training_guideline: Reordered cells, clarified doc

notebooks/training_guideline.ipynb

@@ -96,33 +96,7 @@
    "metadata": {},
    "source": [
     "### 1. Train a new model\n",
-    "#### 1.1. Create subfolder to save new model and its parameters.\n",
-    "\n",
-    "For simplicity, the new model will be created under the `models/` folder in the AxonDeepSeg repository. The name of the model folder will be generated automatically using the date and time (to avoid multiple instances)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define path to where the trained model will be saved\n",
-    "dir_name = time.strftime(\"%Y-%m-%d\") + '_' + time.strftime(\"%H-%M-%S\")\n",
-    "path_model = os.path.join('../models/', dir_name)\n",
-    "\n",
-    "# Create directory\n",
-    "if not os.path.exists(path_model):\n",
-    "    os.makedirs(path_model)\n",
-    "\n",
-    "file_config = 'config_network.json'  # file name of network configuration"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.2. Define the name and path of the training set\n",
+    "#### 1.1. Define the name and path of the training set\n",
     "\n",
     "Here we assume that the training data folder has already been created by following the guidelines detailed in [guide_dataset_building.ipynb](https://github.com/neuropoly/axondeepseg/blob/master/notebooks/guide_dataset_building.ipynb).\n",
     "\n",
@@ -147,19 +121,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "trainingset_name = 'TEM_3c_512'\n",
     "path_training = './TEM_striatum/data'  #  folder containing training data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 1.3. Define the configuration parameters of the training\n",
+    "#### 1.2. Define the U-Net architecture and hyper-parameters\n",
     "\n",
     "Here we defined the network and training parameters (i.e. hyperparameters). We use a lighter network than the ones used in the original [AxonDeepSeg article](https://www.nature.com/articles/s41598-018-22181-4), because they require large GPU memory (>12GB). The network below runs on an NVIDIA TitanX in ~30min. Note that the architecture below might not produce satisfactory results on your data so you likely need to play around with the architecture and hyperparameters.\n",
     "\n",
@@ -179,7 +152,7 @@
     "  \"n_classes\": 3,  # Number of classes. For this application, the number of classes should be set to **3** (i.e. axon pixel, myelin pixel, or background pixel).\n",
     "  \"thresholds\": [0, 0.2, 0.8],  # Thresholds for the 3-class classification problem. Do not modify.  \n",
     "  \"trainingset_patchsize\": 512,  # Patch size of the training set in pixels (note that the patches have the same size in both dimensions).  \n",
-    "  \"trainingset\": trainingset_name,  # Name of the training set.\n",
+    "  \"trainingset\": \"TEM_3c_512\",  # Name of the training set.\n",
     "  \"batch_size\": 8,  # Batch size, i.e. the number of training patches used in one iteration of the training. Note that a larger batch size will take more memory.\n",
     "\n",
     "# Network architecture parameters:     \n",
@@ -237,9 +210,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 1.4. Save configuration parameters of the network as configuration file (.json)\n",
+    "#### 1.3. Define training path and save configuration parameters\n",
     "\n",
-    "After the config. parameters of the network to be trained are defined, they are saved into a .json file in the model folder. This .json file keeps tract of the network and model parameters in a structured way."
+    "Here we define the path where the new model will be saved. It is useful to add date+time in path definition in case multiple training are launched (to avoid conflicts).\n",
+    "\n",
+    "The network configuration parameters defined at 1.2. are saved into a .json file in the model folder. This .json file keeps tract of the network and model parameters in a structured way."
    ]
   },
   {
@@ -248,7 +223,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load config file\n",
+    "# Define path to where the trained model will be saved\n",
+    "dir_name = time.strftime(\"%Y-%m-%d\") + '_' + time.strftime(\"%H-%M-%S\")\n",
+    "path_model = os.path.join('../models/', dir_name)\n",
+    "\n",
+    "# Create directory\n",
+    "if not os.path.exists(path_model):\n",
+    "    os.makedirs(path_model)\n",
+    "\n",
+    "# Define file name of network configuration\n",
+    "file_config = 'config_network.json'\n",
+    "\n",
+    "# Load/Write config file (depending if it already exists or not)\n",
     "fname_config = os.path.join(path_model+file_config)\n",
     "if os.path.exists(fname_config):\n",
     "    with open(fname_config, 'r') as fd:\n",
@@ -264,7 +250,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 1.5. Launch the training procedure\n",
+    "#### 1.4. Launch the training procedure\n",
     "\n",
     "The training can be launched by calling the *'train_model'* function. After each epoch, the function will display the loss and accuracy of the model. The model is automatically saved at every 5 epochs."
    ]
@@ -285,9 +271,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 1.6. Monitor the training with Tensorboard\n",
+    "#### 1.5. Monitor the training with Tensorboard\n",
     "\n",
-    "TensorBoard can be used to monitor the training procedure (loss and accuracy graphs, gradients, activations, identify bugs, etc.). To run TensorBoard, activate ADS virtual environment and run:\n",
+    "[TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard) can be used to monitor the training procedure (loss and accuracy graphs, gradients, activations, identify bugs, etc.). To run TensorBoard, activate ADS virtual environment and run:\n",
     "```\n",
     "tensorboard --logdir PATH_MODEL --port 6006\n",
     "```\n",