Colab problem with metadata

[batrlatom]

Hello, I am having a problem to run a training step in the colab and the same problem is even when I run your code localy with google cloud sdk authenticated. I am not using hyperdash token.

Do you know what could be a problem?

2019-07-13 09:30:43.978054:
E tensorflow/core/platform/cloud/curl_http_request.cc:596] The transmission of request 0x564e8ccb2900 (URI: http://metadata/computeMetadata/v1/instance/service-accounts/default/token) has been stuck at 0 of 0 bytes for 61 seconds and will be aborted. CURL timing information: lookup time: 0.006321 (No error), connect time: 0 (No error), pre-transfer time: 0 (No error), start-transfer time: 0 (No error)

2019-07-13 09:30:43.978267:
I tensorflow/core/platform/cloud/retrying_utils.cc:77] The operation failed and will be automatically retried in 1.23757 seconds (attempt 1 out of 10), caused by: Unavailable: Error executing an HTTP request: libcurl code 42 meaning 'Operation was aborted by an application callback', error details: Callback aborted

[samehraban]

Any updates here?

[batrlatom]

I just gave up and run it on local system. I ommited the whole google sdk cloud thing. But the mildnet itself is nice and works quite well. Noting that there are some bugs in the code .. I think that right code for mildnet_mobilenet is :

def mildnet_mobilenet():
    vgg_model = MobileNet(weights=None, include_top=False, input_shape=(224,224,3))
    intermediate_layer_outputs = get_layers_output_by_name(vgg_model, ["conv_dw_1_relu", "conv_dw_2_relu", "conv_dw_4_relu", "conv_dw_6_relu", "conv_dw_12_relu"])
    convnet_output = GlobalAveragePooling2D()(vgg_model.output)
    for layer_name, output in intermediate_layer_outputs.items():
      output = GlobalAveragePooling2D()(output)
      convnet_output = concatenate([convnet_output, output])
    convnet_output = Dense(1024, activation='relu')(convnet_output)
    convnet_output = Dropout(0.5)(convnet_output)
    convnet_output = Dense(1024, activation='relu')(convnet_output)
    convnet_output = Lambda(lambda  x: K.l2_normalize(x,axis=1))(convnet_output)
      
    first_input = Input(shape=(224,224,3))
    second_input = Input(shape=(224,224,3))

    final_model = tf.keras.models.Model(inputs=[first_input, second_input, vgg_model.input], outputs=convnet_output)

    return final_model

[samehraban]

I'm working on the code -especially datagen module- making it compatible with latest version of tensorflow.

One thing that occurred to me was 3 different inputs. Can you confirm that these three inputs are actually needed in model? I couldn't find the code using these different inputs.

[batrlatom]

Would you be able to share your latest tf compatible version with us? Btw, I am using this repo and it is nice source of inspiration - https://github.com/omoindrot/tensorflow-triplet-loss . And with the model, I also did not find any code which use them. btw ... did you try to learn tripletloss on cropped data? Do you have any experience with it?

[samehraban]

Well you can see what I've done in my fork. Still needs pretty much work I think but training the mildnet in google colab is now possible.

[batrlatom]

I tried your code, looks nice. But still getting me error out of blue. Could you please take a look?

/usr/local/lib/python2.7/dist-packages/tensorflow/python/keras/engine/training_utils.pyc in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    383                              ': expected ' + names[i] + ' to have shape ' +
    384                              str(shape) + ' but got array with shape ' +
--> 385                              str(data_shape))
    386   return data
    387 

ValueError: Error when checking input: expected input_1 to have shape (224, 224, 3) but got array with shape (224, 3, 3)

[StevenShadman]

looks like the line:
img_width, img_height = [int(v) for v in model.input[0].shape[1:3]]
should be
img_width, img_height = [int(v) for v in model.input[0].shape[:2]]