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?
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
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 pretrained neural network from any platform
By : user1899784
Date : March 29 2020, 07:55 AM

Tensorflow: Error while loading pretrained 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())

