mnist_cnn.py

#!/usr/bin/env python3

from __future__ import print_function
import os, sys, json, traceback, gzip
import numpy as np

import keras
from keras.datasets import mnist
from keras.models import Sequential, save_mxnet_model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

from keras.utils import multi_gpu_model

# SageMaker paths
prefix      = '/opt/ml/'
input_path  = prefix + 'input/data/'
output_path = os.path.join(prefix, 'output')
model_path  = os.path.join(prefix, 'model')
param_path  = os.path.join(prefix, 'input/config/hyperparameters.json')
data_path   = os.path.join(prefix, 'input/config/inputdataconfig.json')

# Load MNIST data copied by SageMaker
def load_data(input_path):
    # Adapted from https://github.com/keras-team/keras/blob/master/keras/datasets/fashion_mnist.py

    # Training and validation files
    files = ['training/train-labels-idx1-ubyte.gz', 'training/train-images-idx3-ubyte.gz',
             'validation/t10k-labels-idx1-ubyte.gz', 'validation/t10k-images-idx3-ubyte.gz']
    # Load training labels
    with gzip.open(input_path+files[0], 'rb') as lbpath:
        y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)
    # Load training samples
    with gzip.open(input_path+files[1], 'rb') as imgpath:
        x_train = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)
    # Load validation labels
    with gzip.open(input_path+files[2], 'rb') as lbpath:
        y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)
    # Load validation samples
    with gzip.open(input_path+files[3], 'rb') as imgpath:
        x_test = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)
    print("Files loaded")
    return (x_train, y_train), (x_test, y_test)

# Main code
try:
    # Read hyper parameters passed by SageMaker
    with open(param_path, 'r') as params:
        hyperParams = json.load(params)
    print("Hyper parameters: " + str(hyperParams))
    
    lr = float(hyperParams.get('lr', '0.1'))
    batch_size = int(hyperParams.get('batch_size', '128'))
    epochs = int(hyperParams.get('epochs', '10'))
    gpu_count = int(hyperParams.get('gpu_count', '0'))
               
    # Read input data config passed by SageMaker
    with open(data_path, 'r') as params:
        inputParams = json.load(params)
    print("Input parameters: " + str(inputParams))

    num_classes = 10
    # input image dimensions
    img_rows, img_cols = 28, 28

    # the data, split between train and test sets
    (x_train, y_train), (x_test, y_test) = load_data(input_path)

    if K.image_data_format() == 'channels_first':
        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1)

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    print('x_train shape:', x_train.shape)
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    if gpu_count > 1:
        model = multi_gpu_model(model, gpus=gpu_count)
        
    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=keras.optimizers.Adam(),
                  metrics=['accuracy'])

    model.fit(x_train, y_train,
              batch_size=batch_size,
              epochs=epochs,
              verbose=1,
              validation_data=(x_test, y_test))
    score = model.evaluate(x_test, y_test, verbose=0)
    
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])
    
    model_name='mnist-cnn-'+str(epochs)
    model.save(model_path+'/'+model_name+'.hd5') # Keras model
    print("Saved Keras model")
    save_mxnet_model(model=model, prefix=model_path+'/'+model_name) # MXNet model
    print("Saved MXNet model")

    sys.exit(0)
except Exception as e:
        # Write out an error file. This will be returned as the failureReason in the
        # DescribeTrainingJob result.
        trc = traceback.format_exc()
        with open(os.path.join(output_path, 'failure'), 'w') as s:
            s.write('Exception during training: ' + str(e) + '\n' + trc)
        # Printing this causes the exception to be in the training job logs, as well.
        print('Exception during training: ' + str(e) + '\n' + trc, file=sys.stderr)
        # A non-zero exit code causes the training job to be marked as Failed.
        sys.exit(255)