karpathy · November 14, 2024 05:03 · BlockListed · Aug 7, 2019 · mkffl · Sep 22, 2019
diff --git a/min-char-rnn.py b/min-char-rnn.py
 """
 Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
 BSD License
 """
 import numpy as np

 # data I/O
 data = open('input.txt', 'r').read() # should be simple plain text file
 chars = list(set(data))
 data_size, vocab_size = len(data), len(chars)
 print 'data has %d characters, %d unique.' % (data_size, vocab_size)
 char_to_ix = { ch:i for i,ch in enumerate(chars) }
 ix_to_char = { i:ch for i,ch in enumerate(chars) }

 # hyperparameters
 hidden_size = 100 # size of hidden layer of neurons
 seq_length = 25 # number of steps to unroll the RNN for
 learning_rate = 1e-1

 # model parameters
 Wxh = np.random.randn(hidden_size, vocab_size)*0.01 # input to hidden
 Whh = np.random.randn(hidden_size, hidden_size)*0.01 # hidden to hidden
 Why = np.random.randn(vocab_size, hidden_size)*0.01 # hidden to output
 bh = np.zeros((hidden_size, 1)) # hidden bias
 by = np.zeros((vocab_size, 1)) # output bias

 def lossFun(inputs, targets, hprev):
  """
  inputs,targets are both list of integers.
  hprev is Hx1 array of initial hidden state
  returns the loss, gradients on model parameters, and last hidden state
  """
  xs, hs, ys, ps = {}, {}, {}, {}
  hs[-1] = np.copy(hprev)
  loss = 0
  # forward pass
  for t in xrange(len(inputs)):
    xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation
    xs[t][inputs[t]] = 1
    hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state
    ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars
    ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars
    loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)
  # backward pass: compute gradients going backwards
  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 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here
    dWhy += np.dot(dy, hs[t].T)
    dby += dy
    dh = np.dot(Why.T, dy) + dhnext # backprop into h
    dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity
    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) # clip to mitigate exploding gradients
  return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]

 def sample(h, seed_ix, n):
  """ 
  sample a sequence of integers from the model 
  h is memory state, seed_ix is seed letter for first time step
  """
  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)
  return ixes

 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) # memory variables for Adagrad
 smooth_loss = -np.log(1.0/vocab_size)*seq_length # loss at iteration 0
 while True:
  # prepare inputs (we're sweeping from left to right in steps seq_length long)
  if p+seq_length+1 >= len(data) or n == 0: 
    hprev = np.zeros((hidden_size,1)) # reset RNN memory
    p = 0 # go from start of data
  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]]

  # sample from the model now and then
  if n % 100 == 0:
    sample_ix = sample(hprev, inputs[0], 200)
    txt = ''.join(ix_to_char[ix] for ix in sample_ix)
    print '----\n %s \n----' % (txt, )

  # forward seq_length characters through the net and fetch gradient
  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) # print progress
  
  # perform parameter update with Adagrad
  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 # move data pointer
  n += 1 # iteration counter
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)
	print 'data has %d characters, %d unique.' % (data_size, vocab_size)
	char_to_ix = { ch:i for i,ch in enumerate(chars) }
	ix_to_char = { i:ch for i,ch in enumerate(chars) }

	# hyperparameters
	hidden_size = 100 # size of hidden layer of neurons
	seq_length = 25 # number of steps to unroll the RNN for
	learning_rate = 1e-1

	# model parameters
	Wxh = np.random.randn(hidden_size, vocab_size)*0.01 # input to hidden
	Whh = np.random.randn(hidden_size, hidden_size)*0.01 # hidden to hidden
	Why = np.random.randn(vocab_size, hidden_size)*0.01 # hidden to output
	bh = np.zeros((hidden_size, 1)) # hidden bias
	by = np.zeros((vocab_size, 1)) # output bias

	def lossFun(inputs, targets, hprev):
	"""
	inputs,targets are both list of integers.
	hprev is Hx1 array of initial hidden state
	returns the loss, gradients on model parameters, and last hidden state
	"""
	xs, hs, ys, ps = {}, {}, {}, {}
	hs[-1] = np.copy(hprev)
	loss = 0
	# forward pass
	for t in xrange(len(inputs)):
	xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation
	xs[t][inputs[t]] = 1
	hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state
	ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars
	ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars
	loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)
	# backward pass: compute gradients going backwards
	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 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here
	dWhy += np.dot(dy, hs[t].T)
	dby += dy
	dh = np.dot(Why.T, dy) + dhnext # backprop into h
	dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity
	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) # clip to mitigate exploding gradients
	return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]

	def sample(h, seed_ix, n):
	"""
	sample a sequence of integers from the model
	h is memory state, seed_ix is seed letter for first time step
	"""
	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)
	return ixes

	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) # memory variables for Adagrad
	smooth_loss = -np.log(1.0/vocab_size)*seq_length # loss at iteration 0
	while True:
	# prepare inputs (we're sweeping from left to right in steps seq_length long)
	if p+seq_length+1 >= len(data) or n == 0:
	hprev = np.zeros((hidden_size,1)) # reset RNN memory
	p = 0 # go from start of data
	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]]

	# sample from the model now and then
	if n % 100 == 0:
	sample_ix = sample(hprev, inputs[0], 200)
	txt = ''.join(ix_to_char[ix] for ix in sample_ix)
	print '----\n %s \n----' % (txt, )

	# forward seq_length characters through the net and fetch gradient
	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) # print progress

	# perform parameter update with Adagrad
	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 # move data pointer
	n += 1 # iteration counter