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Could you explain me the output of keras at each iteration?


Could you explain me the output of keras at each iteration?

By : Joey Lozano
Date : November 20 2020, 04:01 AM
this one helps. The loss that Keras calculates during the epoch is accumulated and estimated online. So it includes the loss from the model after different weight updates.
Let we clarify with an easy case: assume for a second that the model is only improving (every weight update results in better accuracy and loss), and that each epoch contains 2 weight updates (each min-batch is half the training dataset).
code :


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What does the standard Keras model output mean? What is epoch and loss in Keras?

What does the standard Keras model output mean? What is epoch and loss in Keras?


By : Ingen
Date : March 29 2020, 07:55 AM
wish help you to fix your issue Just to answer the questions more specifically, here's a definition of epoch and loss:
Epoch: A full pass over all of your training data.
code :
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(data, labels, validation_split=0.2)
What does the acc means in the Keras model.fit output? the accuracy of the final iteration in a epoch or the average acc

What does the acc means in the Keras model.fit output? the accuracy of the final iteration in a epoch or the average acc


By : karpaga jothi41
Date : March 29 2020, 07:55 AM
it helps some times According to callback and history documentation;
acc represents the average training accuracy at the end of an epoch.
Training in SSD Implementation in Keras halts after few iteration without any output or error

Training in SSD Implementation in Keras halts after few iteration without any output or error


By : Nieysa
Date : March 29 2020, 07:55 AM
help you fix your problem You don't have enough memory, things you can do to solve the problem:
reduce the batch size reduce the size of the train data train your models in clouds (AMS, Google cloud and etc) use another GPU card with more memory or try CPU
keras predict_generator is shuffling its output when using a keras.utils.Sequence

keras predict_generator is shuffling its output when using a keras.utils.Sequence


By : Ploink
Date : March 29 2020, 07:55 AM
may help you . predict_generator was not shuffling my predictions, after all. The problem was with the __getitem__ method. For instance, usingn_batch=32, the method would yield values from 1 to 32, then from 2 to 33 and so forth, instead of from 1 to 32, 33 to 64, etc.
Changing the method as follows solves the problem
code :
 def __getitem__(self, idx):
    # batch_x is a numpy.ndarray
    idx_min = idx*self.batch_size
    idx_max = min(idx_min + self.batch_size, self.n)
    batch_x = (
            self.images[idx_min:idx_max]
            .concatenate()
            .reshape(self.batch_size, 720, 1280, 1)
            ) 
    batch_y = self.hf[idx_min:idx_max]
Keras Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata)

Keras Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata)


By : user2525215
Date : March 29 2020, 07:55 AM
This might help you When invoking the Model API, the value for outputs argument should be tensor(or list of tensors), in this case it is a list of list of tensors, hence there is a problem. Just unpack the unpooling_masks list(*unpooling_masks) when calling Model.
code :
model = Model(inputs, [layer, *unpooling_masks], name='vgg19')
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