From 73baf7a4698a5d6aad18eda4ea7e5beb8a2b9d86 Mon Sep 17 00:00:00 2001 From: Donne Martin Date: Sun, 27 Dec 2015 09:31:45 -0500 Subject: [PATCH] Add Theano recurrent neural networks notebook. --- README.md | 1 + .../theano-tutorial/rnn_tutorial/Makefile | 13 + .../rnn_tutorial/instruction.pdf | Bin 0 -> 2822484 bytes .../rnn_tutorial/lstm_text.ipynb | 508 ++++++++++++++++++ .../theano-tutorial/rnn_tutorial/lstm_text.py | 299 +++++++++++ .../theano-tutorial/rnn_tutorial/rnn_lstm.pdf | Bin 0 -> 2950544 bytes .../rnn_tutorial/rnn_precompile.py | 234 ++++++++ .../rnn_tutorial/simple_rnn.ipynb | 393 ++++++++++++++ .../theano-tutorial/rnn_tutorial/synthetic.py | 85 +++ 9 files changed, 1533 insertions(+) create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/Makefile create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/instruction.pdf create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/lstm_text.ipynb create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/rnn_lstm.pdf create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/rnn_precompile.py create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb create mode 100644 deep-learning/theano-tutorial/rnn_tutorial/synthetic.py diff --git a/README.md b/README.md index 5492897..e9415bb 100644 --- a/README.md +++ b/README.md @@ -96,6 +96,7 @@ IPython Notebook(s) demonstrating deep learning functionality. | [theano-intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/intro_theano/intro_theano.ipynb) | Intro to Theano, which allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. | | [theano-scan](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb) | Learn scans, a mechanism to perform loops in a Theano graph. | | [theano-logistic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/intro_theano/logistic_regression.ipynb) | Implement logistic regression in Theano. | +| [theano-rnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb) | Implement recurrent neural networks in Theano. | | [deep-dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/Makefile b/deep-learning/theano-tutorial/rnn_tutorial/Makefile new file mode 100644 index 0000000..70828a1 --- /dev/null +++ b/deep-learning/theano-tutorial/rnn_tutorial/Makefile @@ -0,0 +1,13 @@ +all: instruction.pdf rnn_lstm.pdf + +instruction.pdf: slides_source/instruction.tex + cd slides_source; pdflatex --shell-escape instruction.tex + cd slides_source; pdflatex --shell-escape instruction.tex + cd slides_source; pdflatex --shell-escape instruction.tex + mv slides_source/instruction.pdf . + +rnn_lstm.pdf: slides_source/rnn_lstm.tex + cd slides_source; pdflatex --shell-escape rnn_lstm.tex + cd slides_source; pdflatex --shell-escape rnn_lstm.tex + cd slides_source; pdflatex --shell-escape rnn_lstm.tex + mv slides_source/rnn_lstm.pdf . diff --git a/deep-learning/theano-tutorial/rnn_tutorial/instruction.pdf b/deep-learning/theano-tutorial/rnn_tutorial/instruction.pdf new file mode 100644 index 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znfEjbOd|C6U?E}JLZxBO3K|WGp+>b2n1y+l1`8Nd4n%i$8k{oddo2MudC>AW(6rHf z0U8`g=k18CX^jPk^>1RCsO zXd1+dVfsPD$n9{vX`^Kopi$6knu;gk(PIQO61E>C8hZZ0aS4kH-9EUkqUIWugNq)z z9GPkPXYViU#i(^og;N2s*t3^|Ee!KLxFurC(a`rEIL^KR>XOFctKmemC`X_k$ zHNvgaHCO^tH)Scs2qwYOWd 1:\n", + " flat_prediction = flat_prediction.astype('float64') / flat_pred_sum\n", + " sample = numpy.random.multinomial(1, flat_prediction)\n", + "\n", + " for i in range(n_steps):\n", + " x_t = numpy.argmax(sample)\n", + " prediction, h_p, c_p = single_step(x_t, h_p.flatten(),\n", + " c_p.flatten())\n", + " # Renormalize probability in float64\n", + " flat_prediction = prediction.flatten()\n", + " flat_pred_sum = flat_prediction.sum(dtype='float64')\n", + " if flat_pred_sum > 1:\n", + " flat_prediction = flat_prediction.astype('float64') / flat_pred_sum\n", + " sample = numpy.random.multinomial(1, flat_prediction)\n", + "\n", + " sentence += reverse_mapping[x_t]\n", + "\n", + " return sentence\n", + " except ValueError as e:\n", + " print 'Something went wrong during sentence generation: {}'.format(e)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch: 0\n", + "\n", + "LSTM: \"the meaning of life is i�ateisn ^ltbagss7tuodkca r9 msd,forreypoctlluoiasrn?at�netteofkotenni�cf/vattosnlrxisiovu�al.hahau�ootwo tuost! ]cw� eweunhufaaecihtdtk tticiss cvt2f etoct bllstsluohh-,retti?eusrv eikly an�ade'i stiel�doelnamtuartoci�ht.�woi 2kfs$an tpeo�miiadain9.e eegtamiaesboeinne�unlocityqe dansapeaeiyo�ihaewmtrt�'aa svteatae ,otrr.gsac.-perioswetgoc�io froaoeismhsgtulherbttrh fl�i el nnltnta�sat yhomsnttwlnwnenaee.mhits r�us-thist sn man4lamhpac.osdopl g�\"\n", + "\n", + "epoch: 0 minibatch: 40\n", + "Average validation CE per sentence: 251.167072292\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0miteration\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mcross_entropy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mupdate_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/pbrakel/Repositories/Theano/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 577\u001b[0m \u001b[0mt0_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 578\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 580\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'position_of_error'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/pbrakel/Repositories/Theano/theano/scan_module/scan_op.pyc\u001b[0m in \u001b[0;36mrval\u001b[1;34m(p, i, o, n)\u001b[0m\n\u001b[0;32m 649\u001b[0m \u001b[1;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 651\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 652\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 653\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "start_time = time.clock()\n", + "\n", + "iteration = 0\n", + "\n", + "for epoch in range(n_epochs):\n", + " print 'epoch:', epoch\n", + "\n", + " for x_, mask_ in train_stream.get_epoch_iterator():\n", + " iteration += 1\n", + "\n", + " cross_entropy = update_model(x_.T, mask_.T)\n", + "\n", + "\n", + " # Generate some text after each 20 minibatches\n", + " if iteration % 40 == 0:\n", + " sentence = speak(single_step, prefix='the meaning of life is ', n_steps=450)\n", + " print\n", + " print 'LSTM: \"' + sentence + '\"'\n", + " print\n", + " print 'epoch:', epoch, ' minibatch:', iteration\n", + " val_scores = []\n", + " for x_val, mask_val in val_stream.get_epoch_iterator():\n", + " val_scores.append(evaluate_model(x_val.T, mask_val.T))\n", + " print 'Average validation CE per sentence:', numpy.mean(val_scores)\n", + "\n", + "end_time = time.clock()\n", + "print('Optimization complete.')\n", + "print('The code ran for %.2fm' % ((end_time - start_time) / 60.))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "It can take a while before the text starts to look more reasonable but here are some things to experiment with:\n", + "* Smarter optimization algorithms (or at least momentum)\n", + "* Initializing the recurrent weights orthogonally\n", + "* The sizes of the initial weights and biases (think about what the gates do)\n", + "* Different sentence prefixes\n", + "* Changing the temperature of the character distribution during generation. What happens when you generate deterministically?" + ] + }, + { + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py b/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py new file mode 100644 index 0000000..d2b734b --- /dev/null +++ b/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py @@ -0,0 +1,299 @@ +import cPickle as pkl +import time + +import numpy +import theano +from theano import config +import theano.tensor as T +from theano.tensor.nnet import categorical_crossentropy + +from fuel.datasets import TextFile +from fuel.streams import DataStream +from fuel.schemes import ConstantScheme +from fuel.transformers import Batch, Padding + + +# These files can be downloaded from +# http://www-etud.iro.umontreal.ca/~brakelp/train.txt.gz +# http://www-etud.iro.umontreal.ca/~brakelp/dictionary.pkl +# don't forget to change the paths and gunzip train.txt.gz +TRAIN_FILE = '/u/brakelp/temp/traindata.txt' +VAL_FILE = '/u/brakelp/temp/valdata.txt' +DICT_FILE = '/u/brakelp/temp/dictionary.pkl' + + +def sequence_categorical_crossentropy(prediction, targets, mask): + prediction_flat = prediction.reshape(((prediction.shape[0] * + prediction.shape[1]), + prediction.shape[2]), ndim=2) + targets_flat = targets.flatten() + mask_flat = mask.flatten() + ce = categorical_crossentropy(prediction_flat, targets_flat) + return T.sum(ce * mask_flat) + + +def gauss_weight(ndim_in, ndim_out=None, sd=.005): + if ndim_out is None: + ndim_out = ndim_in + W = numpy.random.randn(ndim_in, ndim_out) * sd + return numpy.asarray(W, dtype=config.floatX) + + +class LogisticRegression(object): + """Multi-class Logistic Regression Class + + The logistic regression is fully described by a weight matrix :math:`W` + and bias vector :math:`b`. Classification is done by projecting data + points onto a set of hyperplanes, the distance to which is used to + determine a class membership probability. + """ + + def __init__(self, input, n_in, n_out): + """ Initialize the parameters of the logistic regression + + :type input: theano.tensor.TensorType + :param input: symbolic variable that describes the input of the + architecture (one minibatch) + + :type n_in: int + :param n_in: number of input units, the dimension of the space in + which the datapoints lie + + :type n_out: int + :param n_out: number of output units, the dimension of the space in + which the labels lie + + """ + + # initialize with 0 the weights W as a matrix of shape (n_in, n_out) + self.W = theano.shared(value=numpy.zeros((n_in, n_out), + dtype=theano.config.floatX), + name='W', borrow=True) + # initialize the baises b as a vector of n_out 0s + self.b = theano.shared(value=numpy.zeros((n_out,), + dtype=theano.config.floatX), + name='b', borrow=True) + + # compute vector of class-membership probabilities in symbolic form + energy = T.dot(input, self.W) + self.b + energy_exp = T.exp(energy - T.max(energy, 2)[:, :, None]) + pmf = energy_exp / energy_exp.sum(2)[:, :, None] + self.p_y_given_x = pmf + + # compute prediction as class whose probability is maximal in + # symbolic form + self.y_pred = T.argmax(self.p_y_given_x, axis=1) + + # parameters of the model + self.params = [self.W, self.b] + + +def index_dot(indices, w): + return w[indices.flatten()] + + +class LstmLayer: + + def __init__(self, rng, input, mask, n_in, n_h): + + # Init params + self.W_i = theano.shared(gauss_weight(n_in, n_h), 'W_i', borrow=True) + self.W_f = theano.shared(gauss_weight(n_in, n_h), 'W_f', borrow=True) + self.W_c = theano.shared(gauss_weight(n_in, n_h), 'W_c', borrow=True) + self.W_o = theano.shared(gauss_weight(n_in, n_h), 'W_o', borrow=True) + + self.U_i = theano.shared(gauss_weight(n_h), 'U_i', borrow=True) + self.U_f = theano.shared(gauss_weight(n_h), 'U_f', borrow=True) + self.U_c = theano.shared(gauss_weight(n_h), 'U_c', borrow=True) + self.U_o = theano.shared(gauss_weight(n_h), 'U_o', borrow=True) + + self.b_i = theano.shared(numpy.zeros((n_h,), dtype=config.floatX), + 'b_i', borrow=True) + self.b_f = theano.shared(numpy.zeros((n_h,), dtype=config.floatX), + 'b_f', borrow=True) + self.b_c = theano.shared(numpy.zeros((n_h,), dtype=config.floatX), + 'b_c', borrow=True) + self.b_o = theano.shared(numpy.zeros((n_h,), dtype=config.floatX), + 'b_o', borrow=True) + + self.params = [self.W_i, self.W_f, self.W_c, self.W_o, + self.U_i, self.U_f, self.U_c, self.U_o, + self.b_i, self.b_f, self.b_c, self.b_o] + + outputs_info = [T.zeros((input.shape[1], n_h)), + T.zeros((input.shape[1], n_h))] + + rval, updates = theano.scan(self._step, + sequences=[mask, input], + outputs_info=outputs_info) + + # self.output is in the format (batchsize, n_h) + self.output = rval[0] + + def _step(self, m_, x_, h_, c_): + + i_preact = (index_dot(x_, self.W_i) + + T.dot(h_, self.U_i) + self.b_i) + i = T.nnet.sigmoid(i_preact) + + f_preact = (index_dot(x_, self.W_f) + + T.dot(h_, self.U_f) + self.b_f) + f = T.nnet.sigmoid(f_preact) + + o_preact = (index_dot(x_, self.W_o) + + T.dot(h_, self.U_o) + self.b_o) + o = T.nnet.sigmoid(o_preact) + + c_preact = (index_dot(x_, self.W_c) + + T.dot(h_, self.U_c) + self.b_c) + c = T.tanh(c_preact) + + c = f * c_ + i * c + c = m_[:, None] * c + (1. - m_)[:, None] * c_ + + h = o * T.tanh(c) + h = m_[:, None] * h + (1. - m_)[:, None] * h_ + + return h, c + + +def train_model(batch_size=100, n_h=50, n_epochs=40): + + # Load the datasets with Fuel + dictionary = pkl.load(open(DICT_FILE, 'r')) + dictionary['~'] = len(dictionary) + reverse_mapping = dict((j, i) for i, j in dictionary.items()) + + print("Loading the data") + train = TextFile(files=[TRAIN_FILE], + dictionary=dictionary, + unk_token='~', + level='character', + preprocess=str.lower, + bos_token=None, + eos_token=None) + + train_stream = DataStream.default_stream(train) + + # organize data in batches and pad shorter sequences with zeros + train_stream = Batch(train_stream, + iteration_scheme=ConstantScheme(batch_size)) + train_stream = Padding(train_stream) + + # idem dito for the validation text + val = TextFile(files=[VAL_FILE], + dictionary=dictionary, + unk_token='~', + level='character', + preprocess=str.lower, + bos_token=None, + eos_token=None) + + val_stream = DataStream.default_stream(val) + + # organize data in batches and pad shorter sequences with zeros + val_stream = Batch(val_stream, + iteration_scheme=ConstantScheme(batch_size)) + val_stream = Padding(val_stream) + + print('Building model') + + # Set the random number generator' seeds for consistency + rng = numpy.random.RandomState(12345) + + x = T.lmatrix('x') + mask = T.matrix('mask') + + # Construct the LSTM layer + recurrent_layer = LstmLayer(rng=rng, input=x, mask=mask, n_in=111, n_h=n_h) + + logreg_layer = LogisticRegression(input=recurrent_layer.output[:-1], + n_in=n_h, n_out=111) + + cost = sequence_categorical_crossentropy(logreg_layer.p_y_given_x, + x[1:], + mask[1:]) / batch_size + + # create a list of all model parameters to be fit by gradient descent + params = logreg_layer.params + recurrent_layer.params + + # create a list of gradients for all model parameters + grads = T.grad(cost, params) + + # update_model is a function that updates the model parameters by + # SGD Since this model has many parameters, it would be tedious to + # manually create an update rule for each model parameter. We thus + # create the updates list by automatically looping over all + # (params[i], grads[i]) pairs. + learning_rate = 0.1 + updates = [ + (param_i, param_i - learning_rate * grad_i) + for param_i, grad_i in zip(params, grads) + ] + + update_model = theano.function([x, mask], cost, updates=updates) + + evaluate_model = theano.function([x, mask], cost) + + # Define and compile a function for generating a sequence step by step. + x_t = T.iscalar() + h_p = T.vector() + c_p = T.vector() + h_t, c_t = recurrent_layer._step(T.ones(1), x_t, h_p, c_p) + energy = T.dot(h_t, logreg_layer.W) + logreg_layer.b + + energy_exp = T.exp(energy - T.max(energy, 1)[:, None]) + + output = energy_exp / energy_exp.sum(1)[:, None] + single_step = theano.function([x_t, h_p, c_p], [output, h_t, c_t]) + + start_time = time.clock() + + iteration = 0 + + for epoch in range(n_epochs): + print 'epoch:', epoch + + for x_, mask_ in train_stream.get_epoch_iterator(): + iteration += 1 + + cross_entropy = update_model(x_.T, mask_.T) + + + # Generate some text after each 20 minibatches + if iteration % 40 == 0: + try: + prediction = numpy.ones(111, dtype=config.floatX) / 111.0 + h_p = numpy.zeros((n_h,), dtype=config.floatX) + c_p = numpy.zeros((n_h,), dtype=config.floatX) + initial = 'the meaning of life is ' + sentence = initial + for char in initial: + x_t = dictionary[char] + prediction, h_p, c_p = single_step(x_t, h_p.flatten(), + c_p.flatten()) + sample = numpy.random.multinomial(1, prediction.flatten()) + for i in range(450): + x_t = numpy.argmax(sample) + prediction, h_p, c_p = single_step(x_t, h_p.flatten(), + c_p.flatten()) + sentence += reverse_mapping[x_t] + sample = numpy.random.multinomial(1, prediction.flatten()) + print 'LSTM: "' + sentence + '"' + except ValueError: + print 'Something went wrong during sentence generation.' + + if iteration % 40 == 0: + print 'epoch:', epoch, ' minibatch:', iteration + val_scores = [] + for x_val, mask_val in val_stream.get_epoch_iterator(): + val_scores.append(evaluate_model(x_val.T, mask_val.T)) + print 'Average validation CE per sentence:', numpy.mean(val_scores) + + end_time = time.clock() + print('Optimization complete.') + print('The code ran for %.2fm' % ((end_time - start_time) / 60.)) + + +if __name__ == '__main__': + train_model() diff --git a/deep-learning/theano-tutorial/rnn_tutorial/rnn_lstm.pdf b/deep-learning/theano-tutorial/rnn_tutorial/rnn_lstm.pdf new file mode 100644 index 0000000000000000000000000000000000000000..225c2550c1e35893901d53930967a1b9ce37f60f GIT binary patch literal 2950544 zcmeF42Y6janfI4{2uo&Zp)P?nwq;pXuUGA=S-n`*k}O-6Wm#R8E!&aAXF z>+JN(%q;OBudbb#ojO9E_k(MOdslu~^0?36zUPk0XMNyl7me3`@{WCLx4txWvj2xq zXn)tX_UZKN?!Nmsm%RS2$J{fpcG<+PS3dct7rdwKoU2T6Fu@$@6lxa5V|e}3+(uDSP$s^8sw_O=T?_|(#W{q(@g&w0@^TkfnK zZF=haUbJQVRa^da&A;?~vAp+RPE6%o`{4&~=)G#}C#9R8wCVD99(vV@7rpoXUFYt8 z@hczy$ICx**ZIe8ti0$g$KUt*E6+Rj^DjME^ZSdww)WffS=Zh7oCp5%hTS7i>3`}4 zSyicH*UWwQzpj4# 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