It can be difficult to determine whether your Long Short-Term Memory model is performing well on your sequence prediction problem.

You may be getting a good model skill score, but it is important to know whether your model is a good fit for your data or if it is underfit or overfit and could do better with a different configuration.

In this tutorial, you will discover how you can diagnose the fit of your LSTM model on your sequence prediction problem.

After completing this tutorial, you will know:

- How to gather and plot training history of LSTM models.
- How to diagnose an underfit, good fit, and overfit model.
- How to develop more robust diagnostics by averaging multiple model runs.

Let’s get started.

## Tutorial Overview

This tutorial is divided into 6 parts; they are:

- Training History in Keras
- Diagnostic Plots
- Underfit Example
- Good Fit Example
- Overfit Example
- Multiple Runs Example

## 1. Training History in Keras

You can learn a lot about the behavior of your model by reviewing its performance over time.

LSTM models are trained by calling the* fit()* function. This function returns a variable called *history* that contains a trace of the loss and any other metrics specified during the compilation of the model. These scores are recorded at the end of each epoch.

1 2 |
... history = model.fit(...) |

For example, if your model was compiled to optimize the log loss (*binary_crossentropy*) and measure accuracy each epoch, then the log loss and accuracy will be calculated and recorded in the history trace for each training epoch.

Each score is accessed by a key in the history object returned from calling *fit()*. By default, the loss optimized when fitting the model is called “*loss*” and accuracy is called “*acc*“.

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... model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) history = model.fit(X, Y, epochs=100) print(history.history['loss']) print(history.history['acc']) |

Keras also allows you to specify a separate validation dataset while fitting your model that can also be evaluated using the same loss and metrics.

This can be done by setting the *validation_split* argument on *fit()* to use a portion of the training data as a validation dataset.

1 2 |
... history = model.fit(X, Y, epochs=100, validation_split=0.33) |

This can also be done by setting the *validation_data* argument and passing a tuple of X and y datasets.

1 2 |
... history = model.fit(X, Y, epochs=100, validation_data=(valX, valY)) |

The metrics evaluated on the validation dataset are keyed using the same names, with a “*val_*” prefix.

1 2 3 4 5 6 7 |
... model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) history = model.fit(X, Y, epochs=100, validation_split=0.33) print(history.history['loss']) print(history.history['acc']) print(history.history['val_loss']) print(history.history['val_acc']) |

## 2. Diagnostic Plots

The training history of your LSTM models can be used to diagnose the behavior of your model.

You can plot the performance of your model using the Matplotlib library. For example, you can plot training loss vs test loss as follows:

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from matplotlib import pyplot ... history = model.fit(X, Y, epochs=100, validation_data=(valX, valY)) pyplot.plot(history.history['loss']) pyplot.plot(history.history['val_loss']) pyplot.title('model train vs validation loss') pyplot.ylabel('loss') pyplot.xlabel('epoch') pyplot.legend(['train', 'validation'], loc='upper right') pyplot.show() |

Creating and reviewing these plots can help to inform you about possible new configurations to try in order to get better performance from your model.

Next, we will look at some examples. We will consider model skill on the train and validation sets in terms of loss that is minimized. You can use any metric that is meaningful on your problem.

## 3. Underfit Example

An underfit model is one that is demonstrated to perform well on the training dataset and poor on the test dataset.

This can be diagnosed from a plot where the training loss is lower than the validation loss, and the validation loss has a trend that suggests further improvements are possible.

A small contrived example of an underfit LSTM model is provided below.

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from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from matplotlib import pyplot from numpy import array # return training data def get_train(): seq = [[0.0, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.4, 0.5]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((len(X), 1, 1)) return X, y # return validation data def get_val(): seq = [[0.5, 0.6], [0.6, 0.7], [0.7, 0.8], [0.8, 0.9], [0.9, 1.0]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((len(X), 1, 1)) return X, y # define model model = Sequential() model.add(LSTM(10, input_shape=(1,1))) model.add(Dense(1, activation='linear')) # compile model model.compile(loss='mse', optimizer='adam') # fit model X,y = get_train() valX, valY = get_val() history = model.fit(X, y, epochs=100, validation_data=(valX, valY), shuffle=False) # plot train and validation loss pyplot.plot(history.history['loss']) pyplot.plot(history.history['val_loss']) pyplot.title('model train vs validation loss') pyplot.ylabel('loss') pyplot.xlabel('epoch') pyplot.legend(['train', 'validation'], loc='upper right') pyplot.show() |

Running this example produces a plot of train and validation loss showing the characteristic of an underfit model. In this case, performance may be improved by increasing the number of training epochs.

In this case, performance may be improved by increasing the number of training epochs.

Alternately, a model may be underfit if performance on the training set is better than the validation set and performance has leveled off. Below is an example of an

Below is an example of an an underfit model with insufficient memory cells.

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from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from matplotlib import pyplot from numpy import array # return training data def get_train(): seq = [[0.0, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.4, 0.5]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((5, 1, 1)) return X, y # return validation data def get_val(): seq = [[0.5, 0.6], [0.6, 0.7], [0.7, 0.8], [0.8, 0.9], [0.9, 1.0]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((len(X), 1, 1)) return X, y # define model model = Sequential() model.add(LSTM(1, input_shape=(1,1))) model.add(Dense(1, activation='linear')) # compile model model.compile(loss='mae', optimizer='sgd') # fit model X,y = get_train() valX, valY = get_val() history = model.fit(X, y, epochs=300, validation_data=(valX, valY), shuffle=False) # plot train and validation loss pyplot.plot(history.history['loss']) pyplot.plot(history.history['val_loss']) pyplot.title('model train vs validation loss') pyplot.ylabel('loss') pyplot.xlabel('epoch') pyplot.legend(['train', 'validation'], loc='upper right') pyplot.show() |

Running this example shows the characteristic of an underfit model that appears under-provisioned.

In this case, performance may be improved by increasing the capacity of the model, such as the number of memory cells in a hidden layer or number of hidden layers.

## 4. Good Fit Example

A good fit is a case where the performance of the model is good on both the train and validation sets.

This can be diagnosed from a plot where the train and validation loss decrease and stabilize around the same point.

The small example below demonstrates an LSTM model with a good fit.

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from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from matplotlib import pyplot from numpy import array # return training data def get_train(): seq = [[0.0, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.4, 0.5]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((5, 1, 1)) return X, y # return validation data def get_val(): seq = [[0.5, 0.6], [0.6, 0.7], [0.7, 0.8], [0.8, 0.9], [0.9, 1.0]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((len(X), 1, 1)) return X, y # define model model = Sequential() model.add(LSTM(10, input_shape=(1,1))) model.add(Dense(1, activation='linear')) # compile model model.compile(loss='mse', optimizer='adam') # fit model X,y = get_train() valX, valY = get_val() history = model.fit(X, y, epochs=800, validation_data=(valX, valY), shuffle=False) # plot train and validation loss pyplot.plot(history.history['loss']) pyplot.plot(history.history['val_loss']) pyplot.title('model train vs validation loss') pyplot.ylabel('loss') pyplot.xlabel('epoch') pyplot.legend(['train', 'validation'], loc='upper right') pyplot.show() |

Running the example creates a line plot showing the train and validation loss meeting.

Ideally, we would like to see model performance like this if possible, although this may not be possible on challenging problems with a lot of data.

## 5. Overfit Example

An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.

This can be diagnosed from a plot where the train loss slopes down and the validation loss slopes down, hits an inflection point, and starts to slope up again.

The example below demonstrates an overfit LSTM model.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from matplotlib import pyplot from numpy import array # return training data def get_train(): seq = [[0.0, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.4, 0.5]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((5, 1, 1)) return X, y # return validation data def get_val(): seq = [[0.5, 0.6], [0.6, 0.7], [0.7, 0.8], [0.8, 0.9], [0.9, 1.0]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((len(X), 1, 1)) return X, y # define model model = Sequential() model.add(LSTM(10, input_shape=(1,1))) model.add(Dense(1, activation='linear')) # compile model model.compile(loss='mse', optimizer='adam') # fit model X,y = get_train() valX, valY = get_val() history = model.fit(X, y, epochs=1200, validation_data=(valX, valY), shuffle=False) # plot train and validation loss pyplot.plot(history.history['loss'][500:]) pyplot.plot(history.history['val_loss'][500:]) pyplot.title('model train vs validation loss') pyplot.ylabel('loss') pyplot.xlabel('epoch') pyplot.legend(['train', 'validation'], loc='upper right') pyplot.show() |

Running this example creates a plot showing the characteristic inflection point in validation loss of an overfit model.

This may be a sign of too many training epochs.

In this case, the model training could be stopped at the inflection point. Alternately, the number of training examples could be increased.

## 6. Multiple Runs Example

LSTMs are stochastic, meaning that you will get a different diagnostic plot each run.

It can be useful to repeat the diagnostic run multiple times (e.g. 5, 10, or 30). The train and validation traces from each run can then be plotted to give a more robust idea of the behavior of the model over time.

The example below runs the same experiment a number of times before plotting the trace of train and validation loss for each run.

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from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from matplotlib import pyplot from numpy import array from pandas import DataFrame # return training data def get_train(): seq = [[0.0, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.4, 0.5]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((5, 1, 1)) return X, y # return validation data def get_val(): seq = [[0.5, 0.6], [0.6, 0.7], [0.7, 0.8], [0.8, 0.9], [0.9, 1.0]] seq = array(seq) X, y = seq[:, 0], seq[:, 1] X = X.reshape((len(X), 1, 1)) return X, y # collect data across multiple repeats train = DataFrame() val = DataFrame() for i in range(5): # define model model = Sequential() model.add(LSTM(10, input_shape=(1,1))) model.add(Dense(1, activation='linear')) # compile model model.compile(loss='mse', optimizer='adam') X,y = get_train() valX, valY = get_val() # fit model history = model.fit(X, y, epochs=300, validation_data=(valX, valY), shuffle=False) # story history train[str(i)] = history.history['loss'] val[str(i)] = history.history['val_loss'] # plot train and validation loss across multiple runs pyplot.plot(train, color='blue', label='train') pyplot.plot(val, color='orange', label='validation') pyplot.title('model train vs validation loss') pyplot.ylabel('loss') pyplot.xlabel('epoch') pyplot.show() |

In the resulting plot, we can see that the general trend of underfitting holds across 5 runs and is a stronger case for perhaps increasing the number of training epochs.

## Further Reading

This section provides more resources on the topic if you are looking go deeper.

## Summary

In this tutorial, you discovered how to diagnose the fit of your LSTM model on your sequence prediction problem.

Specifically, you learned:

- How to gather and plot training history of LSTM models.
- How to diagnose an underfit, good fit, and overfit model.
- How to develop more robust diagnostics by averaging multiple model runs.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

Very useful insight. Thanks for sharing!

Thanks Shiraz

Some good info thanks. Are you sure your x axis units are correct though?

Yes, in one case (Overfit Example) I truncated the data for readability.

Hi Jason!

And what if the loss fluctuates? I get up and down peaks after the second epoch and even with 25 epochs the loss in the validation set is greater than 0.5

Could you give me some clue of what is happening?

Thanks!

Good question.

I would recommend looking at the trends in loss over 10s to 100s of epochs, not over very short periods.

Thank you for posting this informative blog on how to diagnose LSTM models. i will definitely try this nifty trick. Please keep on sharing more helpful tips and suggestions in the upcoming posts.

Thanks. I hope it helps.

What other types of tips would you like me to write about?

Hi, James! Any tips how to detect overfitting without validation set (when I have Dropout layers) ?

(I am beginner at deep learning)

The idea of overfitting the training set only has meaning in the context of another dataset, such as a test or validation set.

Also, my name is Jason, not James.

Hi Jason,

Could a test dataset be used to detect overfitting or underfitting to a training dataset without validation dataset? How to do? Is it different to the method using validation dataset?

Thanks a lot. BTW, your lessons are quite benefit, helpful to study machine learning.

Perhaps, but one data point (as opposed to an evaluation each epoch) might not be sufficient to make claims/diagnose model behavior.

Hi Jason,

Hyper-parameter tuning for LSTMS is something really useful – especially in the context of time-series. Looking forward for a blog-post on this topic..

Best,

Andrei

What would you like to see exactly? What parameters?

I have a few posts on tuning LSTMs.

Thank you for your post Jason.

There is also another case, when val loss goes below training loss! This case indicates a highly non-stationary time series with growing mean (or var), wherein the network focuses on the meaty part of the signal which happenes to fall in the val set.

Great tip, thanks.

Hi Jason!

Thank you for this very useful way of diagnosis of LSTM. I’m working on human activity recognition. Now my plot looks like this https://imgur.com/a/55p9b. What can you advise?

I’m trying to classify fitness exercises.

Great work!

Maybe try early stopping around epoch 10?

In this case my accuracy will be:

Train on 21608 samples, validate on 5403 samples

…

21608/21608 [==============================] – 802s – loss: 0.2115 – acc: 0.9304 – val_loss: 0.1949 – val_acc: 0.9337

Epoch 7/50

21608/21608 [==============================] – 849s – loss: 0.1803 – acc: 0.9424 – val_loss: 0.2132 – val_acc: 0.9249

Epoch 8/50

21608/21608 [==============================] – 786s – loss: 0.1632 – acc: 0.9473 – val_loss: 0.2222 – val_acc: 0.9297

Epoch 9/50

21608/21608 [==============================] – 852s – loss: 0.1405 – acc: 0.9558 – val_loss: 0.1563 – val_acc: 0.9460

Epoch 10/50

21608/21608 [==============================] – 799s – loss: 0.1267 – acc: 0.9590 – val_loss: 0.1453 – val_acc: 0.9606

Epoch 11/50

21608/21608 [==============================] – 805s – loss: 0.1147 – acc: 0.9632 – val_loss: 0.1490 – val_acc: 0.9567

Epoch 12/50

21608/21608 [==============================] – 788s – loss: 0.1069 – acc: 0.9645 – val_loss: 0.1176 – val_acc: 0.9626

Epoch 13/50

21608/21608 [==============================] – 838s – loss: 0.1028 – acc: 0.9667 – val_loss: 0.1279 – val_acc: 0.9578

Epoch 14/50

21608/21608 [==============================] – 808s – loss: 0.0889 – acc: 0.9707 – val_loss: 0.1183 – val_acc: 0.9648

Epoch 15/50

21608/21608 [==============================] – 785s – loss: 0.0843 – acc: 0.9729 – val_loss: 0.1000 – val_acc: 0.9706

After 50 epochs accuracy:

Epoch 50/50

21608/21608 [==============================] – 793s – loss: 0.0177 – acc: 0.9950 – val_loss: 0.0772 – val_acc: 0.9832

Also I didn’t use dropout and regularization.

One of my class (rest) have much more samples than other (exercises) https://imgur.com/a/UxEPr.

Confusion matrix – https://imgur.com/a/LYxUu.

I use next model:

model = Sequential()

model.add(Bidirectional(LSTM(128), input_shape = (None,3)))

model.add(Dense(9, activation=’softmax’))

model.compile(loss=’categorical_crossentropy’, optimizer=’rmsprop’, metrics=[‘accuracy’])

mcp = ModelCheckpoint(‘best_model_50_epochs.hd5’, monitor=”val_acc”,

save_best_only=True, save_weights_only=False)

history = model.fit(X_train,

y_train,

batch_size=32,

epochs=50,

validation_split=0.2,

callbacks=[mcp])

And I tried dropout:

model.add(Bidirectional(LSTM(128, dropout=0.5, recurrent_dropout=0.5), input_shape = (None,3)))

train_loss vs val_loss – https://imgur.com/a/k5TVU

accuracy on 50 epochs:

loss: 0.2269 – acc: 0.9244 – val_loss: 0.1574 – val_acc: 0.9558

Hi Jason! What can you advise to increase accuracy, when I have multi classes and one class takes 50% of samples?

(I showed my model above)

Thank you!

I have ideas here:

http://machinelearningmastery.com/improve-deep-learning-performance/

Activation, batch_size (I noticed it correlates with the test size but not always), loss function, number of hidden layers, number of memory cells, optimizer type, input series history length (http://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/)

For all of these parameters I do trial/error testing. Would be nice to know if there is a more systematic way to find the best parameters.

I do much the same myself Andrei. I design a few days worth of runs in a spreadsheet then kick them off to see “what the problem looks like”:

https://machinelearningmastery.com/plan-run-machine-learning-experiments-systematically/

There are some non-LSTM specific ideas for systematically tuning mini-batch gradient descent algorithms here and in the referred paper (e.g. what to test first, second, third, etc.):

https://machinelearningmastery.com/gentle-introduction-mini-batch-gradient-descent-configure-batch-size/

Thank you, Jason! Great work with your blog!

Thanks Andrei!

Hi Jason,

Great post to for us to know how to diagnose our program! I have been playing with RNNs for a while now. And i started by reading your previous posts. They were great for beginners to start off with!

Now as i want to go into more advanced level, i hope i could get your insights/summary on more interesting recent/state-of-the-art models like WaveNet, Deep Speech, using Attention with keras, etc.

Thanks!

Sure, what do you want to know?

Hi Jason,

thank you for this very useful and clear post.

I have a question.

When we set validation_data in fit(), is it just to see the behaviour of the model when fit() is done (this is what i gues!), or does Keras use this validation_data somehow while optimizing from epoch to another to best fit such validation_data? (This is what I hope :-))

I usually prepare three disjoint data sets: training data to train, validation data to optimize the hyper-parameters, and at the end testing data, as out-of-sample data, to test the model. Thus, if Keras optimizes the model based on validation_data, then I don’t have to optimize by myself!

Validation data is only used to give insight into the skill of the model on unseen data during training.

Hi Jason,

Why accuracy value always are 0s when I regress? Loss values decreased. What is the reason for this? Thank you.

model.compile(optimizer=’adam’, loss=’mse’, metrics=[‘mean_squared_error’, ‘accuracy’])

We cannot measure accuracy for regression. It is a measure of correct label predictions and there are no labels in regression.

Hello,

Thank you for your helpful posts.

Is overfitting always solvable?

In other words, what are the necessary and sufficient conditions for a data to be trainable?

Maybe we have enough data, but because data is not trainable, we have overfit.

This is an open problem and specific to each dataset.

I list a ton of empirical tricks to try for a given problem here:

http://machinelearningmastery.com/machine-learning-performance-improvement-cheat-sheet/

Hi Jason,

Thanks for your great and useful post.

I have a kind of weird problem in my LSTM train/validation loss plot. As you can see here [1], the validation loss starts increasing right after the first (or few) epoch(s) while the training loss decreases constantly and finally becomes zero. I used drop-out to deal with this severe overfitting problem, however, at the best case, the validation error remains the same value as it was in the first epoch. I’ve also tried changing various parameters of the model but in all configuration, I have such a increasing trend in validation loss. Is this really an overfitting problem or something is wrong with my implementation or problem phrasing?

[1] https://imgur.com/mZXh4lh

You could explore models with more representational capacity, perhaps more neurons or more layers?

I tested several layers and also a different number of neurons in each layer but again in many tests I see the same increasing trend for validation loss after few epochs.

Perhaps try changing more other aspects like optimization algorithm, learning rate, batch size, and so on, see here:

http://machinelearningmastery.com/improve-deep-learning-performance/

Hi, Jason.I’m quite shocked by your posts. I’m facing a hard situation where validation loss >>

training loss when using LSTM so I googled it . Here https://github.com/karpathy/char-rnn/issues/160 and here https://www.reddit.com/r/MachineLearning/comments/3rmqxd/determing_if_rnn_model_is_underfitting_vs/ they suggested that this can be overfitting. But in your posts, this should be underfitting. I’m confused, can you explained it ?

Thanks for the good article i want to compare it with other tip and trick to make sure there is no conflict.

– Here you say:

Underfit: Training perform well, testing dataset perform poor.

– In other tip and trick it say:

Overfit: Training loss is much lower than validation loss

Is both tips are the same with different diagnostic ? since perform well should mean loss is low

Hi Jason,

There is something that I did not understand from ‘3. underfit’ example. The graph of the first example in this section shows the validation loss decreasing and you also vouch for loss to decrease even further if the network is trained even more with more epochs. How is it possible that the network will give correct prediction on data sequences that it has never seen before. Specifically how can learning on sequences from 0.0 to 0.5 (data from get_train() function) improve prediction on 0.5 to 0.9 (data from get_val() fucntion)

And other thing i want to mention is i have been reading posts on your blog for a month now though commenting for the first time and I would like to tha k you for the persistent content you’ve put here and making machine learning more accessible.

Good question, in the ideal case the model will generalize from examples.

Thanks for your support!

I did not get that Jason, could you please elaborate a bit. Thanks!

Hi Jason,

I tested the underfit example for 500 epochs, and I mentioned the ” metrics = [‘accuracy’] ” in the model.compile functions but when I fit the model , the accuracy metrics was 0 for all 500 epochs. Why accuracy is 0% in this case ?

Perhaps your model has very poor skill?

What do you mean by poor skill ? I tested this blog example (underfit first example for 500 epochs , rest code is the same as in underfit first example ) and checked the accuracy which gives me 0% accuracy but I was expecting a very good accuracy because on 500 epochs Training Loss and Validation loss meets and that is an example of fit model as mentioned in this blog also.

Sorry for the confusion, model skill refers to the model’s ability to make high-quality predictions.

I tested the good fit example of this blog and its also giving me 0 % accuracy. If a good fit model gives 0% accuracy, we have nothing to tune its accuracy because it is already good fit. However, good fit model should give very high quality predictions. I think you should check yourself accuracy of good fit example of this blog and please give some comments on it to clear the confusion.

There is no accuracy in the examples we are working with a contrived regression problem (not classification) and evaluating loss only.

You can learn more about why you cannot evaluate regression using accuracy here:

https://machinelearningmastery.com/classification-versus-regression-in-machine-learning/

Hi,

great article, thank you!

Two short question:

1) The way you designed the code, the states will not be reseted after every epoch which is a problem. Is there a way to include model.state_reset without training the model.fit for only one epoch in a for loop? So basically:

model.fit(X,y, epochs = 100, validation_data = (valx, valy), shuffle = FALSE, stateful = TRUE, state_reset= TRUE) or something like that?

2) If that is not possible, how can I imitate the same behavior? Would it be something like?

for i in range(epos):

model.fit(X,y, epos=1, stateful=true)

yhat_t = model.predict(train, …)

yhat_val = model.predict(val, …)

train_error = msqrt_error(y, yhat_t)

val_error = msqrt_error(y, yhat_val)

model.state_reset()

I just want to plot the error and accuracy development over the epochs

Thanks!

To manage state you must execute the epochs manually one at a time in a loop.

Is it available also for DecisionTreeClasfier ?

Perhaps.

Hi Dr Brownlee,

I’m working on a classification problem using CNN layers, however I’m getting strange loss curves for training and validation. Training loss decreases very rapidly to convergence at a low level with high accuracy. My validation curve eventually converges as well but at a far slower pace and after a lot more epochs. Thus there is a huge gap between the training and validation loss that suddenly closes in after a number of epochs.

I am using Keras/ Tensorflow Architecture is Conv + Batch Normalization + Convo + Batch Normalization + MaxPooling2D repeated 4 times, my CNN is meant to classify an image as one out of around 30 categories. I have more details posted on the following stack exchange

https://stats.stackexchange.com/questions/335890/how-do-i-interpret-my-validation-and-training-loss-curve-if-there-is-a-large-dif

Do you have any insights into what the problem may be for this situation?

Perhaps the model is underspecified? Explore larger models to see if it makes any difference.

Hello Jason.

Thank you for this. I’m working on machine translation using LSTM. Now my plot looks like this https://imgur.com/a/i2dOB87 . What can you advise?

I’m trying to improve acc.

I’m using 4 Layer in encoder and decoder.

Here , the basic implementation.

………………………….

Encoder:

encoder1 = LSTM(lstm_units, return_state=True , return_sequences = True , name = ‘LSTM1’)

encoder2 = LSTM(lstm_units, return_sequences=True, return_state=True , name = ‘LSTM2’)

encoder3 = LSTM(lstm_units, return_sequences=True, return_state=True , name = ‘LSTM3’)

encoder4 = LSTM(lstm_units, return_state=True , name = ‘LSTM4’)

encoder_outputs4 , state_h4 , state_c4 = encoder4(encoder3(encoder2(encoder1(embedded_output))))

………………………………

Decoder:

decoder_lstm1 = LSTM(lstm_units, return_sequences=True, return_state=True , name = ‘Dec_LSTM1’)

decoder_lstm2 = LSTM(lstm_units, return_sequences=True, return_state=True , name = ‘Dec_LSTM2’)

decoder_lstm3 = LSTM(lstm_units, return_sequences=True, return_state=True , name = ‘Dec_LSTM3’)

decoder_lstm4 = LSTM(lstm_units, return_sequences=True, return_state=True , name = ‘Dec_LSTM4′)

decoder_outputs4 , _, _ = decoder_lstm4(decoder_lstm3(decoder_lstm2(decoder_lstm1(embedded_Ar_output,

initial_state=encoder_states4))))

decoder_dense = Dense(output_vector_length, activation=’softmax’)

decoder_outputs = decoder_dense(decoder_outputs4)

*lstm_units : 500

*epochs : 200

………………………..

fit model part:

#Compile Model

model.compile(loss=’mse’, optimizer=’adam’ , metrics=[‘acc’] )

#Train and fit model # Learning!

history = model.fit([dataset_encoder_input, dataset_decoder_input], target ,

batch_size=batch_size,

epochs=epochs,

validation_split=0.2)

*batch_size = 512

Nice work, here are some general ideas to try:

http://machinelearningmastery.com/improve-deep-learning-performance/