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@rhaps0dy
Created November 21, 2015 18:37
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NaNoGenMo character-based RNN, based on Karpathy's blog and code.
#!/usr/bin/env python2
import tensorflow as tf
import numpy as np
import time
# Hyperparameters
learning_rate = 1e-1
n_hidden = 100
seq_length = 25
n_show = 2500 // seq_length
f = open('corpus.txt', 'r')
corpus = f.read()
f.close()
charset = list(set(corpus))
char_to_ix = { ch:i for i,ch in enumerate(charset) }
ix_to_char = { i:ch for i,ch in enumerate(charset) }
n_chars = len(charset)
print "Corpus has %d characters, %d unique" % (len(corpus), n_chars)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
Wxh = weight_variable([n_chars, n_hidden])
Whh = weight_variable([n_hidden, n_hidden])
bh = bias_variable([1, n_hidden])
Why = weight_variable([n_hidden, n_chars])
by = bias_variable([1, n_chars])
x = list(tf.placeholder("float", shape=[1, n_chars]) for _ in xrange(seq_length))
y_ = list(tf.placeholder("float", shape=[1, n_chars]) for _ in xrange(seq_length))
h0 = tf.placeholder("float", shape=[1, n_hidden])
h = h0
prob = range(seq_length)
for i in xrange(seq_length):
h = tf.nn.tanh(tf.matmul(x[i], Wxh) + tf.matmul(h, Whh) + bh)
y = tf.matmul(h, Why) + by
prob[i] = tf.nn.softmax(y)
loss = -tf.reduce_sum(y_[0] * tf.log(prob[0]))
for i in xrange(1, seq_length):
loss += -tf.reduce_sum(y_[i] * tf.log(prob[i]))
train_step = tf.train.AdagradOptimizer(learning_rate).minimize(loss)
print time.clock()
prev_time = time.clock()
h_prev = [[0]*n_hidden]
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
p = 0
while p < len(corpus):
inputs = corpus[p:p+seq_length]
targets = corpus[p+1:p+seq_length+1]
inp = dict()
i = 0
for c, t in zip(inputs, targets):
inp[x[i]] = [[(1 if j==char_to_ix[c] else 0) for j in xrange(n_chars)]]
inp[y_[i]] = [[(1 if j==char_to_ix[t] else 0) for j in xrange(n_chars)]]
i += 1
inp[h0] = h_prev
if p // seq_length % n_show == 0:
print "Iteration %d:" % (p // seq_length)
print time.clock() - prev_time
prev_time = time.clock()
result = sess.run(prob, inp)
s = ""
for r in result:
c = np.random.choice(range(n_chars), p=r.ravel())
s += ix_to_char[c]
print s
_, h_prev = sess.run([train_step, h], inp)
p += seq_length
#!/usr/bin/env python2
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np
import time
# data I/O
data = open('corpus.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
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
prev_time = time.clock()
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:
print time.clock() - prev_time
prev_time = time.clock()
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
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