79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
import numpy as np
|
|
from data_prep import features, targets, features_test, targets_test
|
|
|
|
np.random.seed(21)
|
|
|
|
|
|
def sigmoid(x):
|
|
"""
|
|
Calculate sigmoid
|
|
"""
|
|
return 1 / (1 + np.exp(-x))
|
|
|
|
|
|
# Hyperparameters
|
|
n_hidden = 2 # number of hidden units
|
|
epochs = 900
|
|
learnrate = 0.005
|
|
|
|
n_records, n_features = features.shape
|
|
last_loss = None
|
|
# Initialize weights
|
|
weights_input_hidden = np.random.normal(scale=1 / n_features ** .5,
|
|
size=(n_features, n_hidden))
|
|
weights_hidden_output = np.random.normal(scale=1 / n_features ** .5,
|
|
size=n_hidden)
|
|
|
|
for e in range(epochs):
|
|
del_w_input_hidden = np.zeros(weights_input_hidden.shape)
|
|
del_w_hidden_output = np.zeros(weights_hidden_output.shape)
|
|
for x, y in zip(features.values, targets):
|
|
## Forward pass ##
|
|
# TODO: Calculate the output
|
|
hidden_input = np.dot(x, weights_input_hidden)
|
|
hidden_output = sigmoid(hidden_input)
|
|
output = sigmoid(np.dot(hidden_output, weights_hidden_output))
|
|
|
|
## Backward pass ##
|
|
# TODO: Calculate the network's prediction error
|
|
error = y - output
|
|
|
|
# TODO: Calculate error term for the output unit
|
|
output_error_term = error * output * (1 - output)
|
|
|
|
# propagate errors to hidden layer
|
|
|
|
# TODO: Calculate the hidden layer's contribution to the error
|
|
hidden_error = np.dot(output_error_term, weights_hidden_output)
|
|
|
|
# TODO: Calculate the error term for the hidden layer
|
|
hidden_error_term = hidden_error * hidden_output * (1 - hidden_output)
|
|
|
|
# TODO: Update the change in weights
|
|
del_w_hidden_output += output_error_term * hidden_output
|
|
del_w_input_hidden += hidden_error_term * x[:, None]
|
|
|
|
# TODO: Update weights (don't forget to division by n_records or number of samples)
|
|
weights_input_hidden += learnrate * del_w_input_hidden / n_records
|
|
weights_hidden_output += learnrate * del_w_hidden_output / n_records
|
|
|
|
# Printing out the mean square error on the training set
|
|
if e % (epochs / 10) == 0:
|
|
hidden_output = sigmoid(np.dot(x, weights_input_hidden))
|
|
out = sigmoid(np.dot(hidden_output,
|
|
weights_hidden_output))
|
|
loss = np.mean((out - targets) ** 2)
|
|
|
|
if last_loss and last_loss < loss:
|
|
print("Train loss: ", loss, " WARNING - Loss Increasing")
|
|
else:
|
|
print("Train loss: ", loss)
|
|
last_loss = loss
|
|
|
|
# Calculate accuracy on test data
|
|
hidden = sigmoid(np.dot(features_test, weights_input_hidden))
|
|
out = sigmoid(np.dot(hidden, weights_hidden_output))
|
|
predictions = out > 0.5
|
|
accuracy = np.mean(predictions == targets_test)
|
|
print("Prediction accuracy: {:.3f}".format(accuracy))
|