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"version": "1.0.0",
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"input": "# Deep Learning Tutorial\n\nThe purpose of this tutorial is to walk new users through Deep Learning using H2O Flow. \n\nThose who have never used H2O before should refer to Using Flow - H2O's Web UI for additional instructions on how to run H2O Flow.\n\nFor tips on improving the performance and results of your Deep Learning model, refer to our Definitive Performance Tuning Guide for Deep Learning.\n\n## Using Deep Learning\n\nH2O's Deep Learning functionalities include:\n\n- purely supervised training protocol for regression and classification tasks\n- fast and memory-efficient Java implementations based on columnar compression and fine- grain Map/Reduce\n- multi-threaded and distributed parallel computation to be run on either a single node or a multi-node cluster\n- fully automatic per-weight adaptive learning rate for fast convergence\n- optional specification of learning rate, annealing and momentum options\n- regularization options include L1, L2, dropout, Hogwild! and model averaging to prevent model overfitting\n- grid search for hyperparameter optimization and model selection\n- model checkpointing for reduced run times and model tuning\n- automatic data pre- and post-processing (one-hot encoding and standardization) for categorical and numerical data\n- automatic imputation of missing values\n- automatic tuning of communication vs computation for best performance\n- model export in plain java code for deployment in production environments\n- export of weights and biases as H2O frames\n- additional expert parameters for model tuning\n- deep autoencoders for unsupervised feature learning and anomaly detection capabilities \n\n\n## Getting Started\nThis tutorial uses the publicly available MNIST data set of hand-written digits, where each row contains the 28^(2)=784 raw gray-scale pixel values from 0 to 255 of the digitized digits (0 to 9). \n\nIf you don't have any data of your own to work with, you can find some example datasets at https://archive.ics.uci.edu/ml/index.php.\n\n#### Importing Data\nBefore creating a model, import the data into H2O:\n\n0. Click the **Assist Me!** button (the last button in the row of buttons below the menus).\n  \n\n0. Click the **importFiles** link and enter the file path to the training dataset in the **Search** entry field. \n\n0. Click the **Add all** link to add the file to the import queue, then click the **Import** button. "
},
{
"type": "cs",
"input": "assist"
},
{
"type": "cs",
"input": "importFiles [ \"https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz\" ]"
},
{
"type": "md",
"input": "### Parsing Data\n\nNow, parse the imported data: \n\n0. Click the **Parse these files...** button. \n\n **Note**: The default options typically do not need to be changed unless the data does not parse correctly. \n0. From the drop-down **Parser** list, select the file type of the data set (Auto, XLS, CSV, or SVMLight). \n0. If the data uses a separator, select it from the drop-down **Separator** list. \n0. If the data uses a column header as the first row, select the **First row contains column names** radio button. If the first row contains data, select the **First row contains data** radio button. To have H2O automatically determine if the first row of the dataset contains the column names or data, select the **Auto** radio button. \n0. If the data uses apostrophes ( `'` - also known as single quotes), check the **Enable single quotes as a field quotation character** checkbox. \n0. To delete the imported dataset after the parse is complete, check the **Delete on done** checkbox. \n\n **NOTE**: In general, we recommend enabling this option. Retaining data requires memory resources, but does not aid in modeling because unparsed data cannot be used by H2O.\n\n0. Review the data in the **Edit Column Names and Types** section. The last column, `C785`, must be changed to an enum for a classification model. \n0. Enter `C785` in the *Search by column name* entry field at the top. \n0. Click the drop-down column heading menu for C785 and select `Enum`. \n\n **NOTE**: Make sure the parse is complete by confirming progress is 100% before continuing to the next step, model building. For small datasets, this should only take a few seconds, but larger datasets take longer to parse."
},
{
"type": "cs",
"input": "setupParse paths:[ \"https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz\" ]"
},
{
"type": "cs",
"input": "parseFiles\n paths: [\"https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz\"]\n destination_frame: \"test.hex\"\n parse_type: \"CSV\"\n separator: 44\n number_columns: 785\n single_quotes: false\n column_names: [\"C1\",\"C2\",\"C3\",\"C4\",\"C5\",\"C6\",\"C7\",\"C8\",\"C9\",\"C10\",\"C11\",\"C12\",\"C13\",\"C14\",\"C15\",\"C16\",\"C17\",\"C18\",\"C19\",\"C20\",\"C21\",\"C22\",\"C23\",\"C24\",\"C25\",\"C26\",\"C27\",\"C28\",\"C29\",\"C30\",\"C31\",\"C32\",\"C33\",\"C34\",\"C35\",\"C36\",\"C37\",\"C38\",\"C39\",\"C40\",\"C41\",\"C42\",\"C43\",\"C44\",\"C45\",\"C46\",\"C47\",\"C48\",\"C49\",\"C50\",\"C51\",\"C52\",\"C53\",\"C54\",\"C55\",\"C56\",\"C57\",\"C58\",\"C59\",\"C60\",\"C61\",\"C62\",\"C63\",\"C64\",\"C65\",\"C66\",\"C67\",\"C68\",\"C69\",\"C70\",\"C71\",\"C72\",\"C73\",\"C74\",\"C75\",\"C76\",\"C77\",\"C78\",\"C79\",\"C80\",\"C81\",\"C82\",\"C83\",\"C84\",\"C85\",\"C86\",\"C87\",\"C88\",\"C89\",\"C90\",\"C91\",\"C92\",\"C93\",\"C94\",\"C95\",\"C96\",\"C97\",\"C98\",\"C99\",\"C100\",\"C101\",\"C102\",\"C103\",\"C104\",\"C105\",\"C106\",\"C107\",\"C108\",\"C109\",\"C110\",\"C111\",\"C112\",\"C113\",\"C114\",\"C115\",\"C116\",\"C117\",\"C118\",\"C119\",\"C120\",\"C121\",\"C122\",\"C123\",\"C124\",\"C125\",\"C126\",\"C127\",\"C128\",\"C129\",\"C130\",\"C131\",\"C132\",\"C133\",\"C134\",\"C135\",\"C136\",\"C137\",\"C138\",\"C139\",\"C140\",\"C141\",\"C142\",\"C143\",\"C144\",\"C145\",\"C146\",\"C147\",\"C148\",\"C149\",\"C150\",\"C151\",\"C152\",\"C153\",\"C154\",\"C155\",\"C156\",\"C157\",\"C158\",\"C159\",\"C160\",\"C161\",\"C162\",\"C163\",\"C164\",\"C165\",\"C166\",\"C167\",\"C168\",\"C169\",\"C170\",\"C171\",\"C172\",\"C173\",\"C174\",\"C175\",\"C176\",\"C177\",\"C178\",\"C179\",\"C180\",\"C181\",\"C182\",\"C183\",\"C184\",\"C185\",\"C186\",\"C187\",\"C188\",\"C189\",\"C190\",\"C191\",\"C192\",\"C193\",\"C194\",\"C195\",\"C196\",\"C197\",\"C198\",\"C199\",\"C200\",\"C201\",\"C202\",\"C203\",\"C204\",\"C205\",\"C206\",\"C207\",\"C208\",\"C209\",\"C210\",\"C211\",\"C212\",\"C213\",\"C214\",\"C215\",\"C216\",\"C217\",\"C218\",\"C219\",\"C220\",\"C221\",\"C222\",\"C223\",\"C224\",\"C225\",\"C226\",\"C227\",\"C228\",\"C229\",\"C230\",\"C231\",\"C232\",\"C233\",\"C234\",\"C235\",\"C236\",\"C237\",\"C238\",\"C239\",\"C240\",\"C241\",\"C242\",\"C243\",\"C244\",\"C245\",\"C246\",\"C247\",\"C248\",\"C249\",\"C250\",\"C251\",\"C252\",\"C253\",\"C254\",\"C255\",\"C256\",\"C257\",\"C258\",\"C259\",\"C260\",\"C261\",\"C262\",\"C263\",\"C264\",\"C265\",\"C266\",\"C267\",\"C268\",\"C269\",\"C270\",\"C271\",\"C272\",\"C273\",\"C274\",\"C275\",\"C276\",\"C277\",\"C278\",\"C279\",\"C280\",\"C281\",\"C282\",\"C283\",\"C284\",\"C285\",\"C286\",\"C287\",\"C288\",\"C289\",\"C290\",\"C291\",\"C292\",\"C293\",\"C294\",\"C295\",\"C296\",\"C297\",\"C298\",\"C299\",\"C300\",\"C301\",\"C302\",\"C303\",\"C304\",\"C305\",\"C306\",\"C307\",\"C308\",\"C309\",\"C310\",\"C311\",\"C312\",\"C313\",\"C314\",\"C315\",\"C316\",\"C317\",\"C318\",\"C319\",\"C320\",\"C321\",\"C322\",\"C323\",\"C324\",\"C325\",\"C326\",\"C327\",\"C328\",\"C329\",\"C330\",\"C331\",\"C332\",\"C333\",\"C334\",\"C335\",\"C336\",\"C337\",\"C338\",\"C339\",\"C340\",\"C341\",\"C342\",\"C343\",\"C344\",\"C345\",\"C346\",\"C347\",\"C348\",\"C349\",\"C350\",\"C351\",\"C352\",\"C353\",\"C354\",\"C355\",\"C356\",\"C357\",\"C358\",\"C359\",\"C360\",\"C361\",\"C362\",\"C363\",\"C364\",\"C365\",\"C366\",\"C367\",\"C368\",\"C369\",\"C370\",\"C371\",\"C372\",\"C373\",\"C374\",\"C375\",\"C376\",\"C377\",\"C378\",\"C379\",\"C380\",\"C381\",\"C382\",\"C383\",\"C384\",\"C385\",\"C386\",\"C387\",\"C388\",\"C389\",\"C390\",\"C391\",\"C392\",\"C393\",\"C394\",\"C395\",\"C396\",\"C397\",\"C398\",\"C399\",\"C400\",\"C401\",\"C402\",\"C403\",\"C404\",\"C405\",\"C406\",\"C407\",\"C408\",\"C409\",\"C410\",\"C411\",\"C412\",\"C413\",\"C414\",\"C415\",\"C416\",\"C417\",\"C418\",\"C419\",\"C420\",\"C421\",\"C422\",\"C423\",\"C424\",\"C425\",\"C426\",\"C427\",\"C428\",\"C429\",\"C430\",\"C431\",\"C432\",\"C433\",\"C434\",\"C435\",\"C436\",\"C437\",\"C438\",\"C439\",\"C440\",\"C441\",\"C442\",\"C443\",\"C444\",\"C445\",\"C446\",\"C447\",\"C448\",\"C449\",\"C450\",\"C451\",\"C452\",\"C453\",\"C454\",\"C455\",\"C456\",\"C457\",\"C458\",\"C459\",\"C460\",\"C461\",\"C462\",\"C463\",\"C464\",\"C465\",\"C466\",\"C467\",\"C468\",\"C469\",\"C470\",\"C471\",\"C472\",\"C473\",\"C474\",\"C475\",\"C476\",\"C477\",\"C478\",\"C479\",\"C480\",\"C481\",\"C482\",\"C483\",\"C484\",\"C485\",\"C486\",\"C487\",\"C488\",\"C489\",\"C490\",\"C491\",\"C492\",\"C493\",\"C494\",\"C495\",\"C496\",\"C497\",\"C498\",\"C499\",\"C500\",\"C501\",\"C502\",\"C503\",\"C504\",\"C505\",\"C506\",\"C507\",\"C508\",\"C509\",\"C510\",\"C511\",\"C512\",\"C513\",\"C514\",\"C515\",\"C516\",\"C517\",\"C518\",\"C519\",\"C520\",\"C521\",\"C522\",\"C523\",\"C524\",\"C525\",\"C526\",\"C527\",\"C528\",\"C529\",\"C530\",\"C531\",\"C532\",\"C533\",\"C534\",\"C535\",\"C536\",\"C537\",\"C538\",\"C539\",\"C540\",\"C541\",\"C542\",\"C543\",\"C544\",\"C545\",\"C546\",\"C547\",\"C548\",\"C549\",\"C550\",\"C551\",\"C552\",\"C553\",\"C554\",\"C555\",\"C556\",\"C557\",\"C558\",\"C559\",\"C560\",\"C561\",\"C562\",\"C563\",\"C564\",\"C565\",\"C566\",\"C567\",\"C5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column_types: 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delete_on_done: true\n check_header: 1\n chunk_size: 4194304"
},
{
"type": "md",
"input": "#### Importing and Parsing Training Data\n\nNow, let's import and parse the training dataset using the same methods as with the testing dataset. "
},
{
"type": "cs",
"input": "assist"
},
{
"type": "cs",
"input": "importFiles [ \"https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz\" ]"
},
{
"type": "cs",
"input": "setupParse paths: [ \"https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz\" ]"
},
{
"type": "cs",
"input": "parseFiles\n paths: [\"https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz\"]\n destination_frame: \"train.hex\"\n parse_type: \"CSV\"\n separator: 44\n number_columns: 785\n single_quotes: false\n column_names: 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column_types: [\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numeric\",\"Numer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delete_on_done: true\n check_header: 1\n chunk_size: 4194304"
},
{
"type": "cs",
"input": "getFrameSummary \"train.hex\""
},
{
"type": "md",
"input": "## Building a Model\n\n0. Once data are parsed, click the **View** button, then click the **Build Model** button. \n0. Select `Deep Learning` from the drop-down **Select an algorithm** menu, then click the **Build model** button. \n0. If the parsed training data is not already listed in the **Training_frame** drop-down list, select it. \n\n **Note**: If the **Ignore\\_const\\_col** checkbox is checked, a list of the excluded columns displays below the **Training_frame** drop-down list. \n0. From the drop-down **Validation_frame** list, select the parsed testing `test.hex` dataset as validation data.\n0. From the drop-down **Response** list, select the last column (`C785`). \n0. In the **Hidden** field, specify the hidden layer sizes (for this example, enter `128,64`). \n0. In the **Epochs** field, enter the number of training passes over the dataset (for this example, enter `500`).\n0. From the drop-down **Activation** list, select `RectifierWithDropout`. \n0. Specify some dropout regularization: For **Input\\_dropout\\_ratio**, specify `0.2` and for **Hidden\\_dropout\\_ratios**, specify `0.3,0.2`. \n0. Enable **Variable\\_importances** to compute feature importances.\n0. Enable **Sparse** data handling (helps here since many pixel values are 0).\n0. Disable **Adaptive_rate** and set **Rate** to `0.05`, **Rate\\_annealing** to `1e-6`, **Momentum\\_start** to `0.9`, **Momentum\\_ramp** to `1e6` and **Momentum\\_stable** to `0.99` for manual control over the learning rate and momentum parameters.\n0. Set **Stopping\\_metric** to `misclassification`, **Stopping\\_rounds** to `3` and **Stopping\\_tolerance** to `1e-2` for early stopping based on convergence of the misclassification rate on the validation data set (stops once the moving average (of length 3) of the misclassification rate doesn't improve by at least 1% over 3 consecutive scoring events). Also disable early stopping when the misclassification rate on the training data hits 0 by setting **Classification_stop** to `-1`.\n0. Click the **Build Model** button."
},
{
"type": "cs",
"input": "assist buildModel, null, training_frame: \"train.hex\""
},
{
"type": "cs",
"input": "buildModel 'deeplearning', {\"model_id\":\"deeplearning-d5c35043-8929-441a-9a23-dc44b06b519f\",\"training_frame\":\"train.hex\",\"validation_frame\":\"test.hex\",\"nfolds\":0,\"response_column\":\"C785\",\"ignored_columns\":[],\"ignore_const_cols\":true,\"activation\":\"RectifierWithDropout\",\"hidden\":[128,64],\"epochs\":\"500\",\"variable_importances\":true,\"balance_classes\":false,\"max_confusion_matrix_size\":20,\"checkpoint\":\"\",\"use_all_factor_levels\":true,\"train_samples_per_iteration\":-2,\"adaptive_rate\":false,\"input_dropout_ratio\":0.2,\"hidden_dropout_ratios\":[0.3,0.2],\"l1\":1e-4,\"l2\":1e-4,\"loss\":\"Automatic\",\"distribution\":\"AUTO\",\"score_interval\":5,\"score_training_samples\":10000,\"score_validation_samples\":0,\"score_duty_cycle\":0.1,\"stopping_rounds\":3,\"stopping_metric\":\"misclassification\",\"stopping_tolerance\":1e-2,\"autoencoder\":false,\"overwrite_with_best_model\":true,\"target_ratio_comm_to_comp\":0.05,\"seed\":2517542307834282500,\"rate\":\"0.005\",\"rate_annealing\":1e-6,\"rate_decay\":1,\"momentum_start\":\"0.9\",\"momentum_ramp\":1e6,\"momentum_stable\":\"0.99\",\"nesterov_accelerated_gradient\":true,\"max_w2\":\"Infinity\",\"initial_weight_distribution\":\"UniformAdaptive\",\"classification_stop\":-1,\"score_validation_sampling\":\"Uniform\",\"diagnostics\":true,\"fast_mode\":true,\"force_load_balance\":true,\"single_node_mode\":false,\"shuffle_training_data\":false,\"missing_values_handling\":\"MeanImputation\",\"quiet_mode\":false,\"sparse\":true,\"col_major\":false,\"average_activation\":0,\"sparsity_beta\":0,\"max_categorical_features\":2147483647,\"reproducible\":false,\"export_weights_and_biases\":false,\"elastic_averaging\":false}"
},
{
"type": "md",
"input": "## Results\n\n To view the results, click the **View** button. The output for the Deep Learning model includes the following information for both the training and testing sets: \n\n- Model parameters (hidden)\n- A chart of the variable importances\n- A graph of the scoring history (training MSE and validation MSE vs epochs)\n- Training and validation metrics confusion matrix\n- Output (model category, weights, biases)\n- Status of neuron layers (layer number, units, type, dropout, L1, L2, mean rate, rate RMS, momentum, mean weight, weight RMS, mean bias, bias RMS)\n- Scoring history in tabular format\n- Training and validation metrics (model name, model checksum name, frame name, frame checksum name, description, model category, duration in ms, scoring time, predictions, MSE, R2, logloss)\n- Top-10 Hit Ratios for training and validation\n- Preview POJO"
},
{
"type": "cs",
"input": "getModel \"deeplearning-d5c35043-8929-441a-9a23-dc44b06b519f\""
}
]
}
