Explore Machine Learning Models with Explainable AI

step-by-step instructions, you will use the skills learned from the labs in the quest to figure out how to complete the tasks on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.

Challenge Lab

Qwiklabs [GSP324]

@prakashfoundation YouTube

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Task 1:

TODO (1):

# ---- TODO --------- 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)

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TODO (2):

# ---- TODO --------- # create the limited_model = Sequential() # limited_model.add (your layers) # limited_model.compile # limited_model.fit 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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Fill out this information:

GCP_PROJECT = '# TODO' MODEL_BUCKET = 'gs:// #TODO' MODEL_NAME = 'complete_model' #do not modify LIM_MODEL_NAME = 'limited_model' #do not modify VERSION_NAME = 'v1' REGION = 'us-central1'

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TODO:

# ---- TODO --------- !gcloud ai-platform models create $MODEL_NAME --regions $REGION

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Now create a version. This will take a couple of minutes to deploy..

TODO:

# ---- TODO ------ !gcloud ai-platform versions create $VERSION_NAME \ --model=$MODEL_NAME \ --framework='TENSORFLOW' \ --runtime-version=2.1 \ --origin=$MODEL_BUCKET/saved_model/my_model \ --staging-bucket=$MODEL_BUCKET \ --python-version=3.7

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Create your second AI Platform model: limited_model

TODO:

# ---- TODO --------- !gcloud ai-platform models create $LIM_MODEL_NAME --regions $REGION

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TODO :

# ---- TODO ------ !gcloud ai-platform versions create $VERSION_NAME \ --model=$LIM_MODEL_NAME \ --framework='TENSORFLOW' \ --runtime-version=2.1 \ --origin=$MODEL_BUCKET/saved_limited_model/my_limited_model \ --staging-bucket=$MODEL_BUCKET \ --python-version=3.7

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