Release History¶
v0.4-beta1.0¶
Calculation of several metrics for classification
sensitivity (TPR), specificity (TNR), accuracy (acc), likelihood ratio (positive), likelihood ratio (negative), confusion matrix (binary and multiclass), confusion tensor (binary for every class in multi-class)
Sequential Class
Move the cost_f and cost_r parameters to be specified from formation method, leave the class instantiation with just the model architecture.
Move the init_weights method to be specified from formation method.
Execution
Create formation method in the Sequential Class, with the following parameters init, cost, metrics, optimizer.
Store selected metrics in Train and Validation History
Visualizations
Select metrics for verbose output.
v0.3-beta1.0¶
Regularization
L1, L2 and ElasticNet on weights and biases, location: gradients
L1, L2 and ElasticNet on weights and biases, location: cost function
Numerical Stability
in functions.py, in cost, added a 1e-25 value to A, to avoid a divide by zero and invalid multiply cases in computations of np.log(A)
Data Handling
train and validation cost
Visualization
print: verbose of cost evolution
Documentation
Improve README
v0.2-beta1.0¶
Files
complete data set: MNIST
complete data set: ‘fashion-MNIST’
Tests passed
fashion MNIST
previous release tests
Topology
single hidden layer (tested)
1 - 2 hidden layers (tested)
different activation functions among hidden layer
Activation functions
For hidden -> Sigmoid, Tanh, ReLU (tested and not working)
For output -> Softmax
Cost Functions
‘binary-logloss’ (Binary-class Cross-Entropy)
‘multi-logloss’ (Multi-class Cross-Entropy)
Metrics
Confusion matrix (Multi-class)
Accuracy (Multi-class)
v0.1-beta1.0¶
Tests passed
Random XOR data classification
Sequential model
hidden_l: Number of neurons per hidden layer (list of int, with length of l_hidden)
hidden_a: Activation of hidden layers (list of str, with length l_hidden)
output_n: Number of neurons in output layer (1)
output_a: Activation of output layer (str)
Layer transformations
linear
Activation functions
For hidden -> Sigmoid, Tanh
For output -> Sigmoid (Binary)
Weights Initialization
Xavier normal, Xavier uniform, common uniform, according to [1]
Training Schemes
Gradient Descent
Cost Functions
Sum of Squared Error (SSE) or Residual Sum of Squares (RSS)
Metrics
Accuracy (Binary)