mnist.py

# Copyright 2018-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""

from __future__ import absolute_import, division, print_function

import argparse
import json
import logging as _logging
import os
import sys as _sys

import numpy as np
import tensorflow as tf
from tensorflow.python.platform import tf_logging


def cnn_model_fn(features, labels, mode):
    """Model function for CNN."""
    # Input Layer
    # Reshape X to 4-D tensor: [batch_size, width, height, channels]
    # MNIST images are 28x28 pixels, and have one color channel
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

    # Convolutional Layer #1
    # Computes 32 features using a 5x5 filter with ReLU activation.
    # Padding is added to preserve width and height.
    # Input Tensor Shape: [batch_size, 28, 28, 1]
    # Output Tensor Shape: [batch_size, 28, 28, 32]
    conv1 = tf.layers.conv2d(
        inputs=input_layer, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu
    )

    # Pooling Layer #1
    # First max pooling layer with a 2x2 filter and stride of 2
    # Input Tensor Shape: [batch_size, 28, 28, 32]
    # Output Tensor Shape: [batch_size, 14, 14, 32]
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # Convolutional Layer #2
    # Computes 64 features using a 5x5 filter.
    # Padding is added to preserve width and height.
    # Input Tensor Shape: [batch_size, 14, 14, 32]
    # Output Tensor Shape: [batch_size, 14, 14, 64]
    conv2 = tf.layers.conv2d(
        inputs=pool1, filters=64, kernel_size=[5, 5], padding="same", activation=tf.nn.relu
    )

    # Pooling Layer #2
    # Second max pooling layer with a 2x2 filter and stride of 2
    # Input Tensor Shape: [batch_size, 14, 14, 64]
    # Output Tensor Shape: [batch_size, 7, 7, 64]
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # Flatten tensor into a batch of vectors
    # Input Tensor Shape: [batch_size, 7, 7, 64]
    # Output Tensor Shape: [batch_size, 7 * 7 * 64]
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])

    # Dense Layer
    # Densely connected layer with 1024 neurons
    # Input Tensor Shape: [batch_size, 7 * 7 * 64]
    # Output Tensor Shape: [batch_size, 1024]
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)

    # Add dropout operation; 0.6 probability that element will be kept
    dropout = tf.layers.dropout(
        inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN
    )

    # Logits layer
    # Input Tensor Shape: [batch_size, 1024]
    # Output Tensor Shape: [batch_size, 10]
    logits = tf.layers.dense(inputs=dropout, units=10)

    predictions = {
        # Generate predictions (for PREDICT and EVAL mode)
        "classes": tf.argmax(input=logits, axis=1),
        # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
        # `logging_hook`.
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
    }
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # Calculate Loss (for both TRAIN and EVAL modes)
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    # Add evaluation metrics (for EVAL mode)
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])
    }
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


def _load_training_data(base_dir):
    x_train = np.load(os.path.join(base_dir, "train_data.npy"))
    y_train = np.load(os.path.join(base_dir, "train_labels.npy"))
    return x_train, y_train


def _load_testing_data(base_dir):
    x_test = np.load(os.path.join(base_dir, "eval_data.npy"))
    y_test = np.load(os.path.join(base_dir, "eval_labels.npy"))
    return x_test, y_test


def _parse_args():

    parser = argparse.ArgumentParser()

    # Data, model, and output directories
    # model_dir is always passed in from SageMaker. By default this is a S3 path under the default bucket.
    parser.add_argument("--model_dir", type=str)
    parser.add_argument("--sm-model-dir", type=str, default=os.environ.get("SM_MODEL_DIR"))
    parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAINING"))
    parser.add_argument("--hosts", type=list, default=json.loads(os.environ.get("SM_HOSTS")))
    parser.add_argument("--current-host", type=str, default=os.environ.get("SM_CURRENT_HOST"))

    return parser.parse_known_args()


def serving_input_fn():
    inputs = {"x": tf.placeholder(tf.float32, [None, 784])}
    return tf.estimator.export.ServingInputReceiver(inputs, inputs)


if __name__ == "__main__":
    args, unknown = _parse_args()

    train_data, train_labels = _load_training_data(args.train)
    eval_data, eval_labels = _load_testing_data(args.train)

    # Create the Estimator
    mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir=args.model_dir)

    # Set up logging for predictions
    # Log the values in the "Softmax" tensor with label "probabilities"
    tensors_to_log = {"probabilities": "softmax_tensor"}
    logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

    # Train the model
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x": train_data}, y=train_labels, batch_size=100, num_epochs=None, shuffle=True
    )

    # Evaluate the model and print results
    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x": eval_data}, y=eval_labels, num_epochs=1, shuffle=False
    )

    train_spec = tf.estimator.TrainSpec(train_input_fn, max_steps=20000)
    eval_spec = tf.estimator.EvalSpec(eval_input_fn)
    tf.estimator.train_and_evaluate(mnist_classifier, train_spec, eval_spec)

    if args.current_host == args.hosts[0]:
        mnist_classifier.export_savedmodel(args.sm_model_dir, serving_input_fn)