Explore ML Models with Explainable AI

 

Explore Machine Learning Models with Explainable AI: Challenge Lab

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)

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)

# 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'

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

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

!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

Create your second AI Platform model: limited_model

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

!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

WATCH VIDEO TO UNDERSTAND BETTER : https://youtu.be/UEYzVKqTKGE