<jupyter_start><jupyter_text>![unopt](map.png)![opt](func_map.png)![opt2](func_map2.png)<jupyter_code>theano.printing.pydotprint(logpt_sum, outfile="sum.png", var_with_name_simple=True)
theano.printing.pydotprint(func_sum, outfile="func_sum.png", var_with_name_simple=True)
theano.printing.pydotprint(func_sum2, outfile="func_sum2.png", var_with_name_simple=True)<jupyter_output>The output file is available at sum.png
The output file is available at func_sum.png
The output file is available at func_sum2.png<jupyter_text>![unopt](sum.png)![opt](func_sum.png)![opt2](func_sum2.png)<jupyter_code>func_map2_ = logp_func(logpt_map, m.basic_RVs, profile=True)
func_sum2_ = logp_func(logpt_sum, m.basic_RVs, profile=True)
func_map2_.profile.summary()
func_sum2_.profile.summary()
func_map3 = pm.model.ValueGradFunction(logpt_map, m.basic_RVs)
func_sum3 = pm.model.ValueGradFunction(logpt_sum, m.basic_RVs)
theano.printing.pydotprint(func_map3._theano_function, outfile="func_map3.png", var_with_name_simple=True)
theano.printing.pydotprint(func_sum3._theano_function, outfile="func_sum3.png", var_with_name_simple=True)<jupyter_output>The output file is available at func_map3.png
The output file is available at func_sum3.png<jupyter_text>![opt3](func_map3.png)![opt3](func_sum3.png)<jupyter_code>pm.model??<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '589346', 'license': 'MIT', 'n_tokens_mistral': 536, 'n_tokens_neox': 530, 'n_words': 63, 'path': 'Advance_topics/theano_graph_optim/theano_graph_related.ipynb', 'repo_name': 'junpenglao/advance-bayesian-modelling-with-PyMC3', 'size': '1334'}