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input_data = {"_train_sample":'/dbfs/mnt/blogs_pl/taxi_fare_feature_eng_train_sample6', "_validate_sample":'/dbfs/mnt/blogs_pl/taxi_fare_feature_eng_validate_sample6',"_test_sample":'/dbfs/mnt/blogs_pl/taxi_fare_feature_eng_test_sample6'}
activation_function = ['relu', 'tanh', 'sigmoid']
for n in range (3):
learning_rate = n+1/1000
batch_size = 512 * (n+1)
for act in activation_function:
input_params = { "_learning_rate":learning_rate, "_steps":100000, "_batch_size":batch_size, "_dataset_size":4000000,\
"_model_dir":'/dbfs/tmp/models', "_activation_function":act,\
"_checkpoints_steps":5000,"_output_path":'/dbfs/mnt/blogs_pl/output1'}
nyt= NYorkTaxiFairPrediction.new_instance(input_params,input_data)
(experimentID, runID) = nyt.mlflow_run(NYorkTaxiFairPrediction.random_key(10))
print("MLflow Run for NYorkTaxiFairPrediction completed with run_id {} and experiment_id {}".format(runID, experimentID))
print("-" * 100)
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