) or n == 0:
    hprev = np.zeros((hidden_size,1))                                                                                                                                    
    p = 0                                                                                                                                                              
  inputs = [char_to_ix[ch] for ch in data[p:p+seq_length]]
  targets = [char_to_ix[ch] for ch in data[p+1:p+seq_length+1]]

  loss, dWxh, dWhh, dWhy, dbh, dby, hprev = lossFun(inputs, targets, hprev)
  smooth_loss = smooth_loss * 0.999 + loss * 0.001

  if n % 100 == 0:
    print 'iter %d, loss: %f' % (n, smooth_loss) 
    if n % 10000 == 0:
        sample(hprev, inputs[0], 200)

  for param, dparam, mem in zip([Wxh, Whh, Why, bh, by],
                                [dWxh, dWhh, dWhy, dbh, dby],
                                [mWxh, mWhh, mWhy, mbh, mby]):
    mem += dparam * dparam
    param += -learning_rate * dparam / np.sqrt(mem + 1e-8) # adagrad update                                                                                                                   

  p += seq_length                                                                                                                                                      
  n += 1 

print("SAVING!!!!!! ")
outfile = open('rnn_midi_25_100_200000.dat', 'w')
np.savez(outfile, chars=chars, hprev=hprev, Wxh=Wxh, Whh=Whh, bh=bh,by=by, Why=Why, vocab_size=np.array([vocab_size]))