livelossplot | Live training loss plot in Jupyter Notebook | Machine Learning library
kandi X-RAY | livelossplot Summary
kandi X-RAY | livelossplot Summary
Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting? Visual feedback allows us to keep track of the training process. Now there is one for Jupyter. If you want to get serious - use TensorBoard, . But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using livelossplot as a plug&play component.
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Top functions reviewed by kandi - BETA
- Initialize training .
- Draw a metric subplot .
- Create a list of log messages .
- Store metrics .
- Runs the main plot .
- Find spec by fullname .
- Create the grid plot
- Add an ExtremaPrinter to the plot .
- Creates a PlotLossCallback that returns a PlotLossCallback .
- Decorator to create PlotLosses .
livelossplot Key Features
livelossplot Examples and Code Snippets
Lines of Code : 3License : Permissive (MIT)
Copyfrom kerasplotlib import TrainingLog
%matplotlib inline
callbacks=[TrainingLog()]
- pip:
- backtrader==1.9.76.123
- livelossplot==0.5.4
- pyportfolioopt==1.4.1
- qdldl==0.1.5.post0
- osqp==0.6.2.post0
- pymdptoolbox==4.0b3
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer=opt, loss='mse', metrics=['accuracy'])
[1,0,1]
[[0,1], [from livelossplot.tf_keras import PlotLossesCallback
from livelossplot.inputs.tf_keras import PlotLossesCallback
from keras.models import load_model
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
del model # deletes the existing model
# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')
from livelossplot import PlotLossesKeras
model.fit(X_train, Y_train,
epochs=10,
validation_data=(X_test, Y_test),
callbacks=[PlotLossesKeras()],
verbose=0)
Community Discussions
Trending Discussions on livelossplot
QUESTION
I have trained a tensorflow.keras model over the night and was suprised about the training process (please see the attached picture). Can anyone tell me, what can produce such an effect during training? I have trained with mse (right) and one other loss displayed (binary crossentropy). I have trained an autoencoder with 'normal' samples. The validation samples are 'anomaly' samples.
If you need more information, please let me know.
Edit: I might found the reason, but I am not sure: I have features as input-data, which do not have values strictly in [0,1], actually I have nearly all values in [0,1] but a few values a little bit bigger than 1. As I am training with the MSE, I thought this should not be a problem, but as reference I also use the binary crossentropy loss (needs values in [0,1]). This might cause some iritation to the training. I am using:
...ANSWER
Answered 2021-Sep-26 at 10:49I found a solution:
-I reinstalled python completely and changed the learning rate to a smaller value (I think the learning rate was the main factor) and since then, no loss explosition occured anymore (trained now several times).
QUESTION
I am training a neural network with a simple dataset. I have tried different combinations of parameters, optimizers, learning rates ... but even after 20 epochs the network is still not learning anything.
I wonder where in the following code lies the problem?
...ANSWER
Answered 2020-Nov-01 at 20:28You have used wrong loss function, change this line
QUESTION
I was trying out this course in Coursera when I stumbled upon this problem. Whenever I try to run the model.fit() it shows this error.
Error shown: ...ANSWER
Answered 2020-Jul-07 at 18:10livelossplot.tf_keras will not work in Tensorflow version 2.1+, Downgrade your TensorFlow version from 2.2 to Tensorflow 2.1 using pip install tensorflow==2.1, it will work and plot your model training plots.
QUESTION
callbacks gives KeyError: 'metrics' while training in Colab
DATASET: SETI
...ANSWER
Answered 2020-Jul-02 at 12:32Your import is using the older API, there have been some API changes in newer versions
Just change your import statement
from livelossplot.tf_keras import PlotLossesCallback
to
from livelossplot.inputs.tf_keras import PlotLossesCallback
Checkout the livelossplot github for more info and examples: livelossplot-github
QUESTION
I borrowed code from this github repo for training of a DenseNet-121 [https://github.com/gaetandi/cheXpert/blob/master/cheXpert_final.ipynb][1]
The github code is for 14 class classification on the CheXpert chest X-ray dataset. I've revised it for binary classification.
...ANSWER
Answered 2020-Apr-18 at 04:07It looks like you have adapted the training correctly for the binary classification, but the prediction wasn't, as you are still trying it as if it were a multi-class prediction.
The output of your model (varOutput) has the size (batch_size, 1), since there is only one class. The maximum across that dimension will always be 0, since that is the only class available, there is no separate class for 1.
This single class represents both cases (0 and 1), so you can consider it is a the probability of it being positive (1). To get the distinct value of either 0 or 1, you simply use a threshold of 0.5, so everything below that receives the class 0 and above that 1. This can be easily done with torch.round.
But you also have another problem, you're applying the sigmoid function twice in a row, once in the classifier nn.Sigmoid() and then afterwards again torch.sigmoid(varOutput). That is problematic, because sigmoid(0) = 0.5, hence all your probabilities are over 0.5.
The output of your model are already the probabilities, the only thing left is to round them:
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