Example migrating to script mode from framework mode (#806)

Confirmed with Marcio that Chuyang Deng is a team member who should have the write permissions. Approving based on her review.

Author: mvsusp

sagemaker-python-sdk/tensorflow_moving_from_framework_mode_to_script_mode/mnist.py

@@ -0,0 +1,160 @@
+import os
+import tensorflow as tf
+from tensorflow.python.estimator.model_fn import ModeKeys as Modes
+
+INPUT_TENSOR_NAME = 'inputs'
+SIGNATURE_NAME = 'predictions'
+
+LEARNING_RATE = 0.001
+
+
+def model_fn(features, labels, mode, params):
+ # Input Layer
+ input_layer = tf.reshape(features[INPUT_TENSOR_NAME], [-1, 28, 28, 1])
+
+ # Convolutional Layer #1
+ conv1 = tf.layers.conv2d(
+ inputs=input_layer,
+ filters=32,
+ kernel_size=[5, 5],
+ padding='same',
+ activation=tf.nn.relu)
+
+ # Pooling Layer #1
+ pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
+
+ # Convolutional Layer #2 and Pooling Layer #2
+ conv2 = tf.layers.conv2d(
+ inputs=pool1,
+ filters=64,
+ kernel_size=[5, 5],
+ padding='same',
+ activation=tf.nn.relu)
+ pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
+
+ # Dense Layer
+ pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
+ dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
+ dropout = tf.layers.dropout(
+ inputs=dense, rate=0.4, training=(mode == Modes.TRAIN))
+
+ # Logits Layer
+ logits = tf.layers.dense(inputs=dropout, units=10)
+
+ # Define operations
+ if mode in (Modes.PREDICT, Modes.EVAL):
+ predicted_indices = tf.argmax(input=logits, axis=1)
+ probabilities = tf.nn.softmax(logits, name='softmax_tensor')
+
+ if mode in (Modes.TRAIN, Modes.EVAL):
+ global_step = tf.train.get_or_create_global_step()
+ label_indices = tf.cast(labels, tf.int32)
+ loss = tf.losses.softmax_cross_entropy(
+ onehot_labels=tf.one_hot(label_indices, depth=10), logits=logits)
+ tf.summary.scalar('OptimizeLoss', loss)
+
+ if mode == Modes.PREDICT:
+ predictions = {
+ 'classes': predicted_indices,
+ 'probabilities': probabilities
+ }
+ export_outputs = {
+ SIGNATURE_NAME: tf.estimator.export.PredictOutput(predictions)
+ }
+ return tf.estimator.EstimatorSpec(
+ mode, predictions=predictions, export_outputs=export_outputs)
+
+ if mode == Modes.TRAIN:
+ optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
+ train_op = optimizer.minimize(loss, global_step=global_step)
+ return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
+
+ if mode == Modes.EVAL:
+ eval_metric_ops = {
+ 'accuracy': tf.metrics.accuracy(label_indices, predicted_indices)
+ }
+ return tf.estimator.EstimatorSpec(
+ mode, loss=loss, eval_metric_ops=eval_metric_ops)
+
+
+def serving_input_fn(params):
+ inputs = {INPUT_TENSOR_NAME: tf.placeholder(tf.float32, [None, 784])}
+ return tf.estimator.export.ServingInputReceiver(inputs, inputs)
+
+
+def read_and_decode(filename_queue):
+ reader = tf.TFRecordReader()
+ _, serialized_example = reader.read(filename_queue)
+
+ features = tf.parse_single_example(
+ serialized_example,
+ features={
+ 'image_raw': tf.FixedLenFeature([], tf.string),
+ 'label': tf.FixedLenFeature([], tf.int64),
+ })
+
+ image = tf.decode_raw(features['image_raw'], tf.uint8)
+ image.set_shape([784])
+ image = tf.cast(image, tf.float32) * (1. / 255)
+ label = tf.cast(features['label'], tf.int32)
+
+ return image, label
+
+
+def train_input_fn(training_dir, params):
+ return _input_fn(training_dir, 'train.tfrecords', batch_size=100)
+
+
+def eval_input_fn(training_dir, params):
+ return _input_fn(training_dir, 'test.tfrecords', batch_size=100)
+
+
+def _input_fn(training_dir, training_filename, batch_size=100):
+ test_file = os.path.join(training_dir, training_filename)
+ filename_queue = tf.train.string_input_producer([test_file])
+
+ image, label = read_and_decode(filename_queue)
+ images, labels = tf.train.batch(
+ [image, label], batch_size=batch_size,
+ capacity=1000 + 3 * batch_size)
+
+ return {INPUT_TENSOR_NAME: images}, labels
+
+def neo_preprocess(payload, content_type):
+ import logging
+ import numpy as np
+ import PIL.Image # Training container doesn't have this package
+ import io
+
+ logging.info('Invoking user-defined pre-processing function')
+
+ if content_type != 'application/x-image':
+ raise RuntimeError('Content type must be application/x-image')
+ 
+ f = io.BytesIO(payload)
+ # Load image and convert to greyscale space
+ image = PIL.Image.open(f).convert('L')
+ # Resize
+ image = np.asarray(image.resize((28, 28)))
+ # Reshape
+ image = image.reshape((1,-1)).astype('float32')
+
+ return image
+
+### NOTE: this function cannot use MXNet
+def neo_postprocess(result):
+ import logging
+ import numpy as np
+ import json
+
+ logging.info('Invoking user-defined post-processing function')
+ 
+ # Softmax (assumes batch size 1)
+ result = np.squeeze(result)
+ result_exp = np.exp(result - np.max(result))
+ result = result_exp / np.sum(result_exp)
+
+ response_body = json.dumps(result.tolist())
+ content_type = 'application/json'
+
+ return response_body, content_type