import?numpy?as?np
import?theano?as?theano
import?theano.tensor?as?T
from?utils?import?*
import?operator

class?RNNTheano:
????
????def?__init__（self?word_dim?hidden_dim=100?bptt_truncate=4）:
????????#?Assign?instance?variables
????????self.word_dim?=?word_dim
????????self.hidden_dim?=?hidden_dim
????????self.bptt_truncate?=?bptt_truncate
????????#?Randomly?initialize?the?network?parameters
????????U?=?np.random.uniform（-np.sqrt（1./word_dim）?np.sqrt（1./word_dim）?（hidden_dim?word_dim））
????????V?=?np.random.uniform（-np.sqrt（1./hidden_dim）?np.sqrt（1./hidden_dim）?（word_dim?hidden_dim））
????????W?=?np.random.uniform（-np.sqrt（1./hidden_dim）?np.sqrt（1./hidden_dim）?（hidden_dim?hidden_dim））
????????#?Theano:?Created?shared?variables
????????self.U?=?theano.shared（name=‘U‘?value=U.astype（theano.config.floatX））
????????self.V?=?theano.shared（name=‘V‘?value=V.astype（theano.config.floatX））
????????self.W?=?theano.shared（name=‘W‘?value=W.astype（theano.config.floatX））??????
????????#?We?store?the?Theano?graph?here
????????self.theano?=?{}
????????self.__theano_build__（）
????
????def?__theano_build__（self）:
????????U?V?W?=?self.U?self.V?self.W
????????x?=?T.ivector（‘x‘）
????????y?=?T.ivector（‘y‘）
????????def?forward_prop_step（x_t?s_t_prev?U?V?W）:
????????????s_t?=?T.tanh（U[:x_t]?+?W.dot（s_t_prev））
????????????o_t?=?T.nnet.softmax（V.dot（s_t））
????????????return?[o_t[0]?s_t]
????????[os]?updates?=?theano.scan（
????????????forward_prop_step
????????????sequences=x
????????????outputs_info=[None?dict（initial=T.zeros（self.hidden_dim））]
????????????non_sequences=[U?V?W]
????????????truncate_gradient=self.bptt_truncate
????????????strict=True）
????????
????????prediction?=?T.argmax（o?axis=1）
????????o_error?=?T.sum（T.nnet.categorical_crossentropy（o?y））
????????
????????#?Gradients
????????dU?=?T.grad（o_error?U）
????????dV?=?T.grad（o_error?V）
????????dW?=?T.grad（o_error?W）
????????
????????#?Assign?functions
????????self.forward_propagation?=?theano.function（[x]?o）
????????self.predict?=?theano.function（[x]?prediction）
????????self.ce_error?=?theano.function（[x?y]?o_error）
????????self.bptt?=?theano.function（[x?y]?[dU?dV?dW]）
????????
????????#?SGD
????????learning_rate?=?T.scalar（‘learning_rate‘）
????????self.sgd_step?=?theano.function（[xylearning_rate]?[]?
??????????????????????updates=[（self.U?self.U?-?learning_rate?*?dU）
??????????????????????????????（self.V?self.V?-?learning_rate?*?dV）
??????????????????????????????（self.W?self.W?-?learning_rate?*?dW）]）
????
????def?calculate_total_loss（self?X?Y）:
????????return?np.sum（[self.ce_error（xy）?for?xy?in?zip（XY）]）
????
????def?calculate_loss（self?X?Y）:
????????#?Divide?calculate_loss?by?the?number?of?words
????????num_words?=?np.sum（[len（y）?for?y?in?Y]）
????????return?self.calculate_total_loss（XY）/float（num_words）???


def?gradient_check_theano（model?x?y?h=0.001?error_threshold=0.01）:
????#?Overwrite?the?bptt?attribute.?We?need?to?backpropagate?all?the?

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2017-10-07?02:14??rnn-tutorial-rnnlm-master\
?????文件??????????29??2017-10-07?02:14??rnn-tutorial-rnnlm-master\.gitignore
?????文件???????11358??2017-10-07?02:14??rnn-tutorial-rnnlm-master\LICENSE
?????文件??????????64??2017-10-07?02:14??rnn-tutorial-rnnlm-master\NOTICE
?????文件????????2008??2017-10-07?02:14??rnn-tutorial-rnnlm-master\README.md
?????文件???????43265??2017-10-07?02:14??rnn-tutorial-rnnlm-master\RNNLM.ipynb
?????目錄???????????0??2017-10-07?02:14??rnn-tutorial-rnnlm-master\data\
?????文件?????7610868??2017-10-07?02:14??rnn-tutorial-rnnlm-master\data\reddit-comments-2015-08.csv
?????文件?????3210520??2017-10-07?02:14??rnn-tutorial-rnnlm-master\data\trained-model-theano.npz
?????文件?????????773??2017-10-07?02:14??rnn-tutorial-rnnlm-master\requirements.txt
?????文件????????5391??2017-10-07?02:14??rnn-tutorial-rnnlm-master\rnn_theano.py
?????文件????????3965??2017-10-07?02:14??rnn-tutorial-rnnlm-master\train-theano.py
?????文件?????????693??2017-10-07?02:14??rnn-tutorial-rnnlm-master\utils.py