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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:
 In this exercise, you'll make your first submission to the Petals to the Metal competition.  You'll learn how to accept the competition rules, run a notebook on Kaggle that uses (free!) TPUs, and how to submit your results to the leaderboard. 
We won't cover the code in detail here, but if you'd like to dive into the details, you're encouraged to check out the tutorial notebook.
Join the competition
Begin by joining the competition. Open a new window with the competition page, and click on the "Rules" tab.
This takes you to the rules acceptance page. You must accept the competition rules in order to participate. These rules govern how many submissions you can make per day, the maximum team size, and other competition-specific details. Click on "I Understand and Accept" to indicate that you will abide by the competition rules.
Commit your Notebook
Committing your notebook will run a fresh copy of the notebook start to finish, saving a copy of the submission.csv file as output.
First, click on the Save Version button in the upper right.
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<img src="https
Step1:  Create Distribution Strategy
Step2:  Loading the Competition Data
Step3:  Explore the Data
Try using some of the helper functions described in the Getting Started tutorial to explore the dataset.
Step4:  Examine the shape of the data.
Step5:  Peek at training data.
Step6:  Define Model
Step7:  Train Model
Step8:  Examine training curves.
Step9:  Validation
Create a confusion matrix.
Step10:  Look at examples from the dataset, with true and predicted classes.
Step11:  Test Predictions
Create predictions to submit to the competition.
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Python Code:
from petal_helper import *
Explanation: In this exercise, you'll make your first submission to the Petals to the Metal competition.  You'll learn how to accept the competition rules, run a notebook on Kaggle that uses (free!) TPUs, and how to submit your results to the leaderboard. 
We won't cover the code in detail here, but if you'd like to dive into the details, you're encouraged to check out the tutorial notebook.
Join the competition
Begin by joining the competition. Open a new window with the competition page, and click on the "Rules" tab.
This takes you to the rules acceptance page. You must accept the competition rules in order to participate. These rules govern how many submissions you can make per day, the maximum team size, and other competition-specific details. Click on "I Understand and Accept" to indicate that you will abide by the competition rules.
Commit your Notebook
Committing your notebook will run a fresh copy of the notebook start to finish, saving a copy of the submission.csv file as output.
First, click on the Save Version button in the upper right.
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<img src="https://i.imgur.com/ebMUMSq.png" alt="The blue Save Version button." width=300>
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Choose Advanced Settings.
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<img src="https://i.imgur.com/sx9l1fL.png" alt="Advanced Settings in the Version menu." width=600>
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Select Run with TPU for this session from the dropdown menu and click the blue Save button.
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<img src="https://i.imgur.com/1cB5ykf.png" alt="The Accelerator dropdown menu." width=600>
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Select Save & Run All (Commit) and click the blue Save button.
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<img src="https://i.imgur.com/YeJLsNG.png" alt="The Save Version menu." width=600>
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The commit may take a while to finish (about 10-15 min), but there's no harm in doing something else while it's running and coming back later.
This generates a window in the bottom left corner of the notebook. After it has finished running, click on the number to the right of the Save Version button. This pulls up a list of versions on the right of the screen. Click on the ellipsis (...) to the right of the most recent version, and select Open in Viewer. This brings you into view mode of the same page. You will need to scroll down to get back to these instructions.
Make a Submission
Now you're ready to make a submission! Click on the Output heading in the menu to the right of the notebook.
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<img src="https://i.imgur.com/thKwt1q.png" alt="The Output heading." width=300>
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And finally you'll submit the predictions! Just look for the blue Submit button. After clicking it, you should shortly be on the leaderboard!
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<img src="https://i.imgur.com/j00mDeI.png" alt="The Save Version menu." width=600>
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Code
The code reproduces the code we covered together in the tutorial.  If you commit the notebook by following the instructions above, then the code is run for you.
Load Helper Functions
End of explanation
# Detect TPU, return appropriate distribution strategy
try:
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver() 
    print('Running on TPU ', tpu.master())
except ValueError:
    tpu = None
if tpu:
    tf.config.experimental_connect_to_cluster(tpu)
    tf.tpu.experimental.initialize_tpu_system(tpu)
    strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
    strategy = tf.distribute.get_strategy() 
print("REPLICAS: ", strategy.num_replicas_in_sync)
Explanation: Create Distribution Strategy
End of explanation
ds_train = get_training_dataset()
ds_valid = get_validation_dataset()
ds_test = get_test_dataset()
print("Training:", ds_train)
print ("Validation:", ds_valid)
print("Test:", ds_test)
Explanation: Loading the Competition Data
End of explanation
print("Number of classes: {}".format(len(CLASSES)))
print("First five classes, sorted alphabetically:")
for name in sorted(CLASSES)[:5]:
    print(name)
print ("Number of training images: {}".format(NUM_TRAINING_IMAGES))
Explanation: Explore the Data
Try using some of the helper functions described in the Getting Started tutorial to explore the dataset.
End of explanation
print("Training data shapes:")
for image, label in ds_train.take(3):
    print(image.numpy().shape, label.numpy().shape)
print("Training data label examples:", label.numpy())
print("Test data shapes:")
for image, idnum in ds_test.take(3):
    print(image.numpy().shape, idnum.numpy().shape)
print("Test data IDs:", idnum.numpy().astype('U')) # U=unicode string
Explanation: Examine the shape of the data.
End of explanation
one_batch = next(iter(ds_train.unbatch().batch(20)))
display_batch_of_images(one_batch)
Explanation: Peek at training data.
End of explanation
with strategy.scope():
    pretrained_model = tf.keras.applications.VGG16(
        weights='imagenet',
        include_top=False ,
        input_shape=[*IMAGE_SIZE, 3]
    )
    pretrained_model.trainable = False
    
    model = tf.keras.Sequential([
        # To a base pretrained on ImageNet to extract features from images...
        pretrained_model,
        # ... attach a new head to act as a classifier.
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dense(len(CLASSES), activation='softmax')
    ])
    model.compile(
        optimizer='adam',
        loss = 'sparse_categorical_crossentropy',
        metrics=['sparse_categorical_accuracy'],
    )
model.summary()
Explanation: Define Model
End of explanation
# Define the batch size. This will be 16 with TPU off and 128 with TPU on
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
# Define training epochs for committing/submitting. (TPU on)
EPOCHS = 12
STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
history = model.fit(
    ds_train,
    validation_data=ds_valid,
    epochs=EPOCHS,
    steps_per_epoch=STEPS_PER_EPOCH,
)
Explanation: Train Model
End of explanation
display_training_curves(
    history.history['loss'],
    history.history['val_loss'],
    'loss',
    211,
)
display_training_curves(
    history.history['sparse_categorical_accuracy'],
    history.history['val_sparse_categorical_accuracy'],
    'accuracy',
    212,
)
Explanation: Examine training curves.
End of explanation
cmdataset = get_validation_dataset(ordered=True)
images_ds = cmdataset.map(lambda image, label: image)
labels_ds = cmdataset.map(lambda image, label: label).unbatch()
cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy()
cm_probabilities = model.predict(images_ds)
cm_predictions = np.argmax(cm_probabilities, axis=-1)
labels = range(len(CLASSES))
cmat = confusion_matrix(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
)
cmat = (cmat.T / cmat.sum(axis=1)).T # normalize
score = f1_score(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
    average='macro',
)
precision = precision_score(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
    average='macro',
)
recall = recall_score(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
    average='macro',
)
display_confusion_matrix(cmat, score, precision, recall)
Explanation: Validation
Create a confusion matrix.
End of explanation
dataset = get_validation_dataset()
dataset = dataset.unbatch().batch(20)
batch = iter(dataset)
images, labels = next(batch)
probabilities = model.predict(images)
predictions = np.argmax(probabilities, axis=-1)
display_batch_of_images((images, labels), predictions)
Explanation: Look at examples from the dataset, with true and predicted classes.
End of explanation
test_ds = get_test_dataset(ordered=True)
print('Computing predictions...')
test_images_ds = test_ds.map(lambda image, idnum: image)
probabilities = model.predict(test_images_ds)
predictions = np.argmax(probabilities, axis=-1)
print(predictions)
print('Generating submission.csv file...')
# Get image ids from test set and convert to integers
test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U')
# Write the submission file
np.savetxt(
    'submission.csv',
    np.rec.fromarrays([test_ids, predictions]),
    fmt=['%s', '%d'],
    delimiter=',',
    header='id,label',
    comments='',
)
# Look at the first few predictions
!head submission.csv
Explanation: Test Predictions
Create predictions to submit to the competition.
End of explanation
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