Learning_Deep_Learning/rnntest.py at master · gary-butler/Learning_Deep_Learning · GitHub

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data = open('rumi.txt', 'r').read()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print('data has %d chars, %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)}
print(char_to_ix)
print(ix_to_char)

import numpy as np

vector_for_char_a = np.zeros((vocab_size, 1))
vector_for_char_a[char_to_ix['a']] = 1
print(vector_for_char_a.ravel())
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 range(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(range(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 range(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)
    print('----\n %s \n----' % (txt, ))

hprev = np.zeros((hidden_size,1))
sample(hprev,char_to_ix['a'],200)
p=0
inputs = [char_to_ix[ch] for ch in data[p:p+seq_length]]
print("inputs", inputs)
targets = [char_to_ix[ch] for ch in data[p+1:p+seq_length+1]]
print("targets", targets)
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<=1000*10:
    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 % 1000 == 0:
        print('iter %d, loss: %f' % (n, smooth_loss))
        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)
    p += seq_length
    n += 1