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randomfun/min-char-rnn-nb2.ipynb
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Andrej Karpathy bd2b874ced steps
2016-02-10 15:52:32 -08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RNN Language Model\n",
"\n",
"Below is a diagram of the RNN computation that we will implement below. We're plugging characters into the RNN with a 1-hot encoding and expecting it to predict the next character. In this example the training data is the string \"hello\", so there are 4 letters in the vocabulary: [h,e,l,o]."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"rnnlm.jpeg\">"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"np.random.seed(1337)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data has 4573338 characters, 67 unique.\n"
]
}
],
"source": [
"# data I/O\n",
"# get shakespeare from http://cs.stanford.edu/people/karpathy/shakespeare.txt\n",
"data = open('shakespeare.txt', 'r').read() # should be simple plain text file\n",
"chars = list(set(data))\n",
"data_size, vocab_size = len(data), len(chars)\n",
"print 'data has %d characters, %d unique.' % (data_size, vocab_size)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"char_to_ix = { ch:i for i,ch in enumerate(chars) }\n",
"ix_to_char = { i:ch for i,ch in enumerate(chars) }"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"41"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"char_to_ix['a']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" thing when he was young,\n"
]
}
],
"source": [
"# lets sample a batch of data\n",
"seq_length = 25 # number of characters in the batch\n",
"p = 220000 # point in the book to sample from\n",
"print data[p:p+seq_length] # print a chunk of data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2, 61, 49, 48, 55, 46, 2, 62, 49, 44, 55, 2, 49, 44, 2, 62, 41, 58, 2, 64, 54, 60, 55, 46, 7]\n",
"[61, 49, 48, 55, 46, 2, 62, 49, 44, 55, 2, 49, 44, 2, 62, 41, 58, 2, 64, 54, 60, 55, 46, 7, 0]\n"
]
}
],
"source": [
"inputs = [char_to_ix[ch] for ch in data[p:p+seq_length]]\n",
"targets = [char_to_ix[ch] for ch in data[p+1:p+seq_length+1]]\n",
"print inputs\n",
"print targets"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
]
}
],
"source": [
"# lets plug the first character into the RNN\n",
"ix_input = inputs[0]\n",
"ix_target = targets[0]\n",
"# encode the input character with a 1-hot representation\n",
"x = np.zeros((vocab_size,1))\n",
"x[ix_input] = 1\n",
"print x.ravel()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create random starting parameters\n",
"hidden_size = 10\n",
"Wxh = np.random.randn(hidden_size, vocab_size)*0.01 # input to hidden\n",
"Whh = np.random.randn(hidden_size, hidden_size)*0.01 # hidden to hidden\n",
"Why = np.random.randn(vocab_size, hidden_size)*0.01 # hidden to output\n",
"bh = np.zeros((hidden_size, 1)) # hidden bias\n",
"by = np.zeros((vocab_size, 1)) # output bias"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.00321813 0.00640499 0.00424507 0.00783602 -0.00428041 0.00900686\n",
" -0.01618631 0.00071848 -0.01342806 -0.0048051 ]\n"
]
}
],
"source": [
"# compute the hidden state\n",
"h_prev = np.zeros((hidden_size, 1))\n",
"h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h_prev + bh))\n",
"print h.ravel()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 3.83078834e-05 3.73893809e-05 -1.07305639e-04 2.67774461e-04\n",
" 1.19325035e-04 -3.37354220e-04 -3.34852820e-04 3.32538588e-04\n",
" 3.18794726e-04 -1.03554566e-05 1.36845629e-04 -5.38710213e-04\n",
" -4.57711660e-04 2.99868693e-04 1.68690516e-04 1.78520042e-04\n",
" 2.36693532e-04 -7.03401501e-05 -7.16902773e-05 1.27203335e-04\n",
" 8.04252112e-05 1.59279072e-04 2.17314448e-04 -2.72573982e-05\n",
" -7.02211895e-04 -2.42104646e-05 5.30702842e-04 1.94948893e-04\n",
" -9.90711082e-05 4.03627462e-04 -2.93323333e-04 3.93383233e-04\n",
" -4.05743771e-04 -3.38345251e-04 -5.29097616e-05 -4.80911954e-05\n",
" 1.33355225e-04 4.75679274e-04 6.58103198e-05 1.94801460e-04\n",
" -2.20233399e-04 3.08235108e-04 -2.53968781e-05 1.44684905e-04\n",
" -8.29930806e-05 5.83661343e-04 7.77516275e-05 2.55059906e-04\n",
" 1.81287457e-04 2.64252428e-04 1.78958318e-04 -2.92440820e-04\n",
" -3.40954240e-04 8.32849829e-05 -6.05381172e-05 3.07491179e-04\n",
" 3.90229048e-04 -1.39828111e-04 2.11848605e-04 -5.79932785e-04\n",
" 6.02578409e-05 -2.12493371e-04 2.07956569e-04 -2.84122452e-04\n",
" -2.30967665e-04 3.80355416e-04 9.87418965e-05]\n"
]
}
],
"source": [
"# compute the scores for next character\n",
"y = np.dot(Why, h) + by\n",
"print y.ravel()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.0149254 0.01492538 0.01492322 0.01492882 0.01492661 0.01491979\n",
" 0.01491983 0.01492979 0.01492958 0.01492467 0.01492687 0.01491679\n",
" 0.014918 0.0149293 0.01492734 0.01492749 0.01492836 0.01492378\n",
" 0.01492376 0.01492672 0.01492603 0.0149272 0.01492807 0.01492442\n",
" 0.01491435 0.01492446 0.01493275 0.01492774 0.01492335 0.01493085\n",
" 0.01492045 0.0149307 0.01491877 0.01491978 0.01492404 0.01492411\n",
" 0.01492682 0.01493193 0.01492581 0.01492773 0.01492154 0.01492943\n",
" 0.01492445 0.01492699 0.01492359 0.01493354 0.01492599 0.01492863\n",
" 0.01492753 0.01492877 0.0149275 0.01492046 0.01491974 0.01492607\n",
" 0.01492392 0.01492942 0.01493065 0.01492274 0.01492799 0.01491617\n",
" 0.01492572 0.01492165 0.01492793 0.01492059 0.01492138 0.0149305\n",
" 0.0149263 ]\n",
"probabilities sum to 1.0\n"
]
}
],
"source": [
"# the scores are unnormalized log probabilities. compute the probabilities\n",
"p = np.exp(y) / np.sum(np.exp(y))\n",
"print p.ravel()\n",
"print 'probabilities sum to ', p.sum()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"probability assigned to the correct next character is right now: 0.0149216543899\n"
]
}
],
"source": [
"print 'probability assigned to the correct next character is right now: ', p[ix_target,0]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the cross-entropy (softmax) loss is 4.20494180632\n"
]
}
],
"source": [
"loss = -np.log(p[ix_target,0])\n",
"print 'the cross-entropy (softmax) loss is ', loss"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.0149254 0.01492538 0.01492322 0.01492882 0.01492661 0.01491979\n",
" 0.01491983 0.01492979 0.01492958 0.01492467 0.01492687 0.01491679\n",
" 0.014918 0.0149293 0.01492734 0.01492749 0.01492836 0.01492378\n",
" 0.01492376 0.01492672 0.01492603 0.0149272 0.01492807 0.01492442\n",
" 0.01491435 0.01492446 0.01493275 0.01492774 0.01492335 0.01493085\n",
" 0.01492045 0.0149307 0.01491877 0.01491978 0.01492404 0.01492411\n",
" 0.01492682 0.01493193 0.01492581 0.01492773 0.01492154 0.01492943\n",
" 0.01492445 0.01492699 0.01492359 0.01493354 0.01492599 0.01492863\n",
" 0.01492753 0.01492877 0.0149275 0.01492046 0.01491974 0.01492607\n",
" 0.01492392 0.01492942 0.01493065 0.01492274 0.01492799 0.01491617\n",
" 0.01492572 -0.98507835 0.01492793 0.01492059 0.01492138 0.0149305\n",
" 0.0149263 ]\n",
"sum of dy is 1.45716771982e-16\n",
"the gradient for the correct character (t) is: -0.98507834561\n",
"the gradient for the character (a) is: 0.0149294265437\n"
]
}
],
"source": [
"# compute the gradient on y\n",
"dy = np.copy(p)\n",
"dy[ix_target] -= 1\n",
"print dy.ravel()\n",
"print 'sum of dy is ', dy.sum()\n",
"print 'the gradient for the correct character (%s) is: %s' % (ix_to_char[ix_target], dy[ix_target,0])\n",
"print 'the gradient for the character (a) is: ', dy[char_to_ix['a'],0]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the hidden vector activations were:\n",
"[-0.00321813 0.00640499 0.00424507 0.00783602 -0.00428041 0.00900686\n",
" -0.01618631 0.00071848 -0.01342806 -0.0048051 ]\n",
"the gradients are:\n",
"[-0.00983172 -0.01049054 0.00911112 -0.01110877 -0.00134586 0.00686193\n",
" -0.01532285 0.0099507 -0.00530998 0.01267197]\n",
"the gradients dWhy have size: (67, 10)\n",
"a small sample is:\n",
"[[ -4.80319012e-05 9.55970187e-05 6.33594305e-05 1.16955768e-04]\n",
" [ -4.80318571e-05 9.55969309e-05 6.33593723e-05 1.16955661e-04]\n",
" [ -4.80249076e-05 9.55830995e-05 6.33502052e-05 1.16938739e-04]\n",
" [ -4.80429242e-05 9.56189576e-05 6.33739710e-05 1.16982609e-04]]\n"
]
}
],
"source": [
"# we computed [y = np.dot(Why, h) + by]; Backpropagate to Why, h, and by\n",
"dWhy = np.dot(dy, h.T)\n",
"dh = np.dot(Why.T, dy)\n",
"dby = np.copy(dy)\n",
"print 'the hidden vector activations were:'\n",
"print h.ravel()\n",
"print 'the gradients are:'\n",
"print dh.ravel()\n",
"print 'the gradients dWhy have size: ', dWhy.shape\n",
"print 'a small sample is:'\n",
"print dWhy[:4,:4]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"small sample of Whh:\n",
"[[-0.0028736 -0.00195895 0.00885911 -0.00349354]\n",
" [-0.00773272 -0.00051873 0.00219991 -0.00234756]\n",
" [ 0.01687054 -0.01221995 0.00125455 -0.00568523]\n",
" [-0.00031645 0.00514377 0.01194564 0.0070584 ]]\n"
]
}
],
"source": [
"# we computed [h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h_prev + bh))]; \n",
"# Backprop into Wxh, x, Whh, h_prev, bh:\n",
"dh_before_tanh = (1-h**2)*dh\n",
"dbh = np.copy(dh_before_tanh)\n",
"dWxh = np.dot(dh_before_tanh, x.T)\n",
"dWhh = np.dot(dh_before_tanh, h.T)\n",
"dh_prev = np.dot(Whh.T, dh_before_tanh)\n",
"print 'small sample of Whh:'\n",
"print Whh[:4,:4]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we now have the gradients for all parameters! (Wxh, Whh, Why, bh, by)\n",
"# lets do a parameter update\n",
"learning_rate = 0.1\n",
"Wxh2 = Wxh - learning_rate * dWxh\n",
"Whh2 = Whh - learning_rate * dWhh\n",
"Why2 = Why - learning_rate * dWhy\n",
"bh2 = bh - learning_rate * dbh\n",
"by2 = by - learning_rate * dby"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"probability assigned to the correct next character was: 0.0149216543899\n",
"probability assigned to the correct next character is now: 0.0164678301753\n",
"the cross-entropy (softmax) loss was 4.20494180632\n",
"the loss is now 4.10634648754\n"
]
}
],
"source": [
"# these parameters should be much better! lets try it out:\n",
"h2 = np.tanh(np.dot(Wxh2, x) + np.dot(Whh2, h_prev + bh2))\n",
"y2 = np.dot(Why2, h2) + by2\n",
"p2 = np.exp(y2) / np.sum(np.exp(y2))\n",
"print 'probability assigned to the correct next character was: ', p[ix_target,0]\n",
"print 'probability assigned to the correct next character is now: ', p2[ix_target,0]\n",
"loss2 = -np.log(p2[ix_target,0])\n",
"print 'the cross-entropy (softmax) loss was ', loss\n",
"print 'the loss is now ', loss2"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# note: the probability for the correct character went up! (and the loss went down)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# putting it together with loops\n",
"def lossFun(inputs, targets, hprev):\n",
" \"\"\"\n",
" inputs,targets are both list of integers.\n",
" hprev is Hx1 array of initial hidden state\n",
" returns the loss, gradients on model parameters, and last hidden state\n",
" \"\"\"\n",
" xs, hs, ys, ps = {}, {}, {}, {}\n",
" hs[-1] = np.copy(hprev)\n",
" loss = 0\n",
" \n",
" # forward pass\n",
" for t in xrange(len(inputs)):\n",
" xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation\n",
" xs[t][inputs[t]] = 1\n",
" hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state\n",
" ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars\n",
" ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars\n",
" loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)\n",
" \n",
" # backward pass: compute gradients going backwards\n",
" dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)\n",
" dbh, dby = np.zeros_like(bh), np.zeros_like(by)\n",
" dhnext = np.zeros_like(hs[0])\n",
" for t in reversed(xrange(len(inputs))):\n",
" dy = np.copy(ps[t])\n",
" dy[targets[t]] -= 1 # backprop into y\n",
" dWhy += np.dot(dy, hs[t].T)\n",
" dby += dy\n",
" dh = np.dot(Why.T, dy) + dhnext # backprop into h\n",
" dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity\n",
" dbh += dhraw\n",
" dWxh += np.dot(dhraw, xs[t].T)\n",
" dWhh += np.dot(dhraw, hs[t-1].T)\n",
" dhnext = np.dot(Whh.T, dhraw)\n",
" \n",
" # clip to mitigate exploding gradients\n",
" for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\n",
" np.clip(dparam, -5, 5, out=dparam)\n",
" \n",
" return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"105.118896505\n"
]
}
],
"source": [
"loss, dWxh, dWhh, dWhy, dbh, dby, hnew = lossFun(inputs, targets, h_prev)\n",
"print loss"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# TODO: write the sampling code\n",
"def sample(h, seed_ix, n):\n",
" \"\"\" \n",
" sample a sequence of integers from the model \n",
" h is initial memory state, seed_ix is seed letter for first time step\n",
" n is the number of time steps to sample for\n",
" \"\"\"\n",
" x = np.zeros((vocab_size, 1))\n",
" x[seed_ix] = 1\n",
" ixes = [] # sampled indices\n",
" for t in xrange(n):\n",
" pass # TODO: run the RNN for one time step, sample from distribution\n",
" return ixes\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iter 0, loss: 105.117314\n",
"iter 100, loss: 104.983384\n",
"iter 200, loss: 104.623511\n",
"iter 300, loss: 104.093896\n",
"iter 400, loss: 103.407954\n",
"iter 500, loss: 102.742497\n",
"iter 600, loss: 101.886946\n",
"iter 700, loss: 101.017334\n",
"iter 800, loss: 100.224049\n",
"iter 900, loss: 99.275074\n",
"iter 1000, loss: 98.310447\n",
"iter 1100, loss: 97.280136\n",
"iter 1200, loss: 96.170347\n",
"iter 1300, loss: 95.026980\n",
"iter 1400, loss: 94.074431\n",
"iter 1500, loss: 93.214796\n",
"iter 1600, loss: 92.194884\n",
"iter 1700, loss: 91.461468\n",
"iter 1800, loss: 90.884004\n",
"iter 1900, loss: 90.051538\n",
"iter 2000, loss: 89.025933\n",
"iter 2100, loss: 88.348015\n",
"iter 2200, loss: 87.853860\n",
"iter 2300, loss: 87.232155\n",
"iter 2400, loss: 86.868957\n",
"iter 2500, loss: 86.633172\n",
"iter 2600, loss: 86.099010\n",
"iter 2700, loss: 85.847026\n",
"iter 2800, loss: 85.920993\n",
"iter 2900, loss: 85.654873\n",
"iter 3000, loss: 85.194774\n",
"iter 3100, loss: 85.314185\n",
"iter 3200, loss: 85.286007\n",
"iter 3300, loss: 85.060589\n",
"iter 3400, loss: 84.918822\n",
"iter 3500, loss: 84.606756\n",
"iter 3600, loss: 84.445501\n",
"iter 3700, loss: 84.407657\n",
"iter 3800, loss: 84.323040\n",
"iter 3900, loss: 84.133951\n",
"iter 4000, loss: 84.064946\n",
"iter 4100, loss: 84.004744\n",
"iter 4200, loss: 83.680242\n",
"iter 4300, loss: 83.838111\n",
"iter 4400, loss: 83.538384\n",
"iter 4500, loss: 83.244682\n",
"iter 4600, loss: 82.926760\n",
"iter 4700, loss: 82.978359\n",
"iter 4800, loss: 83.063037\n",
"iter 4900, loss: 83.094751\n",
"iter 5000, loss: 82.961633\n",
"iter 5100, loss: 82.787609\n",
"iter 5200, loss: 82.812643\n",
"iter 5300, loss: 82.653842\n",
"iter 5400, loss: 82.512128\n",
"iter 5500, loss: 82.471460\n",
"iter 5600, loss: 82.328936\n",
"iter 5700, loss: 82.100785\n",
"iter 5800, loss: 82.109023\n",
"iter 5900, loss: 82.088454\n",
"iter 6000, loss: 81.998958\n",
"iter 6100, loss: 82.107872\n",
"iter 6200, loss: 82.203081\n",
"iter 6300, loss: 82.139286\n",
"iter 6400, loss: 82.375904\n",
"iter 6500, loss: 82.276112\n",
"iter 6600, loss: 82.344397\n",
"iter 6700, loss: 82.709979\n",
"iter 6800, loss: 82.775993\n",
"iter 6900, loss: 82.987775\n",
"iter 7000, loss: 82.979527\n",
"iter 7100, loss: 83.100577\n",
"iter 7200, loss: 83.124201\n",
"iter 7300, loss: 83.255949\n",
"iter 7400, loss: 83.229047\n",
"iter 7500, loss: 83.334554\n",
"iter 7600, loss: 83.281780\n",
"iter 7700, loss: 83.155654\n",
"iter 7800, loss: 83.017638\n",
"iter 7900, loss: 82.702089\n",
"iter 8000, loss: 82.610772\n",
"iter 8100, loss: 82.449181\n",
"iter 8200, loss: 82.512648\n",
"iter 8300, loss: 82.518510\n",
"iter 8400, loss: 82.448900\n",
"iter 8500, loss: 82.438597\n",
"iter 8600, loss: 82.449625\n",
"iter 8700, loss: 82.450940\n",
"iter 8800, loss: 82.330407\n",
"iter 8900, loss: 82.704262\n",
"iter 9000, loss: 82.595555\n",
"iter 9100, loss: 82.738328\n",
"iter 9200, loss: 82.954857\n",
"iter 9300, loss: 82.893699\n",
"iter 9400, loss: 82.574069\n",
"iter 9500, loss: 82.386784\n",
"iter 9600, loss: 82.385382\n",
"iter 9700, loss: 82.287088\n",
"iter 9800, loss: 82.185769\n",
"iter 9900, loss: 81.809823\n"
]
}
],
"source": [
"# Stochastic Gradient Descent\n",
"n, p = 0, 0\n",
"smooth_loss = -np.log(1.0/vocab_size)*seq_length # loss at iteration 0\n",
"learning_rate = 1e-3\n",
"while n < 10000:\n",
" # prepare inputs (we're sweeping from left to right in steps seq_length long)\n",
" if p+seq_length+1 >= len(data) or n == 0: \n",
" hprev = np.zeros((hidden_size,1)) # reset RNN memory\n",
" p = 0 # go from start of data\n",
" inputs = [char_to_ix[ch] for ch in data[p:p+seq_length]]\n",
" targets = [char_to_ix[ch] for ch in data[p+1:p+seq_length+1]]\n",
"\n",
" # forward seq_length characters through the net and fetch gradient\n",
" loss, dWxh, dWhh, dWhy, dbh, dby, hprev = lossFun(inputs, targets, hprev)\n",
" smooth_loss = smooth_loss * 0.999 + loss * 0.001\n",
" if n % 100 == 0: print 'iter %d, loss: %f' % (n, smooth_loss) # print progress\n",
"\n",
" # perform parameter update with Adagrad\n",
" for param, dparam in zip([Wxh, Whh, Why, bh, by], \n",
" [dWxh, dWhh, dWhy, dbh, dby]):\n",
" param += -learning_rate * dparam\n",
"\n",
" p += seq_length # move data pointer\n",
" n += 1 # iteration counter "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# TODO: Implement a sampling function that lets us generate from the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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