def rundeltamultibatch(n_nodes_v, epochs):
start = time.time()
# Create weight matrices
net = NeuralNet()
n_layers = len(n_nodes_v) - 1
print("Layers = ", n_layers)
for l in range(n_layers + 1):
net.add_layer(n_nodes_v[l])
print(net)
net.generate_weights()
err = []
val = []
x = []
net.set_inputs(0, shuffledx)
net.set_y(0, shuffledx)
net.forward_pass()
net.backward_pass(T)
step_v = np.vectorize(step)
first_classification = T - (np.ceil(net.get_outputs()) * 2 - 1)
# print(net.get_outputs())
for epoch in range(epochs):
net.forward_pass()
net.backward_pass(T)
net.update()
# print(np.mean(np.square(net.get_error())))
x.append(epoch)
err.append(np.mean(np.square(net.get_error())))
val.append(np.count_nonzero(T - (np.ceil(net.get_outputs()) * 2 - 1)))
# print(first_classification)
# print(T - (np.ceil(net.get_outputs())*2-1))
# print(T)
# print(net.get_outputs())
# print(np.ceil(net.get_outputs())*2-1)
print("Time elapsed: ", round(time.time() - start, 3), " seconds")
plot_err_val(x, err, val)
# plotpoints(shuffledx)
show_plots()
