add example

docs/docs/APIGuide/Module.md

@@ -120,6 +120,24 @@ model = Model.load_tensorflow(model_def, inputs, outputs, byte_order = "little_e
 model = Model.load_tensorflow(model_def, inputs, outputs, byte_order = "little_endian", bigdl_type="float", bin_file=model_variable)
 ```
 
+### Load Keras model
+
+BigDL also supports loading pre-defined Keras models. See [keras support](https://bigdl-project.github.io/master/#ProgrammingGuide/keras-support/)
+for more details. The Keras version we support and test is [__Keras 1.2.2__](https://faroit.github.io/keras-docs/1.2.2/).
+
+A Keras model definition in __JSON__ file can be loaded as a BigDL model.
+Saved weights in __HDF5__ file can also be loaded together with the architecture of a model.
+
+You can directly use the API `load_keras` to load the Keras model into BigDL.
+
+__Remark__: `tensorflow`, `keras==1.2.2` and `h5py` need to be installed beforehand. They can be installed via `pip` easily.
+
+```python
+from bigdl.nn.layer import *
+
+bigdl_model = Model.load_keras(def_path, weights_path=None, by_name=False)
+```
+
 ## Model Evaluation
 **Scala**
 ```scala

docs/docs/ProgrammingGuide/keras-support.md

@@ -7,12 +7,12 @@ The Keras version we support and test is [__Keras 1.2.2__](https://faroit.github
 ## **Loading a Keras model into BigDL**
 
 A Keras model definition in __JSON__ file can be loaded as a BigDL model.
-You can also load saved weights in __HDF5__ file to the model.
+Saved weights in __HDF5__ file can also be loaded together with the architecture of a model.
 See [here](https://faroit.github.io/keras-docs/1.2.2/getting-started/faq/#how-can-i-save-a-keras-model) on how to save the architecture and weights of a Keras model.
 
-The API `load_keras` can be used directly to load the Keras model into BigDL.
+You can directly use the API `load_keras` to load the Keras model into BigDL.
 
-__Remark__: `tensorflow`, `keras==1.2.2` and `h5py` need to be installed beforehand. You can use `pip` to install them easily.
+__Remark__: `tensorflow`, `keras==1.2.2` and `h5py` need to be installed beforehand. They can be installed via `pip` easily.
 
 ```python
 from bigdl.nn.layer import *
@@ -25,6 +25,81 @@ Parameters:
 * `weights_path`  The HDF5 file path containing the pre-trained keras model weights. Default to be `None` if you choose not to load weights. In this case, initialized weights will be used for the model.
 * `by_name`  Whether to load the weights of layers by name. Use this option only when you do load the pre-trained weights. Default to be `False`, meaning that  weights are loaded based on the network's execution order topology. Otherwise, only those layers with the same name will be loaded with weights.
 
+## **Example**
+
+Here we illustrate with a concrete [LeNet example](https://github.com/fchollet/keras/blob/1.2.2/examples/mnist_cnn.py) from Keras.
+
+```python
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Convolution2D, MaxPooling2D
+
+# Define a convnet model in Keras
+keras_model = Sequential()
+keras_model.add(Convolution2D(32, 3, 3, border_mode='valid',
+                              input_shape=(1, 28, 28)))
+keras_model.add(Activation('relu'))
+keras_model.add(Convolution2D(32, 3, 3))
+keras_model.add(Activation('relu'))
+keras_model.add(MaxPooling2D(pool_size=(2, 2)))
+keras_model.add(Dropout(0.25))
+
+keras_model.add(Flatten())
+keras_model.add(Dense(128))
+keras_model.add(Activation('relu'))
+keras_model.add(Dropout(0.5))
+keras_model.add(Dense(10))
+keras_model.add(Activation('softmax'))
+
+# Save the keras model to JSON file
+model_json = keras_model.to_json()
+def_path = "lenet.json"
+with open(def_path, "w") as json_file:
+    json_file.write(model_json)
+
+from bigdl.util.common import *
+from bigdl.nn.layer import *
+from bigdl.optim.optimizer import *
+from bigdl.nn.criterion import *
+
+# Load the JSON file to a BigDL model
+bigdl_model = Model.load_keras(def_path=def_path)
+
+# Load data from Keras MNIST dataset
+from keras.datasets import mnist
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
+X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
+X_train /= 255
+X_test /= 255
+
+# Distribute data to form RDDs
+sc = get_spark_context(conf=create_spark_conf())
+redire_spark_logs()
+show_bigdl_info_logs()
+init_engine()
+X_train = sc.parallelize(X_train)
+y_train = sc.parallelize(y_train)
+train_data = X_train.zip(y_train).map(lambda t: Sample.from_ndarray(t[0], t[1] + 1))
+X_test = sc.parallelize(X_test)
+y_test = sc.parallelize(y_test)
+test_data = X_test.zip(y_test).map(lambda t: Sample.from_ndarray(t[0], t[1] + 1))
+optimizer = Optimizer(
+    model=bigdl_model,
+    training_rdd=train_data,
+    criterion=ClassNLLCriterion(),
+    optim_method=SGD(learningrate=0.01),
+    end_trigger=MaxEpoch(12),
+    batch_size=128)
+optimizer.set_validation(
+    batch_size=128,
+    val_rdd=test_data,
+    trigger=EveryEpoch(),
+    val_method=[Top1Accuracy()]
+)
+optimizer.optimize()
+```
+
 ### **Limitation**
 We have tested the model loading functionality with some standard [Keras applications](https://faroit.github.io/keras-docs/1.2.2/applications/) and [examples](https://github.com/fchollet/keras/tree/1.2.2/examples).