lepton/graph_recognition_w_attn
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graph_recognition_w_attn/__pycache__/model.cpython-312.pyc

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replicated mecc
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training and plotting updates
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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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Model's forward function. Takes in the parameters and inptu timesteps, returns predictions
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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>
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<EFBFBD>A<EFBFBD><18>+<2B>,<2C>X<EFBFBD>6<>K<EFBFBD><15>"<22>"<22>;<3B><1A>Z<EFBFBD><16>I]<5D>I]<5D>0^<5E>_<>K<EFBFBD><1F>&<26><1B>"5<>c<EFBFBD>":<3A>:<3A>M<EFBFBD> <0E>9<EFBFBD>9<EFBFBD>]<5D>A<EFBFBD>B<EFBFBD> /<2F>D<EFBFBD>A<EFBFBD>q<EFBFBD><17>&<26><1B>-<2D>c<EFBFBD>2<>2<>V<EFBFBD>K<EFBFBD>5H<35><13>5M<35>M<>A<EFBFBD><13>a<EFBFBD>k<EFBFBD>k<EFBFBD>!<21>Q<EFBFBD><01>*<2A>*<2A>c<EFBFBD>h<EFBFBD>h<EFBFBD>z<EFBFBD>.B<>B<>J<EFBFBD><15>&<26>&<26>.<2E>.<2E><1A>"<22>.<2E>5<>K<EFBFBD>"<22>Q<EFBFBD><EFBFBD><14>!<21>F<EFBFBD>6<EFBFBD>N<EFBFBD>3<EFBFBD>$7<>7<>&<26><16>.<2E><13>:M<>M<>J<EFBFBD> <15>rc<00><><00>|dd}||ddz}tj|dd<06><07>\}}||jztj|jd<00>z }tj
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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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training and plotting updates
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<EFBFBD>3<EFBFBD>/?<3F>@<40> A<> <17> <1B> <1B><05> <0C>(G<>(G<> G<>1<EFBFBD> L<>#<23>F<EFBFBD>F<EFBFBD>3<>D<EFBFBD> <12>V<EFBFBD>E<EFBFBD>7<EFBFBD>#<23> $<24> +<2B> +<2B>D<EFBFBD> 1<>!2<>&%<25><18> <20><06>H<EFBFBD> <12>8<EFBFBD> <1B><1B><>;J<01><> Cs <00>+ G <04> G)T)r8r<00> jax.numpy<70>numpyr<00>config_rrr`<00> functoolsr<00>Array<61>int<6E>boolrr/<00>dictrFrLr<><00>rr<00><module>r<>s<><00><01>
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