AWS Code Sample
Catalog demonstrates how to create an inference Lambda function on an AWS DeepLens model.

# Copyright 2010-2019, Inc. or its affiliates. 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. A copy of # the License is located at # # # # or in the "license" file accompanying this file. This file 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. import mxnet as mx from mxnet.gluon.model_zoo import vision squeezenet = vision.squeezenet_v1(pretrained=True, ctx=mx.cpu()) # To export, you need to hybridize your gluon model, squeezenet.hybridize() # SqueezeNet’s input pattern is 224 pixel X 224 pixel images. Prepare a fake image, fake_image = mx.nd.random.uniform(shape=(1,3,224,224), ctx=mx.cpu()) # Run the model once. result = squeezenet(fake_image) # Now you can export the model. You can use a path if you want ‘models/squeezenet’. squeezenet.export(‘squeezenet')

Sample Details

Service: deeplens

Last tested: 2019-01-07

Author: AWS

Type: full-example

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