Last Updated on October 22, 2023
It will probably be troublesome to develop a neural neighborhood predictive model for a model new dataset.
One methodology is to first look at the dataset and develop ideas for what fashions might work, then uncover the tutorial dynamics of simple fashions on the dataset, then lastly develop and tune a model for the dataset with a powerful examine harness.
This course of might be utilized to develop environment friendly neural neighborhood fashions for classification and regression predictive modeling points.
In this tutorial, you may uncover the correct strategy to develop a Multilayer Perceptron neural neighborhood model for the banknote binary classification dataset.
After ending this tutorial, you may know:
- How to load and summarize the banknote dataset and use the outcomes to counsel info preparations and model configurations to utilize.
- How to find the tutorial dynamics of simple MLP fashions on the dataset.
- How to develop sturdy estimates of model effectivity, tune model effectivity and make predictions on new info.
Let’s get started.
- Update Oct/2023: Deprecated predict_classes() syntax
Develop a Neural Network for Banknote Authentication
Photo by Lenny K Photography, some rights reserved.
Tutorial Overview
This tutorial is cut up into 4 parts; they’re:
- Banknote Classification Dataset
- Neural Network Learning Dynamics
- Robust Model Evaluation
- Final Model and Make Predictions
Banknote Classification Dataset
The first step is to stipulate and uncover the dataset.
We might be working with the “Banknote” commonplace binary classification dataset.
The banknote dataset consists of predicting whether or not or not a given banknote is real given quite a lot of measures taken from {{a photograph}}.
The dataset accommodates 1,372 rows with 5 numeric variables. It is a classification draw back with two programs (binary classification).
Below affords a listing of the 5 variables throughout the dataset.
- variance of Wavelet Transformed image (regular).
- skewness of Wavelet Transformed image (regular).
- kurtosis of Wavelet Transformed image (regular).
- entropy of image (regular).
- class (integer).
Below is a sample of the first 5 rows of the dataset
| 3.6216,8.6661,-2.8073,-0.44699,0 4.5459,8.1674,-2.4586,-1.4621,0 3.866,-2.6383,1.9242,0.10645,0 3.4566,9.5228,-4.0112,-3.5944,0 0.32924,-4.4552,4.5718,-0.9888,0 4.3684,9.6718,-3.9606,-3.1625,0 … |
You can examine further in regards to the dataset proper right here:
We can load the dataset as a pandas DataFrame immediately from the URL; as an example:
| # load the banknote dataset and summarize the shape from pandas import be taught_csv # define the position of the dataset url = ‘https://uncooked.githubusercontent.com/jbrownlee/Datasets/grasp/banknote_authentication.csv’ # load the dataset df = read_csv(url, header=None) # summarize type print(df.type) |
Running the occasion tons of the dataset immediately from the URL and tales the type of the dataset.
In this case, we’re capable of confirm that the dataset has 5 variables (4 enter and one output) and that the dataset has 1,372 rows of data.
This simply is not many rows of data for a neural neighborhood and suggests {{that a}} small neighborhood, possibly with regularization, could possibly be relevant.
It moreover implies that using k-fold cross-validation could possibly be a very good suggestion given that it will give a further reliable estimate of model effectivity than a put together/examine break up and since a single model will slot in seconds in its place of hours or days with an important datasets.
Next, we’re capable of examine further in regards to the dataset by summary statistics and a plot of the data.
| # current summary statistics and plots of the banknote dataset from pandas import read_csv from matplotlib import pyplot # define the position of the dataset url = ‘https://uncooked.githubusercontent.com/jbrownlee/Datasets/grasp/banknote_authentication.csv’ # load the dataset df = read_csv(url, header=None) # current summary statistics print(df.describe()) # plot histograms df.hist() pyplot.current() |
Running the occasion first tons of the data sooner than after which prints summary statistics for each variable.
We can see that values fluctuate with fully totally different means and commonplace deviations, possibly some normalization or standardization could possibly be required earlier to modeling.
| 0 1 2 3 4 rely 1372.000000 1372.000000 1372.000000 1372.000000 1372.000000 suggest 0.433735 1.922353 1.397627 -1.191657 0.444606 std 2.842763 5.869047 4.310030 2.101013 0.497103 min -7.042100 -13.773100 -5.286100 -8.548200 0.000000 25% -1.773000 -1.708200 -1.574975 -2.413450 0.000000 50% 0.496180 2.319650 0.616630 -0.586650 0.000000 75% 2.821475 6.814625 3.179250 0.394810 1.000000 max 6.824800 12.951600 17.927400 2.449500 1.000000 |
A histogram plot is then created for each variable.
We can see that possibly the first two variables have a Gaussian-like distribution and the next two enter variables might need a skewed Gaussian distribution or an exponential distribution.
We might need some revenue in using an affect rework on each variable in an effort to make the possibility distribution a lot much less skewed which is ready to potential improve model effectivity.
Histograms of the Banknote Classification Dataset
Now that we’re conversant within the dataset, let’s uncover how we might develop a neural neighborhood model.
Neural Network Learning Dynamics
We will develop a Multilayer Perceptron (MLP) model for the dataset using TensorFlow.
We can’t know what model construction of learning hyperparameters could possibly be good or best for this dataset, so we should always experiment and uncover what works properly.
Given that the dataset is small, a small batch measurement may be a very good suggestion, e.g. 16 or 32 rows. Using the Adam mannequin of stochastic gradient descent is an effective suggestion when getting started as it will probably mechanically adapt the tutorial cost and works properly on most datasets.
Before we take into account fashions in earnest, it is a good suggestion to evaluation the tutorial dynamics and tune the model construction and learning configuration until we have now now regular learning dynamics, then take a look at getting basically probably the most out of the model.
We can try this by the usage of a simple put together/examine break up of the data and evaluation plots of the tutorial curves. This will help us see if we’re over-learning or under-learning; then we’re capable of adapt the configuration accordingly.
First, we should always assure all enter variables are floating-point values and encode the aim label as integer values 0 and 1.
| ... # assure all info are floating degree values X = X.astype(‘float32’) # encode strings to integer y = LabelEncoder().fit_transform(y) |
Next, we’re capable of break up the dataset into enter and output variables, then into 67/33 put together and examine models.
| ... # break up into enter and output columns X, y = df.values[:, :–1], df.values[:, –1] # break up into put together and examine datasets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) |
We can define a minimal MLP model. In this case, we’re going to use one hidden layer with 10 nodes and one output layer (chosen arbitrarily). We will use the ReLU activation function throughout the hidden layer and the “he_normal” weight initialization, as collectively, they’re an excellent apply.
The output of the model is a sigmoid activation for binary classification and we’re going to lower binary cross-entropy loss.
| ... # determine the number of enter choices n_features = X.type[1] # define model model = Sequential() model.add(Dense(10, activation=‘relu’, kernel_initializer=‘he_normal’, input_shape=(n_features,))) model.add(Dense(1, activation=‘sigmoid’)) # compile the model model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’) |
We will match the model for 50 teaching epochs (chosen arbitrarily) with a batch measurement of 32 on account of it is a small dataset.
We have gotten the model on raw info, which we predict could possibly be a very good suggestion, but it surely absolutely is a crucial begin line.
At the highest of teaching, we’re going to take into account the model’s effectivity on the examine dataset and report effectivity as a result of the classification accuracy.
| ... # predict examine set and convert to class label ypred = model.predict(X_test) yhat = (ypred > 0.5).flatten().astype(int) # take into account predictions score = accuracy_score(y_test, yhat) print(‘Accuracy: %.3f’ % score) |
Finally, we’re going to plot learning curves of the cross-entropy loss on the put together and examine models all through teaching.
| ... # plot learning curves pyplot.title(‘Learning Curves’) pyplot.xlabel(‘Epoch’) pyplot.ylabel(‘Cross Entropy’) pyplot.plot(historic previous.historic previous[‘loss’], label=‘put together’) pyplot.plot(historic previous.historic previous[‘val_loss’], label=‘val’) pyplot.legend() pyplot.current() |
Tying this all collectively, the entire occasion of evaluating our first MLP on the banknote dataset is listed underneath.
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 41 42 43 | # match a simple mlp model on the banknote and evaluation learning curves from pandas import read_csv from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.metrics import accuracy_score from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense from matplotlib import pyplot # load the dataset path = ‘https://uncooked.githubusercontent.com/jbrownlee/Datasets/grasp/banknote_authentication.csv’ df = read_csv(path, header=None) # break up into enter and output columns X, y = df.values[:, :–1], df.values[:, –1] # assure all info are floating degree values X = X.astype(‘float32’) # encode strings to integer y = LabelEncoder().fit_transform(y) # break up into put together and examine datasets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) # determine the number of enter choices n_features = X.type[1] # define model model = Sequential() model.add(Dense(10, activation=‘relu’, kernel_initializer=‘he_normal’, input_shape=(n_features,))) model.add(Dense(1, activation=‘sigmoid’)) # compile the model model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’) # match the model historic previous = model.match(X_train, y_train, epochs=50, batch_size=32, verbose=0, validation_data=(X_test,y_test)) # predict examine set and convert to class label ypred = model.predict(X_test) yhat = (ypred > 0.5).flatten().astype(int) # take into account predictions score = accuracy_score(y_test, yhat) print(‘Accuracy: %.3f’ % score) # plot learning curves pyplot.title(‘Learning Curves’) pyplot.xlabel(‘Epoch’) pyplot.ylabel(‘Cross Entropy’) pyplot.plot(historic previous.historic previous[‘loss’], label=‘put together’) pyplot.plot(historic previous.historic previous[‘val_loss’], label=‘val’) pyplot.legend() pyplot.current() |
Running the occasion first matches the model on the teaching dataset, then tales the classification accuracy on the examine dataset.
Note: Your outcomes would possibly fluctuate given the stochastic nature of the algorithm or evaluation course of, or variations in numerical precision. Consider working the occasion a few situations and consider the widespread consequence.
In this case, we’re capable of see that the model achieved good or good accuracy of 100% %. This might counsel that the prediction draw back is easy and/or that neural networks are an excellent match for the problem.
Line plots of the loss on the put together and examine models are then created.
We can see that the model appears to converge properly and would not current any indicators of overfitting or underfitting.
Learning Curves of Simple Multilayer Perceptron on Banknote Dataset
We did amazingly properly on our first try.
Now that we have now now some considered the tutorial dynamics for a simple MLP model on the dataset, we’re in a position to take a look at making a further sturdy evaluation of model effectivity on the dataset.
Robust Model Evaluation
The k-fold cross-validation course of can current a further reliable estimate of MLP effectivity, although it might be very gradual.
This is on account of okay fashions ought to be match and evaluated. This simply is not a difficulty when the dataset measurement is small, such as a result of the banknote dataset.
We can use the StratifiedKFold class and enumerate each fold manually, match the model, take into account it, after which report the suggest of the evaluation scores on the end of the method.
| ... # put collectively cross validation kfold = KFold(10) # enumerate splits scores = document() for train_ix, test_ix in kfold.break up(X, y): # match and take into account the model… ... ... # summarize all scores print(‘Mean Accuracy: %.3f (%.3f)’ % (suggest(scores), std(scores))) |
We can use this framework to develop a reliable estimate of MLP model effectivity with our base configuration, and even with a selection of assorted info preparations, model architectures, and learning configurations.
It is important that we first developed an understanding of the tutorial dynamics of the model on the dataset throughout the earlier half sooner than using k-fold cross-validation to estimate the effectivity. If we started to tune the model immediately, we might get good outcomes, however when not, we might do not know of why, e.g. that the model was over or beneath turning into.
If we make big modifications to the model as soon as extra, it is a good suggestion to return and be sure that the model is converging appropriately.
The full occasion of this framework to guage the underside MLP model from the sooner half is listed underneath.
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 41 42 43 44 45 | # k-fold cross-validation of base model for the banknote dataset from numpy import suggest from numpy import std from pandas import read_csv from sklearn.model_selection import StratifiedKFold from sklearn.preprocessing import LabelEncoder from sklearn.metrics import accuracy_score from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense from matplotlib import pyplot # load the dataset path = ‘https://uncooked.githubusercontent.com/jbrownlee/Datasets/grasp/banknote_authentication.csv’ df = read_csv(path, header=None) # break up into enter and output columns X, y = df.values[:, :–1], df.values[:, –1] # assure all info are floating degree values X = X.astype(‘float32’) # encode strings to integer y = LabelEncoder().fit_transform(y) # put collectively cross validation kfold = StratifiedKFold(10) # enumerate splits scores = document() for train_ix, test_ix in kfold.break up(X, y): # break up info X_train, X_test, y_train, y_test = X[train_ix], X[test_ix], y[train_ix], y[test_ix] # determine the number of enter choices n_features = X.type[1] # define model model = Sequential() model.add(Dense(10, activation=‘relu’, kernel_initializer=‘he_normal’, input_shape=(n_features,))) model.add(Dense(1, activation=‘sigmoid’)) # compile the model model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’) # match the model model.match(X_train, y_train, epochs=50, batch_size=32, verbose=0) # predict examine set and convert to class label ypred = model.predict(X_test) yhat = (ypred > 0.5).flatten().astype(int) # take into account predictions score = accuracy_score(y_test, yhat) print(‘>%.3f’ % score) scores.append(score) # summarize all scores print(‘Mean Accuracy: %.3f (%.3f)’ % (suggest(scores), std(scores))) |
Running the occasion tales the model effectivity each iteration of the evaluation course of and tales the suggest and commonplace deviation of classification accuracy on the end of the run.
Note: Your outcomes would possibly fluctuate given the stochastic nature of the algorithm or evaluation course of, or variations in numerical precision. Consider working the occasion a few situations and consider the widespread consequence.
In this case, we’re capable of see that the MLP model achieved a suggest accuracy of about 99.9 %.
This confirms our expectation that the underside model configuration works very properly for this dataset, and definitely the model is an efficient match for the problem and possibly the problem is type of trivial to resolve.
This is beautiful (to me) on account of I’d have anticipated some info scaling and possibly an affect rework to be required.
| >1.000 >1.000 >1.000 >1.000 >0.993 >1.000 >1.000 >1.000 >1.000 >1.000 Mean Accuracy: 0.999 (0.002) |
Next, let’s take a look at how we might match a closing model and use it to make predictions.
Final Model and Make Predictions
Once we choose a model configuration, we’re capable of put together a closing model on all accessible info and use it to make predictions on new info.
In this case, we’re going to use the model with dropout and a small batch measurement as our closing model.
We can put collectively the data and match the model as sooner than, although on the whole dataset in its place of a training subset of the dataset.
| ... # break up into enter and output columns X, y = df.values[:, :–1], df.values[:, –1] # assure all info are floating degree values X = X.astype(‘float32’) # encode strings to integer le = LabelEncoder() y = le.fit_transform(y) # determine the number of enter choices n_features = X.type[1] # define model model = Sequential() model.add(Dense(10, activation=‘relu’, kernel_initializer=‘he_normal’, input_shape=(n_features,))) model.add(Dense(1, activation=‘sigmoid’)) # compile the model model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’) |
We can then use this model to make predictions on new info.
First, we’re capable of define a row of newest info.
| ... # define a row of newest info row = [3.6216,8.6661,–2.8073,–0.44699] |
Note: I took this row from the first row of the dataset and the anticipated label is a ‘0’.
We can then make a prediction.
| ... # make prediction and convert to class label ypred = model.predict([row]) yhat = (ypred > 0.5).flatten().astype(int) |
Then invert the rework on the prediction, so we’re in a position to make use of or interpret the consequence throughout the acceptable label (which is solely an integer for this dataset).
| ... # invert rework to get label for sophistication yhat = le.inverse_transform(yhat) |
And on this case, we’re going to merely report the prediction.
| ... # report prediction print(‘Predicted: %s’ % (yhat[0])) |
Tying this all collectively, the entire occasion of turning into a closing model for the banknote dataset and using it to make a prediction on new info is listed underneath.
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 | # match a closing model and make predictions on new info for the banknote dataset from pandas import read_csv from sklearn.preprocessing import LabelEncoder from sklearn.metrics import accuracy_score from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Dropout # load the dataset path = ‘https://uncooked.githubusercontent.com/jbrownlee/Datasets/grasp/banknote_authentication.csv’ df = read_csv(path, header=None) # break up into enter and output columns X, y = df.values[:, :–1], df.values[:, –1] # assure all info are floating degree values X = X.astype(‘float32’) # encode strings to integer le = LabelEncoder() y = le.fit_transform(y) # determine the number of enter choices n_features = X.type[1] # define model model = Sequential() model.add(Dense(10, activation=‘relu’, kernel_initializer=‘he_normal’, input_shape=(n_features,))) model.add(Dense(1, activation=‘sigmoid’)) # compile the model model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’) # match the model model.match(X, y, epochs=50, batch_size=32, verbose=0) # define a row of newest info row = [3.6216,8.6661,–2.8073,–0.44699] # make prediction and convert to class label ypred = model.predict([row]) yhat = (ypred > 0.5).flatten().astype(int) # invert rework to get label for sophistication yhat = le.inverse_transform(yhat) # report prediction print(‘Predicted: %s’ % (yhat[0])) |
Running the occasion matches the model on the whole dataset and makes a prediction for a single row of newest info.
Note: Your outcomes would possibly fluctuate given the stochastic nature of the algorithm or evaluation course of, or variations in numerical precision. Consider working the occasion a few situations and consider the widespread consequence.
In this case, we’re capable of see that the model predicted a “0” label for the enter row.
Further Reading
This half affords further sources on the topic should you’re making an attempt to go deeper.
Tutorials
- How to Develop a Neural Net for Predicting Disturbances throughout the Ionosphere
- Best Results for Standard Machine Learning Datasets
- TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras
- A Gentle Introduction to k-fold Cross-Validation
Summary
In this tutorial, you discovered the correct strategy to develop a Multilayer Perceptron neural neighborhood model for the banknote binary classification dataset.
Specifically, you found:
- How to load and summarize the banknote dataset and use the outcomes to counsel info preparations and model configurations to utilize.
- How to find the tutorial dynamics of simple MLP fashions on the dataset.
- How to develop sturdy estimates of model effectivity, tune model effectivity and make predictions on new info.
Do you’ve got gotten any questions?
Ask your questions throughout the suggestions underneath and I’ll do my best to answer.
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