Row 2188

Row ID: 2188 | Dataset Entry | Axioma AXP Content Repository

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I am deploying a MobileNetV2 model onto an Arduino using the TF Lite framework. I have used the MobileNetV2 preprocess layer in my compiled model, do I still need to rescale any input or will my model take care of it during inference?

I have also used a single dimension dense layer output as I only have 2 output classes, is there only the softmax output available from the micro_ops_resolver?

FieldValue
text I am deploying a MobileNetV2 model onto an Arduino using the TF Lite framework. I have used the MobileNetV2 preprocess layer in my compiled model, do I still need to rescale any input or will my model take care of it during inference? I have also used a single dimension dense layer output as I only have 2 output classes, is there only the softmax output available from the micro_ops_resolver?
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2023-07-12
username_encoded Z0FBQUFBQm5LakwwcU9SUE5aNHR0Qy1STWUtSjFWZXdOZTZIRjIzRVlnLTUyVGR0a3lZUzR4S0tDRDA1YnRiWFl0QU4tSjZHR2hpZHF4YmZLUUlhWVJRcDhpNXpCaEZVUEE9PQ==
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Raw Record

{
  "text": "I am deploying a MobileNetV2 model onto an Arduino using the TF Lite framework. I have used the MobileNetV2 preprocess layer in my compiled model, do I still need to rescale any input or will my model take care of it during inference? \n\nI have also used a single dimension dense layer output as I only have 2 output classes, is there only the softmax output available from the micro_ops_resolver?",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2023-07-12",
  "username_encoded": "Z0FBQUFBQm5LakwwcU9SUE5aNHR0Qy1STWUtSjFWZXdOZTZIRjIzRVlnLTUyVGR0a3lZUzR4S0tDRDA1YnRiWFl0QU4tSjZHR2hpZHF4YmZLUUlhWVJRcDhpNXpCaEZVUEE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9FRmtpa2FQVmRnc0RoSlNqTjhWU1Mxdm93SGFoZFREYmlqWmh3ZEYtdS12MXIzbXlOakEydUVFVmRRWmkyLTVfdkRxWEdnMFhtZWhxZkFJb1dlQlBJazhHMEdjWWYtYlRTLWlCR2QtRS1pbjhrbUo1WEtOTk1aVEp3R3lveldhLUlwMmhiZ1lwWW5ub3JoeV85NUpHUkJNTl9jYW5KdTM5ZHZFM3RjS2h4STNQUlFYNWxDMzRzM1o2UU9GaVJqdHJr"
}
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