Loading deepchem/models/tests/test_reload.py +19 −1 Original line number Diff line number Diff line Loading @@ -1192,7 +1192,8 @@ def test_fittransform_regression_reload(): batch_size=n_samples, fit_transformers=fit_transformers, n_evals=1, optimizer=Adam(learning_rate=0.003, beta1=0.9, beta2=0.999), optimizer=dc.models.optimizers.Adam( learning_rate=0.003, beta1=0.9, beta2=0.999), model_dir=model_dir) # Fit trained model Loading @@ -1201,3 +1202,20 @@ def test_fittransform_regression_reload(): # Eval model on train scores = model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] < .1 reloaded_model = dc.models.MultitaskFitTransformRegressor( n_tasks, [n_features, n_features], dropouts=[0.01], weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)], batch_size=n_samples, fit_transformers=fit_transformers, n_evals=1, optimizer=dc.models.optimizers.Adam( learning_rate=0.003, beta1=0.9, beta2=0.999), model_dir=model_dir) reloaded_model.restore() # Check predictions match on random sample origpred = model.predict(dataset) reloadpred = reloaded_model.predict(dataset) assert np.all(origpred == reloadpred) Loading
deepchem/models/tests/test_reload.py +19 −1 Original line number Diff line number Diff line Loading @@ -1192,7 +1192,8 @@ def test_fittransform_regression_reload(): batch_size=n_samples, fit_transformers=fit_transformers, n_evals=1, optimizer=Adam(learning_rate=0.003, beta1=0.9, beta2=0.999), optimizer=dc.models.optimizers.Adam( learning_rate=0.003, beta1=0.9, beta2=0.999), model_dir=model_dir) # Fit trained model Loading @@ -1201,3 +1202,20 @@ def test_fittransform_regression_reload(): # Eval model on train scores = model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] < .1 reloaded_model = dc.models.MultitaskFitTransformRegressor( n_tasks, [n_features, n_features], dropouts=[0.01], weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)], batch_size=n_samples, fit_transformers=fit_transformers, n_evals=1, optimizer=dc.models.optimizers.Adam( learning_rate=0.003, beta1=0.9, beta2=0.999), model_dir=model_dir) reloaded_model.restore() # Check predictions match on random sample origpred = model.predict(dataset) reloadpred = reloaded_model.predict(dataset) assert np.all(origpred == reloadpred)
