[WIP]General Inference notebooks

.gitignore

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+.ipynb_checkpoints
+notebooks/model_tests/*

notebooks/Prediction_bayesian.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import nobrainer\n",
+    "from nobrainer.volume import standardize_numpy\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# image constants\n",
+    "block_shape=(128,128,128)\n",
+    "batch_size = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load sample dataset\n",
+    "csv_path = nobrainer.utils.get_data()\n",
+    "tmp = pd.read_csv(csv_path)\n",
+    "features = tmp['features'].tolist()\n",
+    "image_path = features[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load pre-trained model\n",
+    "model_path = \"/trained-models/neuronets/kwyk/0.4.1/all_50_bvwn_multi_prior/\"\n",
+    "model = tf.keras.models.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict\n",
+    "out,variance, entropy = nobrainer.prediction.predict_from_filepath(image_path,\n",
+    "                                                                   model,\n",
+    "                                                                   block_shape = (128,128,128),\n",
+    "                                                                   batch_size = batch_size,\n",
+    "                                                                   normalizer = standardize_numpy,\n",
+    "                                                                   n_samples=2,\n",
+    "                                                                   return_variance=True,\n",
+    "                                                                   return_entropy=True,\n",
+    "                                                                  )\n",
+    "                                                                  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/Prediction_deterministic.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import nobrainer\n",
+    "from nobrainer.volume import standardize_numpy\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load sample dataset\n",
+    "csv_path = nobrainer.utils.get_data()\n",
+    "tmp = pd.read_csv(csv_path)\n",
+    "features = tmp['features'].tolist()\n",
+    "image_path = features[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# image constants\n",
+    "block_shape=(128,128,128)\n",
+    "batch_size = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load pre-trained model\n",
+    "model_path = \"/trained-models/neuronets/brainy/0.1.0/brain-extraction-unet-128iso-model.h5\"\n",
+    "model = tf.keras.models.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict\n",
+    "out = nobrainer.prediction.predict_from_filepath(image_path, \n",
+    "                                           model,\n",
+    "                                           block_shape = (128,128,128),\n",
+    "                                           batch_size = batch_size,\n",
+    "                                           normalizer = standardize_numpy,\n",
+    "                                             )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}