# Run the features through the neural net (to compute a and z)
y_pred = nn.feed_forward(X)
# Compute the gradient
grad_w = nn._gradient_fun(X, T)
# Update the neural network weight matrices
for w, gw in zip(nn.weights, grad_w):
w -= eta * gw
# Print some statistics every thousandth iteration
if i % 1000 == 0:
misclassified = sum(np.argmax(y_pred, axis=1) != y.ravel())
print(
"Iteration: {0}, Objective Function Value: {1:3f}, Misclassified: {2}"
.format(i, nn._J_fun(X, T), misclassified))
# Predict the training data and classify
y_pred = np.argmax(nn.feed_forward(X_test), axis=1)
print("Test data accuracy: {0:3f}".format(1 - sum(y_pred != y_test.ravel()) /
float(len(y_test))))
# Print a matrix of plots showing points and misfit point
fig, axs = plt.subplots(nrows=4, ncols=4)
for i in range(4):
for j in range(4):
if i > j:
axs[i, j].scatter(X_test[:, i], X_test[:, j], c=y_pred)
axs[i, j].plot(X_test[y_test != y_pred, i],
X_test[y_test != y_pred, j],
'ro',
# Perform gradient descent
for i in range(N_iterations):
# For stochastic gradient descent, take random samples of X and T
batch = np.random.randint(0, m, size=batch_size)
# Run the features through the neural net (to compute a and z)
y_pred = nn.feed_forward(X)
# Compute the gradient
grad_w = nn._gradient_fun(X, y)
# Update the neural network weight matrices
for w, gw in zip(nn.weights, grad_w):
w -= eta * gw
# Print some statistics every thousandth iteration
if i % 10000 == 0:
print('Iteration: {0}, Objective Function Value: {1:3f}'
.format(i, nn._J_fun(X, y_pred)))
print('Total error: {}'.format(
np.abs(np.sum(y_pred - y) / np.sum(y))))
plt.plot(X, y, '*')
plt.plot(X, y_pred, '*')
plt.show()
# ========================= EOF ================================================================
