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Error indicates flattened dimensions when loading pre-trained network


Error indicates flattened dimensions when loading pre-trained network

By : ionut carp
Date : November 22 2020, 07:01 PM
I wish this help you It seems like you are taking a pretrained net where layer "fc4" was a fully connected layer (aka type: "InnerProduct" layer) and it was "reshaped" into a convolutional layer.
Since both inner product layer and convolutional layer performs roughly the same linear operation on the inputs this change can be made under certain assumptions (see, e.g. here).
code :
layer {
  name: "fc4"
  type: "Convolution"
  bottom: "conv3"
  top: "fc4"
  convolution_param {
    num_output: 512
    pad: 0
    kernel_size: 4
  }
  param {
    lr_mult: 1
    decay_mult: 1
    share_mode: PERMISSIVE  # should help caffe overcome the shape mismatch
  }
  param {
    lr_mult: 2
    decay_mult: 0
    share_mode: PERMISSIVE
  }
}


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Error function in Artificial Neural Network trained using backpropogation

Error function in Artificial Neural Network trained using backpropogation


By : zl535320706
Date : March 29 2020, 07:55 AM
hop of those help? Your answer
The error function is the function which you try to minimize. What you have listed above is a set of error functions, and the derivatives of some of them. It might be a bit confusing when litterature uses the same term when the minimizing function has been derivated. Just remember that we wish to minimize the error in our network, and the functions which helps us achieve it is the error function.
How can I reduce the error in my trained values while implementing Artificial Neural Network?

How can I reduce the error in my trained values while implementing Artificial Neural Network?


By : sylvain gagnon
Date : March 29 2020, 07:55 AM
This might help you An artificial neural network accepts a set of hyperparameters that decides the accuracy of classification of your test dataset given that your neural network has been trained on a training dataset.
These hyperparameters are:
TFLearn throws error while loading trained model

TFLearn throws error while loading trained model


By : Daniel Eduardo Alzat
Date : March 29 2020, 07:55 AM
I hope this helps . I'm not sure what your problem is, but model.load() doesn't return anything. Here's example usage from tflearn:
code :
model = DNN(network)
model.load('model.tflearn')
model.predict(X)
loading and using a pre-trained neural network from any platform

loading and using a pre-trained neural network from any platform


By : user1899784
Date : March 29 2020, 07:55 AM
wish helps you I don't think it is possible to write a program that can load pre-trained models from both Torch and Tensorflow as they save in different formats.
You might want to look into the Open Neural Network Exchange Format (https://onnx.ai/) if you are creating the models yourself, this is an initiative backed by Amazon, Facebook, Microsoft, and others to create a portable file format for deep learning models.
Tensorflow: Error while loading pre-trained ResNet model

Tensorflow: Error while loading pre-trained ResNet model


By : nizadox
Date : March 29 2020, 07:55 AM
this will help Maybe you could use ResNet50 from tf.keras.applications?
According to the error, if you haven't altered the graph in any way, and this is your whole source code, it might be really, really hard to debug.
code :
import tensorflow

in_width, in_height, in_channels = 224, 224, 3

pretrained_resnet = tensorflow.keras.applications.ResNet50(
    weights="imagenet",
    include_top=False,
    input_shape=(in_width, in_height, in_channels),
)

# You can freeze some layers if you want, depends on your task
# Make "top" (last 3 layers below) whatever fits your task as well

model = tensorflow.keras.models.Sequential(
    [
        pretrained_resnet,
        tensorflow.keras.layers.Flatten(),
        tensorflow.keras.layers.Dense(1024, activation="relu"),
        tensorflow.keras.layers.Dense(10, activation="softmax"),
    ]
)

print(model.summary())
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