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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:
 Copyright 2020 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1:  A simple classification model using Keras with Cloud TPUs
Overview
This notebook shows how to use Keras to build a simple classification model. The model can train, evaluate, and generate predictions using Cloud TPUs. It uses the iris dataset to predict the species of the flower and also shows how to use your own data instead of using pre-loaded data. This model uses 4 input features (SepalLength, SepalWidth, PetalLength, PetalWidth) to determine one of these flower species (Setosa, Versicolor, Virginica).
The model trains for 50 epochs and completes in approximately 2 minutes.
This notebook is hosted on GitHub. To view it in its original repository, after opening the notebook, select File > View on GitHub.
NOTE
Step2:  Resolve TPU Address
Step3:  FLAGS used as model params
Step5:  Download training input data and define prediction input & output
Step6:  Define a Keras model (2 hidden layers with 10 neurons in each)
Step7:  Compiling the model with a distribution strategy
To make the model usable by a TPU, we first must create and compile it using a distribution strategy.
Step8:  Train the model on TPU
Step9:  Evaluation of the model
Step10:  Save the model
Step11:  Prediction
Prediction data
Step12:  Prediction on TPU
Step13:  Prediction on CPU
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Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
Explanation: Copyright 2020 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
End of explanation
import json
import os
import pandas as pd
import pprint
import tensorflow as tf
import time
import numpy as np
from tensorflow import keras
print(tf.__version__)
import distutils
if distutils.version.LooseVersion(tf.__version__) < '1.14':
    raise Exception('This notebook is compatible with TensorFlow 1.14 or higher, for TensorFlow 1.13 or lower please use the previous version at https://github.com/tensorflow/tpu/blob/r1.13/tools/colab/classification_iris_data_with_keras.ipynb')
Explanation: A simple classification model using Keras with Cloud TPUs
Overview
This notebook shows how to use Keras to build a simple classification model. The model can train, evaluate, and generate predictions using Cloud TPUs. It uses the iris dataset to predict the species of the flower and also shows how to use your own data instead of using pre-loaded data. This model uses 4 input features (SepalLength, SepalWidth, PetalLength, PetalWidth) to determine one of these flower species (Setosa, Versicolor, Virginica).
The model trains for 50 epochs and completes in approximately 2 minutes.
This notebook is hosted on GitHub. To view it in its original repository, after opening the notebook, select File > View on GitHub.
NOTE: This tutorial is designed to show how to write a simple model using Keras. It should not be used for comparision with training on CPU's because of the very small amount of data being used.
Learning objectives
In this Colab, you will learn how to:
*   Define a Keras model with 2 hidden layers and 10 nodes in each layer.
*   Create and compile a Keras model on TPU with a distribution strategy.
*   Train, evaluate, and and generate predictions on Cloud TPU.
Instructions
<h3>  &nbsp;&nbsp;Train on TPU&nbsp;&nbsp; <a href="https://cloud.google.com/tpu/"><img valign="middle" src="https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/tpu-hexagon.png" width="50"></a></h3>
On the main menu, click Runtime and select Change runtime type. Set "TPU" as the hardware accelerator.
Click Runtime again and select Runtime > Run All. You can also run the cells manually with Shift-ENTER. 
Data, model, and training
Imports
End of explanation
use_tpu = True #@param {type:"boolean"}
if use_tpu:
    assert 'COLAB_TPU_ADDR' in os.environ, 'Missing TPU; did you request a TPU in Notebook Settings?'
if 'COLAB_TPU_ADDR' in os.environ:
  TF_MASTER = 'grpc://{}'.format(os.environ['COLAB_TPU_ADDR'])
else:
  TF_MASTER=''
Explanation: Resolve TPU Address
End of explanation
# Model specific parameters
# TPU address
tpu_address = TF_MASTER
# Number of epochs
epochs = 50
# Number of steps_per_epoch
steps_per_epoch = 5
# NOTE: Total number of training steps = Number of epochs * Number of steps_per_epochs
Explanation: FLAGS used as model params
End of explanation
TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
                    'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']
PREDICTION_INPUT_DATA = {
    'SepalLength': [6.9, 5.1, 5.9, 6.0, 5.5, 6.2, 5.5, 6.3],
    'SepalWidth': [3.1, 3.3, 3.0, 3.4, 2.5, 2.9, 4.2, 2.8],
    'PetalLength': [5.4, 1.7, 4.2, 4.5, 4.0, 4.3, 1.4, 5.1],
    'PetalWidth': [2.1, 0.5, 1.5, 1.6, 1.3, 1.3, 0.2, 1.5],
}
PREDICTION_OUTPUT_DATA = ['Virginica', 'Setosa', 'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor', 'Setosa', 'Virginica']
def maybe_download():
    train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
    test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
    return train_path, test_path
def load_data(y_name='Species'):
    Returns the iris dataset as (train_x, train_y), (test_x, test_y).
    train_path, test_path = maybe_download()
    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0, dtype={'SepalLength': pd.np.float32,
        'SepalWidth': pd.np.float32, 'PetalLength': pd.np.float32, 'PetalWidth': pd.np.float32, 'Species': pd.np.int32})
    train_x, train_y = train, train.pop(y_name)
    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0, dtype={'SepalLength': pd.np.float32,
        'SepalWidth': pd.np.float32, 'PetalLength': pd.np.float32, 'PetalWidth': pd.np.float32, 'Species': pd.np.int32})
    test_x, test_y = test, test.pop(y_name)
    return (train_x, train_y), (test_x, test_y)
Explanation: Download training input data and define prediction input & output
End of explanation
def get_model():
  return keras.Sequential([
    keras.layers.Dense(10, input_shape=(4,), activation=tf.nn.relu, name = "Dense_1"),
    keras.layers.Dense(10, activation=tf.nn.relu, name = "Dense_2"),
    keras.layers.Dense(3, activation=None, name = "logits"),
    keras.layers.Dense(3, activation=tf.nn.softmax, name = "softmax")
  ])
Explanation: Define a Keras model (2 hidden layers with 10 neurons in each)
End of explanation
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(TF_MASTER)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
with strategy.scope():
  model = get_model()
  model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1), 
                loss=tf.keras.losses.sparse_categorical_crossentropy, 
                metrics=['accuracy'])
model.summary()
Explanation: Compiling the model with a distribution strategy
To make the model usable by a TPU, we first must create and compile it using a distribution strategy.
End of explanation
# Fetch the data
(train_x, train_y), (test_x, test_y) = load_data()
# Train the model
model.fit(
  train_x.values, train_y.values,
  steps_per_epoch = steps_per_epoch,
  epochs=epochs,
)
Explanation: Train the model on TPU
End of explanation
model.evaluate(test_x.values, test_y.values,
    batch_size=8)
Explanation: Evaluation of the model
End of explanation
model.save_weights('./DNN_TPU_1024.h5', overwrite=True)
Explanation: Save the model
End of explanation
COLUMNS_NAME=['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth']
data = pd.DataFrame(PREDICTION_INPUT_DATA, columns=COLUMNS_NAME)
print(data)
Explanation: Prediction
Prediction data
End of explanation
predictions = model.predict(data.values.astype(np.float32))
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
for pred_dict, expec in zip(predictions, PREDICTION_OUTPUT_DATA):
  class_index = np.argmax(pred_dict)
  class_probability = np.max(pred_dict)
  print(template.format(SPECIES[class_index], 100*class_probability, expec))
Explanation: Prediction on TPU
End of explanation
cpu_model = get_model()
cpu_model.load_weights('./DNN_TPU_1024.h5')
cpu_predictions = cpu_model.predict(data)
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
for pred_dict, expec in zip(cpu_predictions, PREDICTION_OUTPUT_DATA):
  class_index = np.argmax(pred_dict)
  class_probability = np.max(pred_dict)
  print(template.format(SPECIES[class_index], 100*class_probability, expec))
Explanation: Prediction on CPU
End of explanation
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