tensorflowgcloudgoogle-cloud-mlgoogle-ai-platform

Why is the model variable not found when uploading the model to the Gcloud AI Platform?

I trained a model and uploaded it to Google AI Platform. When I test the model from the command line I expect to get predictions back from my uploaded model, instead I get an error message. Here are the steps I followed:

  1. Installing Gcloud
  2. Saving my model
gcloud ai-platform local train \
    --module-name trainer.final_task \
    --package-path trainer/ --
  1. Created manually a bucket
  2. Added created file from step 2 to bucket (saved_model.pb)
  3. Created a model in Gcloud like here and linked it with the bucket from step 5 (Yes, I configured Python and Tensorflow locally as I configured it in the bucket.).

enter image description here

  1. Tested it from a command line (this produces the error)
MODEL_NAME=ML6Mugs
VERSION=FinalModel6

gcloud ai-platform predict \
    --region europe-west1 \
    --model $MODEL_NAME \
    --version $VERSION \
    --json-instances check_deployed_model/test.json

What do I miss out? It's difficult to find something online about the issue. The only thing I found was this.

Architecture of my model

def model(input_layer):
    """Returns a compiled model.

    This function is expected to return a model to identity the different mugs.
    The model's outputs are expected to be probabilities for the classes and
    and it should be ready for training.
    The input layer specifies the shape of the images. The preprocessing
    applied to the images is specified in data.py.

    Add your solution below.

    Parameters:
        input_layer: A tf.keras.layers.InputLayer() specifying the shape of the input.
            RGB colored images, shape: (width, height, 3)
    Returns:
        model: A compiled model
    """
    input_shape=(input_layer.shape[1], input_layer.shape[2], input_layer.shape[3])
    base_model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=input_shape, include_top=False)
    for layer in base_model.layers:
        layer.trainable = False
    model = models.Sequential()
    model.add(base_model)
    model.add(layers.GlobalAveragePooling2D())
    model.add(layers.Dense(4, activation='softmax'))
    model.compile(optimizer="rmsprop", loss='sparse_categorical_crossentropy', metrics=["accuracy"])
    return model

Error

ERROR: (gcloud.ai-platform.predict) HTTP request failed. Response: {
  "error": {
    "code": 400,
    "message": "{\n    \"error\": \"Could not find variable block_15_depthwise_BN/beta. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status error message=Container localhost does not exist. (Could not find resource: localhost/block_15_depthwise_BN/beta)\\n\\t [[{{function_node __inference__wrapped_model_15632}}{{node model/sequential/mobilenetv2_1.00_224/block_15_depthwise_BN/ReadVariableOp_1}}]]\"\n}",
    "status": "INVALID_ARGUMENT"
  }
}
Solution

  • The issue is resolved. My problem was that I added the wrong path to my bucket.

    Wrong

    gs://your_bucket_name/saved_model.pb

    Correct

    gs://your_bucket_name/model-dir/