ML Practioner | ML 11 - XGBoost notebook | cannot import keras.applications.resnet50

[Slalom_Tobias]

the following code...

from sparkdl.xgboost import XgboostRegressor

from pyspark.ml import Pipeline

params = {"n_estimators": 100, "learning_rate": 0.1, "max_depth": 4, "random_state": 42, "missing": 0}

xgboost = XgboostRegressor(**params)

pipeline = Pipeline(stages=[string_indexer, vec_assembler, xgboost])

pipeline_model = pipeline.fit(train_df)

generates the following error...

ImportError: cannot import name 'resnet50' from 'keras.applications' (/local_disk0/.ephemeral_nfs/envs/pythonEnv-d671417d-c26e-4e1b-ab99-6ea93e646ec3/lib/python3.8/site-packages/keras/applications/__init__.py)

i've looked at the modules in keras.applications and as the screenshots show, there is no resnet50, so I think a dependency needs to be updated.

[Prabakar · Accepted solution]

@Tobias Cortese​  I could see you are using the standard DBR version and it is expected. Please use the ML DBR version and it should resolve your issue.

[SaraGHn]

@Prabakar Ammeappin​  I have the same issue. I am getting error and I don’t understand what do you man by ML DBR version. Can you help me.

 

[Prabakar]

You need to choose the runtime for ML instead of the standard.