Loading deepchem/models/keras_model.py +1 −1 Original line number Diff line number Diff line Loading @@ -788,7 +788,7 @@ class KerasModel(Model): if it produces multiple outputs """ generator = self.default_generator( dataset, mode='predict', pad_batches=False) dataset, mode='predict', deterministic=True, pad_batches=False) return self.predict_on_generator( generator, transformers=transformers, Loading deepchem/models/tests/test_reload.py +67 −15 Original line number Diff line number Diff line Loading @@ -550,10 +550,9 @@ def test_weave_classification_reload(): classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) batch_size = 10 batch_size = 3 #model_dir = tempfile.mkdtemp() model_dir = "/tmp/foobarbaz7" model_dir = tempfile.mkdtemp() model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, Loading @@ -563,7 +562,7 @@ def test_weave_classification_reload(): model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=30) model.fit(dataset, nb_epoch=3) # Eval model on train scores = model.evaluate(dataset, [classification_metric]) Loading @@ -572,13 +571,12 @@ def test_weave_classification_reload(): # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Xpred = featurizer(predmols) predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) print("origpred") print(origpred) origpred2 = model.predict(predset) assert np.all(origpred == origpred2) del model.model del model reloaded_model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, Loading @@ -587,13 +585,37 @@ def test_weave_classification_reload(): dropouts=0.0, model_dir=model_dir) reloaded_model.restore() Xproc = reloaded_model.compute_features_on_batch(Xpred) reloadout = reloaded_model.model(Xproc) print("reloadout") print(reloadout) reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) print("reloadpred") print(reloadpred) print("origpred") print(origpred) ## Try re-restore #reloaded_model.restore() #reloadpred = reloaded_model.predict(predset) #assert np.all(origpred == reloadpred) print("np.amax(origpred - reloadpred)") print(np.amax(origpred - reloadpred)) print("np.allclose(origpred, reloadpred)") print(np.allclose(origpred, reloadpred)) # Eval model on train scores = reloaded_model.evaluate(dataset, [classification_metric]) print("scores") print(scores) assert scores[classification_metric.name] > .9 assert np.all(origpred == reloadpred) # TODO: THIS IS FAILING! def test_MPNN_regression_reload(): Loading Loading @@ -637,6 +659,13 @@ def test_MPNN_regression_reload(): scores = model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] > .8 # Custom save save_dir = tempfile.mkdtemp() model.model.save(save_dir) from tensorflow import keras reloaded = keras.models.load_model(save_dir) # Reload trained model reloaded_model = dc.models.MPNNModel( n_tasks, Loading @@ -649,7 +678,12 @@ def test_MPNN_regression_reload(): use_queue=False, mode="regression", model_dir=model_dir) reloaded_model.restore() #reloaded_model.restore() reloaded_model.model = reloaded # Eval model on train scores = reloaded_model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] > .8 # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Loading @@ -657,12 +691,10 @@ def test_MPNN_regression_reload(): predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) print("np.amax(origpred - reloadpred)") print(np.amax(origpred - reloadpred)) assert np.all(origpred == reloadpred) # Eval model on train scores = reloaded_model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] > .8 # TODO: THIS IS FAILING! def test_textCNN_classification_reload(): Loading @@ -682,7 +714,7 @@ def test_textCNN_classification_reload(): classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) char_dict, length = dc.models.TextCNNModel.build_char_dict(dataset) batch_size = 10 batch_size = 3 model_dir = tempfile.mkdtemp() model = dc.models.TextCNNModel( Loading Loading @@ -714,12 +746,25 @@ def test_textCNN_classification_reload(): model_dir=model_dir) reloaded_model.restore() assert len(reloaded_model.model.get_weights()) == len( model.model.get_weights()) for (reloaded, orig) in zip(reloaded_model.model.get_weights(), model.model.get_weights()): assert np.all(reloaded == orig) # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Xpred = featurizer(predmols) predset = dc.data.NumpyDataset(Xpred, ids=predmols) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) Xproc = reloaded_model.smiles_to_seq_batch(np.array(predmols)) reloadout = reloaded_model.model(Xproc) origout = model.model(Xproc) assert len(model.model.layers) == len(reloaded_model.model.layers) assert np.all(origpred == reloadpred) # Eval model on train Loading Loading @@ -809,6 +854,13 @@ def test_graphconvmodel_reload(): scores = model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] >= 0.9 # Custom save save_dir = tempfile.mkdtemp() model.model.save(save_dir) from tensorflow import keras reloaded = keras.models.load_model(save_dir) # Reload trained Model reloaded_model = dc.models.GraphConvModel( len(tasks), Loading Loading
deepchem/models/keras_model.py +1 −1 Original line number Diff line number Diff line Loading @@ -788,7 +788,7 @@ class KerasModel(Model): if it produces multiple outputs """ generator = self.default_generator( dataset, mode='predict', pad_batches=False) dataset, mode='predict', deterministic=True, pad_batches=False) return self.predict_on_generator( generator, transformers=transformers, Loading
deepchem/models/tests/test_reload.py +67 −15 Original line number Diff line number Diff line Loading @@ -550,10 +550,9 @@ def test_weave_classification_reload(): classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) batch_size = 10 batch_size = 3 #model_dir = tempfile.mkdtemp() model_dir = "/tmp/foobarbaz7" model_dir = tempfile.mkdtemp() model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, Loading @@ -563,7 +562,7 @@ def test_weave_classification_reload(): model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=30) model.fit(dataset, nb_epoch=3) # Eval model on train scores = model.evaluate(dataset, [classification_metric]) Loading @@ -572,13 +571,12 @@ def test_weave_classification_reload(): # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Xpred = featurizer(predmols) predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) print("origpred") print(origpred) origpred2 = model.predict(predset) assert np.all(origpred == origpred2) del model.model del model reloaded_model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, Loading @@ -587,13 +585,37 @@ def test_weave_classification_reload(): dropouts=0.0, model_dir=model_dir) reloaded_model.restore() Xproc = reloaded_model.compute_features_on_batch(Xpred) reloadout = reloaded_model.model(Xproc) print("reloadout") print(reloadout) reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) print("reloadpred") print(reloadpred) print("origpred") print(origpred) ## Try re-restore #reloaded_model.restore() #reloadpred = reloaded_model.predict(predset) #assert np.all(origpred == reloadpred) print("np.amax(origpred - reloadpred)") print(np.amax(origpred - reloadpred)) print("np.allclose(origpred, reloadpred)") print(np.allclose(origpred, reloadpred)) # Eval model on train scores = reloaded_model.evaluate(dataset, [classification_metric]) print("scores") print(scores) assert scores[classification_metric.name] > .9 assert np.all(origpred == reloadpred) # TODO: THIS IS FAILING! def test_MPNN_regression_reload(): Loading Loading @@ -637,6 +659,13 @@ def test_MPNN_regression_reload(): scores = model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] > .8 # Custom save save_dir = tempfile.mkdtemp() model.model.save(save_dir) from tensorflow import keras reloaded = keras.models.load_model(save_dir) # Reload trained model reloaded_model = dc.models.MPNNModel( n_tasks, Loading @@ -649,7 +678,12 @@ def test_MPNN_regression_reload(): use_queue=False, mode="regression", model_dir=model_dir) reloaded_model.restore() #reloaded_model.restore() reloaded_model.model = reloaded # Eval model on train scores = reloaded_model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] > .8 # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Loading @@ -657,12 +691,10 @@ def test_MPNN_regression_reload(): predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) print("np.amax(origpred - reloadpred)") print(np.amax(origpred - reloadpred)) assert np.all(origpred == reloadpred) # Eval model on train scores = reloaded_model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] > .8 # TODO: THIS IS FAILING! def test_textCNN_classification_reload(): Loading @@ -682,7 +714,7 @@ def test_textCNN_classification_reload(): classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) char_dict, length = dc.models.TextCNNModel.build_char_dict(dataset) batch_size = 10 batch_size = 3 model_dir = tempfile.mkdtemp() model = dc.models.TextCNNModel( Loading Loading @@ -714,12 +746,25 @@ def test_textCNN_classification_reload(): model_dir=model_dir) reloaded_model.restore() assert len(reloaded_model.model.get_weights()) == len( model.model.get_weights()) for (reloaded, orig) in zip(reloaded_model.model.get_weights(), model.model.get_weights()): assert np.all(reloaded == orig) # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Xpred = featurizer(predmols) predset = dc.data.NumpyDataset(Xpred, ids=predmols) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) Xproc = reloaded_model.smiles_to_seq_batch(np.array(predmols)) reloadout = reloaded_model.model(Xproc) origout = model.model(Xproc) assert len(model.model.layers) == len(reloaded_model.model.layers) assert np.all(origpred == reloadpred) # Eval model on train Loading Loading @@ -809,6 +854,13 @@ def test_graphconvmodel_reload(): scores = model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] >= 0.9 # Custom save save_dir = tempfile.mkdtemp() model.model.save(save_dir) from tensorflow import keras reloaded = keras.models.load_model(save_dir) # Reload trained Model reloaded_model = dc.models.GraphConvModel( len(tasks), Loading
