Learn Keras by Example – How to Visualize Performance History

Hits: 20

Visualize Performance History

Preliminaries


/* Load libraries */
import numpy as np
from keras.datasets import imdb
from keras.preprocessing.text import Tokenizer
from keras import models
from keras import layers
import matplotlib.pyplot as plt

/* Set random seed */
np.random.seed(0)
Using TensorFlow backend.

Load Movie Review Data


/* Set the number of features we want */
number_of_features = 10000

/* Load data and target vector from movie review data */
(train_data, train_target), (test_data, test_target) = imdb.load_data(num_words=number_of_features)

/* Convert movie review data to a one-hot encoded feature matrix */
tokenizer = Tokenizer(num_words=number_of_features)
train_features = tokenizer.sequences_to_matrix(train_data, mode='binary')
test_features = tokenizer.sequences_to_matrix(test_data, mode='binary')

Create Neural Network Architecture


/* Start neural network */
network = models.Sequential()

/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu', input_shape=(number_of_features,)))

/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu'))

/* Add fully connected layer with a sigmoid activation function */
network.add(layers.Dense(units=1, activation='sigmoid'))

Compile Neural Network


/* Compile neural network */
network.compile(loss='binary_crossentropy', # Cross-entropy
                optimizer='rmsprop', # Root Mean Square Propagation
                metrics=['accuracy']) # Accuracy performance metric

Train Neural Network


/* Train neural network */
history = network.fit(train_features, /* Features */
                      train_target, /* Target */
                      epochs=15, /* Number of epochs */
                      verbose=0, /* No output */
                      batch_size=1000, /* Number of observations per batch */
                      validation_data=(test_features, test_target)) /* Data for evaluation */

Visualize Neural Network Performance History

Specifically, we visualize the neural network’s accuracy score on training and test sets over each epoch.


/* Get training and test accuracy histories */
training_accuracy = history.history['acc']
test_accuracy = history.history['val_acc']

/* Create count of the number of epochs */
epoch_count = range(1, len(training_accuracy) + 1)

/* Visualize accuracy history */
plt.plot(epoch_count, training_accuracy, 'r--')
plt.plot(epoch_count, test_accuracy, 'b-')
plt.legend(['Training Accuracy', 'Test Accuracy'])
plt.xlabel('Epoch')
plt.ylabel('Accuracy Score')
plt.show();

png

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  

Google –> SETScholars

A list of Python, R and SQL Codes for Applied Machine Learning and Data  Science at https://setscholars.net/Learn by Coding Categories:

  1. Classification: https://setscholars.net/category/classification/
  2. Data Analytics: https://setscholars.net/category/data-analytics/
  3. Data Science: https://setscholars.net/category/data-science/
  4. Data Visualisation: https://setscholars.net/category/data-visualisation/
  5. Machine Learning Recipe: https://setscholars.net/category/machine-learning-recipe/
  6. Pandas: https://setscholars.net/category/pandas/
  7. Python: https://setscholars.net/category/python/
  8. SKLEARN: https://setscholars.net/category/sklearn/
  9. Supervised Learning: https://setscholars.net/category/supervised-learning/
  10. Tabular Data Analytics: https://setscholars.net/category/tabular-data-analytics/
  11. End-to-End Data Science Recipes: https://setscholars.net/category/a-star-data-science-recipe/
  12. Applied Statistics: https://setscholars.net/category/applied-statistics/
  13. Bagging Ensemble: https://setscholars.net/category/bagging-ensemble/
  14. Boosting Ensemble: https://setscholars.net/category/boosting-ensemble/
  15. CatBoost: https://setscholars.net/category/catboost/
  16. Clustering: https://setscholars.net/category/clustering/
  17. Data Analytics: https://setscholars.net/category/data-analytics/
  18. Data Science: https://setscholars.net/category/data-science/
  19. Data Visualisation: https://setscholars.net/category/data-visualisation/
  20. Decision Tree: https://setscholars.net/category/decision-tree/
  21. LightGBM: https://setscholars.net/category/lightgbm/
  22. Machine Learning Recipe: https://setscholars.net/category/machine-learning-recipe/
  23. Multi-Class Classification: https://setscholars.net/category/multi-class-classification/
  24. Neural Networks: https://setscholars.net/category/neural-networks/
  25. Python Machine Learning: https://setscholars.net/category/python-machine-learning/
  26. Python Machine Learning Crash Course: https://setscholars.net/category/python-machine-learning-crash-course/
  27. R Classification: https://setscholars.net/category/r-classification/
  28. R for Beginners: https://setscholars.net/category/r-for-beginners/
  29. R for Business Analytics: https://setscholars.net/category/r-for-business-analytics/
  30. R for Data Science: https://setscholars.net/category/r-for-data-science/
  31. R for Data Visualisation: https://setscholars.net/category/r-for-data-visualisation/
  32. R for Excel Users: https://setscholars.net/category/r-for-excel-users/
  33. R Machine Learning: https://setscholars.net/category/r-machine-learning/
  34. R Machine Learning Crash Course: https://setscholars.net/category/r-machine-learning-crash-course/
  35. R Regression: https://setscholars.net/category/r-regression/
  36. Regression: https://setscholars.net/category/regression/
  37. XGBOOST: https://setscholars.net/category/xgboost/
  38. Excel examples for beginners: https://setscholars.net/category/excel-examples-for-beginners/
  39. C Programming tutorials & examples: https://setscholars.net/category/c-programming-tutorials/
  40. Javascript tutorials & examples: https://setscholars.net/category/javascript-tutorials-and-examples/
  41. Python tutorials & examples: https://setscholars.net/category/python-tutorials/
  42. R tutorials & examples: https://setscholars.net/category/r-for-beginners/
  43. SQL tutorials & examples: https://setscholars.net/category/sql-tutorials-for-business-analyst/

 

( FREE downloadable Mathematics Worksheet for Kids )