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Create .new_graph() after computation graph is renewed.r])r ess rstart_new_sequencez_RNNBuilder.start_new_sequences+ CKKM )=K2L,L )r ct||jtjk7r t dt j |j|jj|jSr rcr^r_r`rar from_cexprthisptr add_inputr)r rs rrz_RNNBuilder.add_inputs\q CKKM )=K2L,L  " "4??DLL4J4J13354Q RRr ct||jtjk7r t dt j |j|jj||jSrr)r prevrs radd_input_to_prevz_RNNBuilder.add_input_to_prevsbq CKKM )=K2L,L  " "4??DLL4J4J4QRQTQTQV4W XXr c|jtjk7r td|jj yr)r^r_r`rarrewind_one_stepr)s rrz_RNNBuilder.rewind_one_steps8 CKKM )=K2L,L ll""$r c|jtjk7r tdtj |j|j jSr)r^r_r`rarrrbackr)s rrz_RNNBuilder.backsL CKKM )=K2L,L  " "4??DLL4E4E4G HHr c|jtjk7r tdg}|jj }|D]1}|j tj|j|3|Sr) r^r_r`rarfinal_hrrrr rescexpscexps rrz_RNNBuilder.final_hk CKKM )=K2L,L cll""$e$ **Z**4??DA B jr c|jtjk7r tdg}|jj }|D]1}|j tj|j|3|Sr) r^r_r`rarfinal_srrrrs rrz_RNNBuilder.final_srr c|jtjk7r tdg}|jj |}|D]1}|j tj|j|3|Sr) r^r_r`rarget_hrrrr rrrrs rrz_RNNBuilder.get_hm CKKM )=K2L,L cll  #e$ **Z**4??DA B jr c|jtjk7r tdg}|jj |}|D]1}|j tj|j|3|Sr) r^r_r`rarget_srrrrs rrz_RNNBuilder.get_srr c|j!|jtjk7rE|j ||j |n|j t |d|_|jSr) _init_stater^r_r`rrRNNState)r vecss r initial_statez_RNNBuilder.initial_statesd   !T__ %E ..   %%d+%%'%dB/$    r c||j!|jtjk7r|j |Qg}|D]8}t t |}|j||j|:|j|n|jt|d|_|jSr) rr^r_r`rrrUrrrr)r rrrrs rinitial_state_from_raw_vectorsz*_RNNBuilder.initial_state_from_raw_vectorss   !T__ %E ..   b!s1v&!%%())A,%%b)%%'%dB/$    r r)r r r rrrrrrrrrrrrrrr r rrrsH !/LS Y %I r rceZdZdZdZy)SimpleRNNBuildercvd|_||_||_||_||_d|_t |_yrr^layers input_dim hidden_dimmodelrrrr rrrrs rr!zSimpleRNNBuilder.__init__8DODKDN DODJD*,Dr cy)Nrr r)s rwhoamizSimpleRNNBuilder.whoamis-r Nr r r r!rr r rrrs -.r rceZdZdZdZy) GRUBuildercvd|_||_||_||_||_d|_t |_yrrrs rr!zGRUBuilder.__init__rr cy)Nrr r)s rrzGRUBuilder.whoamis|dkDsJ|dzdk(sJg|_|d||dz |}|d||dz |}|jj||ft|dz D];}|d||dz |}|d||dz |}|jj||f=y)a @param num_layers: depth of the BiRNN @param input_dim: size of the inputs @param hidden_dim: size of the outputs (and intermediate layer representations) @param model @param rnn_builder_factory: RNNBuilder subclass, e.g. LSTMBuilder rrrN)builder_layersrxrange) r num_layersrrrrnn_builder_factoryrrZ_s rr!zBiRNNBuilder.__init__sA~~A~"""  9jlE B 9jlE B ""Aa5) 1 %A#Az:a<GA#Az:a<GA    & &!u -&r cy)Nrr r)s rrzBiRNNBuilder.whoami+s^r cp|jD]'\}}|j||j|)yr)rr)r rfbbbs rrzBiRNNBuilder.set_dropout-s.(('2b q q)r cl|jD]%\}}|j|j'yr)rr)r rrs rrzBiRNNBuilder.disable_dropout1s.(('2b  )r c d|D] }t||jddD]~\}}|jj|}|jjt |}t |t |Dcgc]\}}t ||g}}}|jd\}}|jj|}|jjt |}t |t |Dcgc] \}}||f c}}Scc}}wcc}}w)a returns the list of state pairs (stateF, stateB) obtained by adding inputs to both forward (stateF) and backward (stateB) RNNs. @param es: a list of Expression see also transduce(xs) .transduce(xs) is different from .add_inputs(xs) in the following way: .add_inputs(xs) returns a list of RNNState pairs. RNNState objects can be queried in various ways. In particular, they allow access to the previous state, as well as to the state-vectors (h() and s() ) .transduce(xs) returns a list of Expression. These are just the output expressions. For many cases, this suffices. transduce is much more memory efficient than add_inputs. Nr)rcrr transducereversedrr add_inputs r rrrrfsbsrrZs rrzBiRNNBuilder.add_inputs6s&A Q **3B/GR!!#--b1B!!#--hrl;B03B 0EF0E1+qe$0EBF0%%b)B     * *2 .     * *8B< 8!$R"!67!6#!A1!677 G8s D&D,c P|D] }t||jD]~\}}|jj|}|jjt |}t |t |Dcgc]\}}t ||g}}}|Scc}}w)a1 returns the list of output Expressions obtained by adding the given inputs to the current state, one by one, to both the forward and backward RNNs, and concatenating. @param es: a list of Expression see also add_inputs(xs) .transduce(xs) is different from .add_inputs(xs) in the following way: .add_inputs(xs) returns a list of RNNState pairs. RNNState objects can be queried in various ways. In particular, they allow access to the previous state, as well as to the state-vectors (h() and s() ) .transduce(xs) returns a list of Expression. These are just the output expressions. For many cases, this suffices. transduce is much more memory efficient than add_inputs. )rcrrrrrrr s rrzBiRNNBuilder.transduceTs(A Q **GR!!#--b1B!!#--hrl;B03B 0EF0E1+qe$0EBF+ GsB"N) r r r __doc__r!rrrrrr r rrrs% .&, 8<r rc>eZdZd dZdZdZdZdZdZdZ d Z y) rNc<||_||_||_||_yr)builder state_idx_prev_out)r r'r( prev_stateouts rr!zRNNState.__init__qsdldndjdir c t|jj}|j|k(r |jnt |j|}|jj }|j dr|dtd }td||||jj|jdzgt|jj}t|j|jdz||}|S)NBuilderrrr) r[r'rrfrBrendswithrUrurr(rr)r rorrnn_typeoutput_e new_states rrzRNNState.add_inputws4<<112i55)+!%%1CAEE91Ui$$&h   9 %(;KS^O2Lx Q 8T\\=Y=Y[_[i[ijk[k$lrz{|H|H|S|SsTUh4<<)94Ji r c|jt|j_g}|}|D]$}|j |}|j |&|Sr)r)rr'rrrr rnstatescurros rrzRNNState.add_inputssP :: )8):$,, &A--"C MM#  r cf|j|Dcgc]}|jc}Scc}wr)routput)r rnros rrzRNNState.transduces+$(OOB$78$7q $7888s.c|jSr)r*r)s rr8zRNNState.outputs TYY&r c|jSr)r)r)s rrz RNNState.prevs 4::%r c|jSr)r'r)s rrZz RNNState.bs  $r c|jSr)r(r)s r get_state_idxzRNNState.get_state_idxs DNN2r )rNN) r r r r!rrrr8rrZr=r r rrrps$ 9'%$2r rc8eZdZd dZdZdZdZdZdZdZ y) StackedRNNStateNc ||_||_yr)r5r)r r5rs rr!zStackedRNNState.__init__s  r cg}|jD]5}|j|j||dj}7t ||Sr)r5rrr8r?)r ro next_statesrs rrzStackedRNNState.add_inputsL A   q{{1~ .B&&(A{D11r c<|jdjSr)r5r8r)s rr8zStackedRNNState.outputsT[[_3355r c|jSr)rr)s rrzStackedRNNState.prevs 499$r c\|jDcgc]}|jc}Scc}wr)r5hr rs rrFzStackedRNNState.h# 4 1 444)c\|jDcgc]}|jc}Scc}wr)r5rrGs rrzStackedRNNState.srHrIc`g}|}|D]$}|j|}|j|&|S)zz returns the list of states obtained by adding the given inputs to the current state, one by one. )rrr4s rrzStackedRNNState.add_inputss9 A--"C MM#  r r) r r r r!rr8rrFrrr r rr?r?s26$44 r r?c.eZdZddZddZdZdZdZy)Trainercyrr rGs rr)zTrainer.updaterr cyrr )r rs r update_epochzTrainer.update_epochs4r cyrr r)s rstatuszTrainer.statusrr cyrr )r thrs rset_clip_thresholdzTrainer.set_clip_thresholdrHr cyrr r)s rget_clip_thresholdzTrainer.get_clip_thresholdsr Ng?)r r r r)rQrSrVrXr r rrMrMs')$r rMceZdZdZddZy)SimpleSGDTrainerz# This object is very cool! 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