def get_AMLRun():
try:
run = Run.get_submitted_run()
return run
except Exception as e:
print("Caught = {}".format(e.message))
return None
os.makedirs("./outputs", exist_ok=True)
test_data = pd.read_csv(f"{data_path}/test.csv")
print("Shape of test_data:", test_data.shape)
test_data.head()
train_data = pd.read_csv(f"{data_path}/train.csv")
print("Shape of train_data:", train_data.shape)
train_data.head()
sirna_label_encoder = LabelEncoder().fit(train_data.sirna)
joblib.dump(sirna_label_encoder, "./outputs/sirna_label_encoder.joblib")
run = Run.get_submitted_run()
model = models.create_cnn_model()
test_size = 0.025
batch_size = args.batch
run.log("Batch Size", batch_size)
run.log("Test fraction", test_size)
run.log("Training samples", len(train_data))
run.log("Learning rate", learning_rate)
aml_callback = CheckpointCallback(run)
# resampling entire training dataset
train_data = train_data.sample(frac=training_fraction).reset_index(
def main(argv=None):
# get hold of the current run
run = Run.get_submitted_run()
train_evaluate(run)