import numpy as npdata = open('input.txt', 'r').read()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)}# hyperparametershidden_size = 100 # size of hidden layer of neuronsseq_length = 25 # number of steps to unroll the RNN forlearning_rate = 1e-1# model parametersWxh = np.random.randn(hidden_size, vocab_size) * 0.01 # input to hiddenWhh = np.random.randn(hidden_size, hidden_size) * 0.01 # hidden to hiddenWhy = np.random.randn(vocab_size, hidden_size) * 0.01 # hidden to outputbh = np.zeros((hidden_size, 1)) # hidden biasby = np.zeros((vocab_size, 1)) # output biasdef 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 range(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) dh_next = np.zeros_like(hs[0]) for t in reversed(range(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) + dh_next # backprop into h dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh non-linearity dWxh += np.dot(dhraw, xs[t].T) dWhh += np.dot(dhraw, hs[t - 1].T) dbh += dhraw dh_next = 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 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) return ixesn, p = 0, 0hprev = np.zeros((hidden_size, 1))# memory variables for AdagradmWxh, 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 # loss at iteration 0while 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]] # 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 # 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 if n % 100 == 0: # print current loss function print('iter %d, loss: %f' % (n, smooth_loss)) # sample from the model now and then sample_ix = sample(hprev, inputs[0], 200) txt = ''.join(ix_to_char[ix] for ix in sample_ix) print('----\n %s \n----' % txt) p += seq_length # move data pointer n += 1 # iteration counter