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Merged
merged 4 commits into from
Oct 25, 2018
Merged

Update TF version to 1.11 #442

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5878bd1
Update TF version to 1.11
mvsusp Oct 22, 2018
8549867
Fixed Keras and Batch Transform notebooks
mvsusp Oct 25, 2018
cd32582
Fixed Keras Cifar model
mvsusp Oct 25, 2018
ff5be43
Updated TF Pipe Mode Version
mvsusp Oct 25, 2018
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2 changes: 1 addition & 1 deletion hyperparameter_tuning/tensorflow_mnist/hpo_tensorflow_mnist.ipynb
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Expand Up @@ -176,7 +176,7 @@
"source": [
"estimator = TensorFlow(entry_point='mnist.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" training_steps=1000, \n",
" evaluation_steps=100,\n",
" train_instance_count=1,\n",
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2 changes: 1 addition & 1 deletion ...flow_abalone_age_predictor_using_keras/tensorflow_abalone_age_predictor_using_keras.ipynb
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Expand Up @@ -461,7 +461,7 @@
"\n",
"abalone_estimator = TensorFlow(entry_point='abalone.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" training_steps= 100, \n",
" evaluation_steps= 100,\n",
" hyperparameters={'learning_rate': 0.001},\n",
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2 changes: 1 addition & 1 deletion ...ow_abalone_age_predictor_using_layers/tensorflow_abalone_age_predictor_using_layers.ipynb
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Expand Up @@ -506,7 +506,7 @@
"\n",
"abalone_estimator = TensorFlow(entry_point='abalone.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" training_steps= 100, \n",
" evaluation_steps= 100,\n",
" hyperparameters={'learning_rate': 0.001},\n",
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6 changes: 3 additions & 3 deletions sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_batch_transform_mnist.ipynb
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Expand Up @@ -117,7 +117,7 @@
"\n",
"mnist_estimator = TensorFlow(entry_point='mnist.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" training_steps=1000, \n",
" evaluation_steps=100,\n",
" train_instance_count=2,\n",
Expand Down Expand Up @@ -233,11 +233,11 @@
"outputs": [],
"source": [
"import json\n",
"from urllib.parse import urlparse\n",
"from six.moves.urllib import parse\n",
"\n",
"import boto3\n",
"\n",
"parsed_url = urlparse(transformer.output_path)\n",
"parsed_url = parse.urlparse(transformer.output_path)\n",
"bucket_name = parsed_url.netloc\n",
"prefix = parsed_url.path[1:]\n",
"\n",
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2 changes: 1 addition & 1 deletion sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb
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Expand Up @@ -149,7 +149,7 @@
"\n",
"mnist_estimator = TensorFlow(entry_point='mnist.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" training_steps=1000, \n",
" evaluation_steps=100,\n",
" train_instance_count=2,\n",
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2 changes: 1 addition & 1 deletion sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_local_mode_mnist.ipynb
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Expand Up @@ -165,7 +165,7 @@
"\n",
"mnist_estimator = TensorFlow(entry_point='mnist.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" training_steps=10, \n",
" evaluation_steps=10,\n",
" train_instance_count=2,\n",
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2 changes: 1 addition & 1 deletion ...ris_dnn_classifier_using_estimators/tensorflow_iris_dnn_classifier_using_estimators.ipynb
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Expand Up @@ -305,7 +305,7 @@
"\n",
"iris_estimator = TensorFlow(entry_point='iris_dnn_classifier.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" output_path=model_artifacts_location,\n",
" code_location=custom_code_upload_location,\n",
" train_instance_count=1,\n",
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20 changes: 8 additions & 12 deletions sagemaker-python-sdk/tensorflow_keras_cifar10/cifar10_cnn.py
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Expand Up @@ -18,10 +18,10 @@
import os

import tensorflow as tf
from tensorflow.python.keras.layers import InputLayer, Conv2D, Activation, MaxPooling2D, Dropout, Flatten, Dense
from tensorflow.python.keras.layers import Activation, Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.optimizers import RMSprop
from tensorflow.python.saved_model.signature_constants import PREDICT_INPUTS
from tensorflow.python.training.rmsprop import RMSPropOptimizer

HEIGHT = 32
WIDTH = 32
Expand All @@ -30,6 +30,7 @@
NUM_DATA_BATCHES = 5
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 10000 * NUM_DATA_BATCHES
BATCH_SIZE = 128
INPUT_TENSOR_NAME = 'inputs_input' # needs to match the name of the first layer + "_input"


def keras_model_fn(hyperparameters):
Expand All @@ -44,10 +45,7 @@ def keras_model_fn(hyperparameters):
"""
model = Sequential()

# TensorFlow Serving default prediction input tensor name is PREDICT_INPUTS.
# We must conform to this naming scheme.
model.add(InputLayer(input_shape=(HEIGHT, WIDTH, DEPTH), name=PREDICT_INPUTS))
model.add(Conv2D(32, (3, 3), padding='same'))
model.add(Conv2D(32, (3, 3), padding='same', name='inputs', input_shape=(HEIGHT, WIDTH, DEPTH)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
Expand All @@ -68,15 +66,13 @@ def keras_model_fn(hyperparameters):
model.add(Dense(NUM_CLASSES))
model.add(Activation('softmax'))

_model = tf.keras.Model(inputs=model.input, outputs=model.output)
opt = RMSPropOptimizer(learning_rate=hyperparameters['learning_rate'], decay=hyperparameters['decay'])

opt = RMSprop(lr=hyperparameters['learning_rate'], decay=hyperparameters['decay'])

_model.compile(loss='categorical_crossentropy',
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])

return _model
return model


def serving_input_fn(hyperparameters):
Expand Down Expand Up @@ -147,7 +143,7 @@ def _input(mode, batch_size, data_dir):
images, labels = iterator.get_next()

# We must use the default input tensor name PREDICT_INPUTS
return {PREDICT_INPUTS: images}, labels
return {INPUT_TENSOR_NAME: images}, labels


def _train_preprocess_fn(image, label):
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22 changes: 14 additions & 8 deletions sagemaker-python-sdk/tensorflow_keras_cifar10/tensorflow_keras_CIFAR10.ipynb
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Expand Up @@ -123,10 +123,18 @@
"outputs": [],
"source": [
"def keras_model_fn(hyperparameters):\n",
" \"\"\"keras_model_fn receives hyperparameters from the training job and returns a compiled keras model.\n",
" The model will be transformed into a TensorFlow Estimator before training and it will be saved in a \n",
" TensorFlow Serving SavedModel at the end of training.\n",
"\n",
" Args:\n",
" hyperparameters: The hyperparameters passed to the SageMaker TrainingJob that runs your TensorFlow \n",
" training script.\n",
" Returns: A compiled Keras model\n",
" \"\"\"\n",
" model = Sequential()\n",
"\n",
" model.add(InputLayer(input_shape=(HEIGHT, WIDTH, DEPTH), name=PREDICT_INPUTS))\n",
" model.add(Conv2D(32, (3, 3), padding='same'))\n",
" model.add(Conv2D(32, (3, 3), padding='same', name='inputs', input_shape=(HEIGHT, WIDTH, DEPTH)))\n",
" model.add(Activation('relu'))\n",
" model.add(Conv2D(32, (3, 3)))\n",
" model.add(Activation('relu'))\n",
Expand All @@ -147,15 +155,13 @@
" model.add(Dense(NUM_CLASSES))\n",
" model.add(Activation('softmax'))\n",
" \n",
" _model = tf.keras.Model(inputs=model.input, outputs=model.output)\n",
"\n",
" opt = RMSprop(lr=hyperparameters['learning_rate'], decay=hyperparameters['decay'])\n",
" opt = RMSPropOptimizer(learning_rate=hyperparameters['learning_rate'], decay=hyperparameters['decay'])\n",
"\n",
" _model.compile(loss='categorical_crossentropy',\n",
" model.compile(loss='categorical_crossentropy',\n",
" optimizer=opt,\n",
" metrics=['accuracy'])\n",
"\n",
" return _model"
" return model"
]
},
{
Expand Down Expand Up @@ -216,7 +222,7 @@
"\n",
"estimator = TensorFlow(entry_point='cifar10_cnn.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" hyperparameters={'learning_rate': 1e-4, 'decay':1e-6},\n",
" training_steps=1000, evaluation_steps=100,\n",
" train_instance_count=1, train_instance_type='ml.c4.xlarge')\n",
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2 changes: 1 addition & 1 deletion sagemaker-python-sdk/tensorflow_pipemode_example/tensorflow_pipemode_example.ipynb
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Expand Up @@ -139,7 +139,7 @@
"\n",
"tensorflow = TensorFlow(entry_point='pipemode.py',\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" input_mode='Pipe',\n",
" output_path=model_artifacts_location,\n",
" code_location=custom_code_upload_location,\n",
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2 changes: 1 addition & 1 deletion ...nsorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb
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Expand Up @@ -118,7 +118,7 @@
"estimator = TensorFlow(entry_point='resnet_cifar_10.py',\n",
" source_dir=source_dir,\n",
" role=role,\n",
" framework_version='1.10.0',\n",
" framework_version='1.11.0',\n",
" hyperparameters={'throttle_secs': 30},\n",
" training_steps=1000, evaluation_steps=100,\n",
" train_instance_count=2, train_instance_type='ml.c4.xlarge', \n",
Expand Down