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Towards Better Keras Modeling – Part VI

Towards Better Keras Modeling – Part VI

Posted March 25, 2021 at 1:32 pm
The Alpha Scientist

See the previous installment in this series to learn about Univariate Relationships.

Multivariate Effects

Of course, reality is non-linear and we can’t think about each of these parameters in isolation. Below are a few ways to look at pairs of factors that are logically related.

df_grid = df.reset_index().groupby([“first_neuron”, “hidden_neuron”]).val_loss_improvement.mean().unstack()
ax = sns.heatmap(data=df_grid,cmap = (sns.diverging_palette(10, 220, sep=80, n=7)),annot=True)
ax.set_title(‘Log-Loss Improvement for Varying Shapes’)

# Unsurprisingly, bigger on both layer types is better.

Towards Better Keras Modeling

df_grid = df.reset_index().groupby([“hidden_layers”, “hidden_neuron”]).val_loss_improvement.mean().unstack()
ax = sns.heatmap(data=df_grid,cmap = (sns.diverging_palette(10, 220, sep=80, n=7)),annot=True)
ax.set_title(‘Log-Loss Improvement for Varying Shapes’)

df_grid = df.reset_index().groupby([“dropout”, “hidden_neuron”]).val_loss_improvement.mean().unstack()
ax = sns.heatmap(data=df_grid,cmap = (sns.diverging_palette(10, 220, sep=80, n=7)),annot=True)
ax.set_title(‘Log-Loss Improvement vs Dropout/Size’)

# Surprisingly, dropout didn’t help even at larger geometries

metric = ‘hidden_layers’
ax = sns.boxplot(x=metric, y=”val_loss”, data=df.reset_index(),color=’salmon’)
ax.set_title(f’Validation Loss as function of {metric}’)

# Very interestingly, no hidden layers is MUCH better. Possibly a consequence of
# some deep models with too few neurons to properly represent data

Stay tuned for the next installment in this series to learn more about Relative Importance of Features.

Visit The Alpha Scientist blog to download the complete code:
https://alphascientist.com/hyperparameter_optimization_with_talos.html

The Alpha Scientist blog – Chad is a full-time quantitative trader who has been working on data analytics since before it was cool. He has long balanced his interest in computer science (MS in EE/CS from MIT) with a fascination in markets (CFA designation in 2009). Prior to becoming a full-time quant, he built analytics products and managed teams at software companies across Silicon Valley. If you’ve found this post useful, please follow @data2alpha on Twitter and forward to a friend or colleague who may also find this topic interesting. https://alphascientist.com/

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