add readme

Author: hyoo

examples/mnist/README.md

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+# MNIST Example
+
+This example demonstrate how to convert keras code into CANDLE compliant. 
+Please refer [tutorial](https://ecp-candle.github.io/Candle/html/tutorials/writing_candle_code.html) for more detail.
+
+Here is the list of files,
+
+- mnist.py: CANDLE class
+- mnist_cnn.py and mnist_mlp.py: original mnist implementation from keras project
+- mnist_cnn_candle.py: mnist_cnn.py converted in CANDLE compliant mode
+- mnist_mlp_candle.py: mnist_mlp.py converted in CANDLE compliant mode
+- mnist_params.txt: model parameters are stored in a file for reproduciblity
+
+
+```
+$ python mnist_cnn_candle.py -e 3
+Using TensorFlow backend.
+
+Importing candle utils for keras
+Params:
+{'activation': 'relu',
+'batch_size': 128,
+'data_type': <class 'numpy.float32'>,
+'epochs': 3,
+'experiment_id': 'EXP000',
+'gpus': [],
+'logfile': None,
+'optimizer': 'rmsprop',
+'output_dir': '/Users/hsyoo/projects/CANDLE/Benchmarks/examples/mnist/Output/EXP000/RUN000',
+'profiling': False,
+'rng_seed': 7102,
+'run_id': 'RUN000',
+'shuffle': False,
+'timeout': -1,
+'train_bool': True,
+'verbose': None}
+Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
+11493376/11490434 [==============================] - 2s 0us/step
+x_train shape: (60000, 28, 28, 1)
+60000 train samples
+10000 test samples
+Instructions for updating:
+Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
+
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+Train on 60000 samples, validate on 10000 samples
+Epoch 1/3
+2020-05-13 11:53:17.373979: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA
+To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
+2020-05-13 11:53:17.374474: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 16. Tune using inter_op_parallelism_threads for best performance.
+60000/60000 [==============================] - 56s 932us/step - loss: 0.2719 - acc: 0.9157 - val_loss: 0.0683 - val_acc: 0.9774
+Epoch 2/3
+60000/60000 [==============================] - 55s 909us/step - loss: 0.0904 - acc: 0.9733 - val_loss: 0.0411 - val_acc: 0.9872
+Epoch 3/3
+60000/60000 [==============================] - 55s 909us/step - loss: 0.0666 - acc: 0.9808 - val_loss: 0.0339 - val_acc: 0.9893
+Test loss: 0.03386178284487105
+Test accuracy: 0.9893
+```