MNIST Image Classification with TensorFlow on Cloud ML Engine

This notebook demonstrates how to implement different image models on MNIST using Estimator.

Note the MODEL_TYPE; change it to try out different models


In [ ]:
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE = "linear"  # "linear", "dnn", "dnn_dropout", or "cnn"

# do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
os.environ["REGION"] = REGION
os.environ["MODEL_TYPE"] = MODEL_TYPE
os.environ["TFVERSION"] = "1.13"  # Tensorflow version

In [ ]:
%%bash
gcloud config set project $PROJECT
gcloud config set compute/region $REGION

Run as a Python module

In the previous notebook (mnist_linear.ipynb) we ran our code directly from the notebook.

Now since we want to run our code on Cloud ML Engine, we've packaged it as a python module.

The model.py and task.py containing the model code is in mnistmodel/trainer

Complete the TODOs in model.py before proceeding!

Once you've completed the TODOs, set MODEL_TYPE and run it locally for a few steps to test the code.


In [ ]:
%%bash
rm -rf mnistmodel.tar.gz mnist_trained
gcloud ml-engine local train \
    --module-name=trainer.task \
    --package-path=${PWD}/mnistmodel/trainer \
    -- \
    --output_dir=${PWD}/mnist_trained \
    --train_steps=100 \
    --learning_rate=0.01 \
    --model=$MODEL_TYPE

Now, let's do it on Cloud ML Engine so we can train on GPU: --scale-tier=BASIC_GPU

Note the GPU speed up depends on the model type. You'll notice the more complex CNN model trains significantly faster on GPU, however the speed up on the simpler models is not as pronounced.


In [ ]:
%%bash
OUTDIR=gs://${BUCKET}/mnist/trained_${MODEL_TYPE}
JOBNAME=mnist_${MODEL_TYPE}_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR
gcloud ml-engine jobs submit training $JOBNAME \
    --region=$REGION \
    --module-name=trainer.task \
    --package-path=${PWD}/mnistmodel/trainer \
    --job-dir=$OUTDIR \
    --staging-bucket=gs://$BUCKET \
    --scale-tier=BASIC_GPU \
    --runtime-version=$TFVERSION \
    -- \
    --output_dir=$OUTDIR \
    --train_steps=10000 --learning_rate=0.01 --train_batch_size=512 \
    --model=$MODEL_TYPE --batch_norm

Monitoring training with TensorBoard

Use this cell to launch tensorboard


In [ ]:
from google.datalab.ml import TensorBoard
TensorBoard().start("gs://{}/mnist/trained_{}".format(BUCKET, MODEL_TYPE))

In [ ]:
for pid in TensorBoard.list()["pid"]:
    TensorBoard().stop(pid)
    print("Stopped TensorBoard with pid {}".format(pid))

Here are my results:

Model Accuracy Time taken Model description Run time parameters
linear 91.53 3 min linear 100 steps, LR=0.01, Batch=512
linear 92.73 8 min linear 1000 steps, LR=0.01, Batch=512
linear 92.29 18 min linear 10000 steps, LR=0.01, Batch=512
dnn 98.14 15 min 300-100-30 nodes fully connected 10000 steps, LR=0.01, Batch=512
dnn 97.99 48 min 300-100-30 nodes fully connected 100000 steps, LR=0.01, Batch=512
dnn_dropout 97.84 29 min 300-100-30-DL(0.1)- nodes 20000 steps, LR=0.01, Batch=512
cnn 98.97 35 min maxpool(10 5x5 cnn, 2)-maxpool(20 5x5 cnn, 2)-300-DL(0.25) 20000 steps, LR=0.01, Batch=512
cnn 98.93 35 min maxpool(10 11x11 cnn, 2)-maxpool(20 3x3 cnn, 2)-300-DL(0.25) 20000 steps, LR=0.01, Batch=512
cnn 99.17 35 min maxpool(10 11x11 cnn, 2)-maxpool(20 3x3 cnn, 2)-300-DL(0.25), batch_norm (logits only) 20000 steps, LR=0.01, Batch=512
cnn 99.27 35 min maxpool(10 11x11 cnn, 2)-maxpool(20 3x3 cnn, 2)-300-DL(0.25), batch_norm (logits, deep) 10000 steps, LR=0.01, Batch=512
cnn 99.48 12 hr as-above but nfil1=20, nfil2=27, dprob=0.1, lr=0.001, batchsize=233 (hyperparameter optimization)

Create a table to keep track of your own results as you experiment with model type and hyperparameters!

Deploying and predicting with model

Deploy the model:


In [ ]:
%%bash
MODEL_NAME="mnist"
MODEL_VERSION=${MODEL_TYPE}
MODEL_LOCATION=$(gsutil ls gs://${BUCKET}/mnist/trained_${MODEL_TYPE}/export/exporter | tail -1)
echo "Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes"
#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}
#gcloud ml-engine models delete ${MODEL_NAME}
gcloud ml-engine models create ${MODEL_NAME} --regions $REGION
gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version=$TFVERSION

To predict with the model, let's take one of the example images.


In [ ]:
import json, codecs
import matplotlib.pyplot as plt
import tensorflow as tf

HEIGHT = 28
WIDTH = 28

# Get mnist data
mnist = tf.keras.datasets.mnist

(_, _), (x_test, _) = mnist.load_data()

# Scale our features between 0 and 1
x_test = x_test / 255.0 

IMGNO = 5 # CHANGE THIS to get different images
jsondata = {"image": x_test[IMGNO].reshape(HEIGHT, WIDTH).tolist()}
json.dump(jsondata, codecs.open("test.json", 'w', encoding = "utf-8"))
plt.imshow(x_test[IMGNO].reshape(HEIGHT, WIDTH));

Send it to the prediction service


In [ ]:
%%bash
gcloud ml-engine predict \
   --model=mnist \
   --version=${MODEL_TYPE} \
   --json-instances=./test.json
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