Explore Machine Learning Models with Explainable AI: Challenge Lab [GSP324]

Link to the Lab: https://www.cloudskillsboost.google/focuses/12011?parent=catalog

Link to the video: https://youtu.be/gIJmNrIwR4k

Link to the channel: https://www.youtube.com/channel/UCiWP5KYh4MWj-yp3j-DMIrA

STEPS

OPEN "Notebooks" (AI Platform)

New instance

Select "Tensorflow enterprise 2.3" > without GPUs

Region: us-central1 (lowa)

Zone: us-central1-a

create

open jupyterlab

open terminal

git clone https://github.com/GoogleCloudPlatform/training-data-analyst

Go to the enclosing folder: training-data-analyst/quests/dei.

Open the notebook file what-if-tool-challenge.ipynb.

Download and import the dataset hmda_2017_ny_all-records_labels.

*to run the cells either you can press run button or press shift+enter in each cell*

[*] - this means the cell is in running state

#this is the size of the array you'll be feeding into our model for each example

model = Sequential()

model.add(layers.Dense(200, input_shape=(input_size,), activation='relu'))

model.add(layers.Dense(50, activation='relu'))

model.add(layers.Dense(20, activation='relu'))

model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

model.fit(train_data, train_labels, epochs=10, batch_size=2048, validation_split=0.1)

#Train your second model on the Limited dataset. Use `Limited_train_data` fir your data and `Limited_train_labels`

limited_model = Sequential()

limited_model.add(layers.Dense(200, input_shape=(input_size,), activation='relu'))

limited_model.add(layers.Dense(50, activation='relu'))

limited_model.add(layers.Dense(20, activation='relu'))

limited_model.add(layers.Dense(1, activation='sigmoid'))

limited_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

limited_model.fit(limited_train_data, limited_train_labels, epochs=10, batch_size=2048, validation_split=0.1)

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Go to cloud storage create bucket and name it by your gcp project id

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#Fill out this information

GCP_PROJECT = <PUT_PROJECT_ID>

MODEL_BUCKET = 'gs://<PUT_PROJECT_ID>'

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Create the Complete AI Platform model

Use the following to create your first AI Platform model:

Model Name = complete_model

Version Name = v1

Python version = 3.7

Framework = TensorFlow

Framework version = 2.3.1

ML Runtime version = 2.3

Create the Limited AI Platform model

Use the following to create your second AI Platform model:

Model Name = limited_model

Version Name = v1

Python version = 3.7

Framework = TensorFlow

Framework version = 2.3.1

ML Runtime version = 2.3

Congratulations you have completed the lab!

SAKURA SATIO

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