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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 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
Step1:  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 <a href="mnistmodel/trainer">mnistmodel/trainer</a>
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.
Step2:  Now, let's do it on Cloud ML Engine so we can train on GPU
Step3:  Monitoring training with TensorBoard
Use this cell to launch tensorboard
Step4:  Here are my results
Step5:  To predict with the model, let's take one of the example images.
Step6:  Send it to the prediction service
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Python Code:
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
%%bash
gcloud config set project $PROJECT
gcloud config set compute/region $REGION
Explanation: MNIST Image Classification with TensorFlow on Cloud ML Engine
This notebook demonstrates how to implement different image models on MNIST using Estimator. 
Note the MODEL_TYPE; change it to try out different models
End of explanation
%%bash
rm -rf mnistmodel.tar.gz mnist_trained
gcloud ml-engine local train \
    --module-name=trainer.task \
    --package-path=${PWD}/mnistmodel/trainer \
    -- \
    --output_dir=${PWD}/mnist_trained \
    --train_steps=100 \
    --learning_rate=0.01 \
    --model=$MODEL_TYPE
Explanation: 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 <a href="mnistmodel/trainer">mnistmodel/trainer</a>
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.
End of explanation
%%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
Explanation: 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.
End of explanation
from google.datalab.ml import TensorBoard
TensorBoard().start("gs://{}/mnist/trained_{}".format(BUCKET, MODEL_TYPE))
for pid in TensorBoard.list()["pid"]:
    TensorBoard().stop(pid)
    print("Stopped TensorBoard with pid {}".format(pid))
Explanation: Monitoring training with TensorBoard
Use this cell to launch tensorboard
End of explanation
%%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
Explanation: 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:
End of explanation
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));
Explanation: To predict with the model, let's take one of the example images.
End of explanation
%%bash
gcloud ml-engine predict \
   --model=mnist \
   --version=${MODEL_TYPE} \
   --json-instances=./test.json
Explanation: Send it to the prediction service
End of explanation
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