def test_all_hyperparameters(sagemaker_session): ntm = NTM(sagemaker_session=sagemaker_session, encoder_layers=[1, 2, 3], epochs=3, encoder_layers_activation='tanh', optimizer='sgd', tolerance=0.05, num_patience_epochs=2, batch_norm=False, rescale_gradient=0.5, clip_gradient=0.5, weight_decay=0.5, learning_rate=0.5, **ALL_REQ_ARGS) assert ntm.hyperparameters() == dict(num_topics=str( ALL_REQ_ARGS['num_topics']), encoder_layers='[1, 2, 3]', epochs='3', encoder_layers_activation='tanh', optimizer='sgd', tolerance='0.05', num_patience_epochs='2', batch_norm='False', rescale_gradient='0.5', clip_gradient='0.5', weight_decay='0.5', learning_rate='0.5')
def test_all_hyperparameters(sagemaker_session): ntm = NTM(sagemaker_session=sagemaker_session, encoder_layers=[1, 2, 3], epochs=3, encoder_layers_activation="tanh", optimizer="sgd", tolerance=0.05, num_patience_epochs=2, batch_norm=False, rescale_gradient=0.5, clip_gradient=0.5, weight_decay=0.5, learning_rate=0.5, **ALL_REQ_ARGS) assert ntm.hyperparameters() == dict( num_topics=str(ALL_REQ_ARGS["num_topics"]), encoder_layers="[1, 2, 3]", epochs="3", encoder_layers_activation="tanh", optimizer="sgd", tolerance="0.05", num_patience_epochs="2", batch_norm="False", rescale_gradient="0.5", clip_gradient="0.5", weight_decay="0.5", learning_rate="0.5", )
def test_all_hyperparameters(sagemaker_session): ntm = NTM(sagemaker_session=sagemaker_session, encoder_layers=[1, 2, 3], epochs=3, encoder_layers_activation='tanh', optimizer='sgd', tolerance=0.05, num_patience_epochs=2, batch_norm=False, rescale_gradient=0.5, clip_gradient=0.5, weight_decay=0.5, learning_rate=0.5, **ALL_REQ_ARGS) assert ntm.hyperparameters() == dict( num_topics=str(ALL_REQ_ARGS['num_topics']), encoder_layers='[1, 2, 3]', epochs='3', encoder_layers_activation='tanh', optimizer='sgd', tolerance='0.05', num_patience_epochs='2', batch_norm='False', rescale_gradient='0.5', clip_gradient='0.5', weight_decay='0.5', learning_rate='0.5' )