📝 Add documents about How to resume training with a saved checkpoint …

…file. BrikerMan#117

mkdocs/docs/FAQ.md

@@ -0,0 +1,79 @@
+# FAQ
+
+## How can I run Keras on GPU
+
+Kashgari will use GPU by default if available, but you need to setup the Tensorflow GPU environment first. You can check gpu status using the code below:
+
+```python
+import tensorflow as tf
+print(tf.test.is_gpu_available())
+```
+
+Here is the official document of [TensorFlow-GPU](https://www.tensorflow.org/install/gpu)
+
+## How to save and resume training with ModelCheckpoint callback
+
+You can use [tf.keras.callbacks.ModelCheckpoint](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint) for saving model during training.
+
+```python
+from tensorflow.python.keras.callbacks import ModelCheckpoint
+
+filepath = "saved-model-{epoch:02d}-{acc:.2f}.hdf5"
+checkpoint_callback = ModelCheckpoint(filepath,
+                                      monitor = 'acc',
+                                      verbose = 1)
+
+model = CNN_GRU_Model()
+model.fit(train_x,
+          train_y,
+          valid_x,
+          valid_y,
+          callbacks=[checkpoint_callback])
+```
+
+ModelCheckpoint will save models struct and weights to target file, but we need token dict and label dict to fully restore the model, so we have to save model using `model.save()` function.
+
+So, the full solution will be like this.
+
+```python
+from tensorflow.python.keras.callbacks import ModelCheckpoint
+
+filepath = "saved-model-{epoch:02d}-{acc:.2f}.hdf5"
+checkpoint_callback = ModelCheckpoint(filepath,
+                                      monitor = 'acc',
+                                      verbose = 1)
+
+model = CNN_GRU_Model()
+
+# This function will build token dict, label dict and model struct.
+model.build_model(train_x, train_y, valid_x, valid_y)
+# Save full model info and initial weights to the full_model folder.
+model.save('full_model')
+
+# Start Training
+model.fit(train_x,
+          train_y,
+          valid_x,
+          valid_y,
+          callbacks=[checkpoint_callback])
+
+
+# Load Model
+from kashgari.utils import load_model
+
+# We only need model struct and dicts
+new_model = load_model('full_model', load_weights=False)
+# Load weights from ModelCheckpoint
+new_model.tf_model.load_weights('saved-model-05-0.96.hdf5')
+
+# Resume Training
+# Only need to set {'initial_epoch': 5} when you wish to start new epoch from 6
+# Otherwise epoch will start from 1
+model.fit(train_x,
+          train_y,
+          valid_x,
+          valid_y,
+          callbacks=[checkpoint_callback],
+          epochs=10,
+          fit_kwargs={'initial_epoch': 5})
+```

mkdocs/mkdocs.yml

@@ -87,6 +87,9 @@ nav:
       - api/utils.md
       - api/callbacks.md
 
+  - FAQ:
+      - FAQ.md
+
   - About:
       - about/contributing.md
       - about/release-notes.md