print('')
print('training...')
print('')
for z in range(0, epochs):
for i in range(0, 60000+4078):
hidden1 = nn.create_hidden(weights['W1'], inputs[i].input, weights['b1'], activation='sigmoid',
dropout=True)
output = nn.create_hidden(weights['W2'], hidden1, weights['b2'], activation='sigmoid', dropout=False)
target = nn.create_target(inputs[i].target)
error = np.subtract(target, output)
hidden1_error = np.dot(weights['W2'].T, error)
# Gradient for the OUTPUT #
derivative_output = nn.Derivative(output, activation='sigmoid')
gradient = error * derivative_output
gradient *= lr
# delta bias2 #
weights['b2'] += gradient
# Delta weights based on gradient and transposed matrices #
delta_weight2 = np.dot(gradient, hidden1.T)
# Gradient for the HIDDEN #
derivative_output = nn.Derivative(hidden1, activation='sigmoid')
gradient = hidden1_error * derivative_output
gradient *= lr
# delta bias1 #
