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Addressing AnalysisException error in workflow example #77

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Addressing AnalysisException error in workflow example #77

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16 changes: 8 additions & 8 deletions examples/workflow.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -465,13 +465,13 @@
"# 2. Output dimensionality: 5\n",
"# With these parameters, we obtain the following vector in the DataFrame column: [0,0,0,1,0]\n",
"\n",
"transformer = OneHotTransformer(output_dim=nb_classes, input_col=\"label_index\", output_col=\"label\")\n",
"transformer = OneHotTransformer(output_dim=nb_classes, input_col=\"label_index\", output_col=\"label_out\")\n",
"dataset = transformer.transform(dataset)\n",
"# Only select the columns we need (less data shuffling) while training.\n",
"dataset = dataset.select(\"features_normalized\", \"label_index\", \"label\")\n",
"dataset = dataset.select(\"features_normalized\", \"label_index\", \"label_out\")\n",
"\n",
"# Show the expected output vectors of the neural network.\n",
"dataset.select(\"label_index\", \"label\").take(1)"
"dataset.select(\"label_index\", \"label_out\").take(1)"
]
},
{
Expand Down Expand Up @@ -597,7 +597,7 @@
" # Allocate a Distributed Keras Accuracy evaluator.\n",
" evaluator = AccuracyEvaluator(prediction_col=\"prediction_index\", label_col=\"label_index\")\n",
" # Clear the prediction column from the testset.\n",
" test_set = test_set.select(\"features_normalized\", \"label_index\", \"label\")\n",
" test_set = test_set.select(\"features_normalized\", \"label_index\", \"label_out\")\n",
" # Apply a prediction from a trained model.\n",
" predictor = ModelPredictor(keras_model=trained_model, features_col=\"features_normalized\")\n",
" test_set = predictor.predict(test_set)\n",
Expand Down Expand Up @@ -674,7 +674,7 @@
"source": [
"trainer = SingleTrainer(keras_model=model, worker_optimizer=optimizer,\n",
" loss=loss, features_col=\"features_normalized\",\n",
" label_col=\"label\", num_epoch=1, batch_size=32)\n",
" label_col=\"label_out\", num_epoch=1, batch_size=32)\n",
"trained_model = trainer.train(training_set)"
]
},
Expand Down Expand Up @@ -718,7 +718,7 @@
"outputs": [],
"source": [
"trainer = AEASGD(keras_model=model, worker_optimizer=optimizer, loss=loss, num_workers=num_workers, \n",
" batch_size=32, features_col=\"features_normalized\", label_col=\"label\", num_epoch=1,\n",
" batch_size=32, features_col=\"features_normalized\", label_col=\"label_out\", num_epoch=1,\n",
" communication_window=32, rho=5.0, learning_rate=0.1)\n",
"trainer.set_parallelism_factor(1)\n",
"trained_model = trainer.train(training_set)"
Expand Down Expand Up @@ -762,7 +762,7 @@
"outputs": [],
"source": [
"trainer = EAMSGD(keras_model=model, worker_optimizer=optimizer, loss=loss, num_workers=num_workers,\n",
" batch_size=32, features_col=\"features_normalized\", label_col=\"label\", num_epoch=1,\n",
" batch_size=32, features_col=\"features_normalized\", label_col=\"label_out\", num_epoch=1,\n",
" communication_window=32, rho=5.0, learning_rate=0.1, momentum=0.6)\n",
"trainer.set_parallelism_factor(1)\n",
"trained_model = trainer.train(training_set)"
Expand Down Expand Up @@ -800,7 +800,7 @@
"source": [
"trainer = DOWNPOUR(keras_model=model, worker_optimizer=optimizer, loss=loss, num_workers=num_workers,\n",
" batch_size=32, communication_window=5, learning_rate=0.05, num_epoch=1,\n",
" features_col=\"features_normalized\", label_col=\"label\")\n",
" features_col=\"features_normalized\", label_col=\"label_out\")\n",
"trainer.set_parallelism_factor(1)\n",
"trained_model = trainer.train(training_set)"
]
Expand Down