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graph_recognition_w_attn/__pycache__/model.cpython-312.pyc

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replicated mecc
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Updated data generation code
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replicated mecc
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Initializes the weights and biases in a linear layer.
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training and plotting updates
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<20>3/home/letpon/code/graph_recognition_w_attn/model.py<70>init_linear_layerr
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replicated mecc
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Initializes all model parameters. Returns a pytree
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num_agents<EFBFBD> embedding_dimr<00> input_dim<69>
training and plotting updates
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output_dim)rr<00> key_embed<65> key_translate<74> key_attn_proj<6F>key_headrs r<00>init_fnr/s<><00><00>
replicated mecc
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9?<3F> <0C> <0C>S<EFBFBD>!<21>8L<38>5<>I<EFBFBD>}<7D>m<EFBFBD>X<EFBFBD> <15>v<EFBFBD>}<7D>}<7D>Y<EFBFBD>v<EFBFBD>7H<37>7H<37>&<26>J^<5E>J^<5E>6_<36>`<60>
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Updated data generation code
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Model's forward function. Takes in the parameters and input timesteps, returns predictions
replicated mecc
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r"rr$rr!<00><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><01>axisr#rr<00>r%)
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batch_sizer'<00>_<> agent_embed<65> attn_proj_out<75>k<>q<>v<>
att_scores<EFBFBD> att_weights<74>weighted_average<67>
training and plotting updates
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predictions r<00>forwardrF/s<><00><00>!0<> 5<> 5<><1D>J<EFBFBD>
replicated mecc
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Calculates and returns the learned attention matrix between agents.
This is a pure function for analysis.
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training and plotting updates
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embeddingsr>r?r@<00> attn_scoress r<00>get_attention_fnrLBst<00><00>
replicated mecc
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<18>*<2A>+<2B>H<EFBFBD>5<>J<EFBFBD><1F><16> <0B>!4<>S<EFBFBD>!9<>9<>M<EFBFBD> <0E>9<EFBFBD>9<EFBFBD>]<5D>A<EFBFBD>B<EFBFBD> /<2F>D<EFBFBD>A<EFBFBD>q<EFBFBD><15>q<EFBFBD>s<EFBFBD>s<EFBFBD>7<EFBFBD>c<EFBFBD>h<EFBFBD>h<EFBFBD>q<EFBFBD>w<EFBFBD>w<EFBFBD>r<EFBFBD>{<7B>3<>3<>K<EFBFBD> <0E>;<3B>;<3B>{<7B> #<23>#r<00>inputs<74>targets<74>
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training and plotting updates
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<00><00>}|S<00>N)rFr<00>mean<61>abs)<06>p<>x_batch<63>y_batchr<00> predictions<6E>losss r<00>loss_fnztrain_model.<locals>.loss_fn^s1<00><00><1D>a<EFBFBD><17>&<26>1<> <0B><12>x<EFBFBD>x<EFBFBD><03><07><07> <0B>g<EFBFBD> 5<>6<>7<><04><13> rr)<01>static_argnamesc<00><><00><02>tj<00>
replicated mecc
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training and plotting updates
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new_paramsr[<00> optimizers <20><>r<00> update_stepz train_model.<locals>.update_stepcs_<00><><00>6<>#<23>,<2C>,<2C>W<EFBFBD>5<>f<EFBFBD>g<EFBFBD>w<EFBFBD>PV<50>W<><0F><08>%<25>!*<2A>!1<>!1<>%<25><19>F<EFBFBD>!K<><1E><07><1D><1A>(<28>(<28><16><17>9<>
replicated mecc
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<EFBFBD><19>=<3D>(<28>2<>2r<00>epoch_gr5<00>
zEpoch <20>3dz | Loss: z.6f)<03> loss_history<72>graphsrO)r<00>PRNGKeyrr/r`<00>adamw<6D> learning_rate<74>initrr8<00>jit<69>range<67>epochs<68>len<65>append<6E>verbose<73>print<6E>log<6F>log_epoch_intervalrL)rrMrNrOrPr<00>init_keyrrbri<00>irmrn<00> num_batches<65>epoch<63> running_loss<73> batch_num<75>x<>yrc<00>
training and plotting updates
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epoch_loss<EFBFBD>attn<74>all_logsr[rhs @@r<00> train_modelr<6C>Rs<00><><00>
replicated mecc
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<11>.<2E>.<2E><11>
<1B>C<EFBFBD><1A>L<EFBFBD>L<EFBFBD><13>%<25>M<EFBFBD>C<EFBFBD><18> <14>X<EFBFBD>v<EFBFBD> &<26>F<EFBFBD><15> <0B> <0B>L<EFBFBD>6<>6<>7<>I<EFBFBD><19><0E><0E>v<EFBFBD>&<26>I<EFBFBD><14>
Updated data generation code
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 <0A>S<EFBFBD>W<EFBFBD>W<EFBFBD>x<EFBFBD>j<EFBFBD>1<>3<>2<>3<>/4<>L<EFBFBD>4G<34>4G<34>.H<>I<><11>f<EFBFBD>Q<EFBFBD>C<EFBFBD>L<EFBFBD>"<22>$<24>I<>L<EFBFBD>I<>(-<2D>l<EFBFBD>.A<>.A<>(B<> C<>1<EFBFBD><06>q<EFBFBD>c<EFBFBD>l<EFBFBD>B<EFBFBD><1E> C<>F<EFBFBD> C<><15>f<EFBFBD>+<2B>K<EFBFBD><16>|<7C>*<2A>*<2A>+<2B>2<><05><1A> <0C><1E>{<7B>+<2B> %<25>I<EFBFBD><19>)<29>$<24>g<EFBFBD>i<EFBFBD>&8<>q<EFBFBD>A<EFBFBD>*5<>f<EFBFBD>i<EFBFBD><11>A<EFBFBD>v<EFBFBD>*V<> '<27>F<EFBFBD>I<EFBFBD>x<EFBFBD> <18>H<EFBFBD> $<24>L<EFBFBD>  %<25>"<22>K<EFBFBD>/<2F>
replicated mecc
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<EFBFBD><14>v<EFBFBD>e<EFBFBD>W<EFBFBD>%<25>&<26>-<2D>-<2D>j<EFBFBD>9<> <17> <1F> <1F>U<EFBFBD>Q<EFBFBD>Y<EFBFBD>"<22>$4<><01>$9<> <11>F<EFBFBD>5<EFBFBD><11>7<EFBFBD>2<EFBFBD>,<2C>i<EFBFBD>
Updated data generation code
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