history_performance = []
training_data = tuple(load_mnist.load_training(output_nodes))
test_data = tuple(load_mnist.load_test())
for i in range(0, 100):
scorecard = []
network = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
for row in training_data:
network.train(*row)
for row in test_data:
correct_label = row[1]
outputs = network.query(row[0])
label = numpy.argmax(outputs)
if label == correct_label:
scorecard.append(1)
else:
scorecard.append(0)
performance = sum(scorecard) / len(scorecard)
print("ШАГ:", i, "/,", "Эффективность =", performance)
history_performance.append(performance)
print("Средняя эффективность =", sum(history_performance) / len(history_performance))
if __name__ == "__main__":
pass
step_width = 0.01
x1_test = []
x2_test = []
for i in range(int(1.0 / step_width) + 1):
x1_test.append(i * step_width)
x2_test.append(i * step_width)
y_test = []
# go through all records in the test data set
for i in range(len(x1_test)):
temp = []
for j in range(len(x2_test)):
input_list = [x1_test[i], x2_test[j]]
# query the network
output = neuralNetwork.query(input_list)
# append output value
temp.append(output[0, 0])
# append temp to y
y_test.append(temp)
# Visualize test result
x1_test, x2_test = np.meshgrid(x1_test, x2_test)
y_test = np.array(y_test)
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(x1_test, x2_test, y_test)
plt.title("Network output")
ax.set_xlabel('x1')
# test the neural network
# scorecard for how well the network performs, initially empty
scorecard = []
# go through all the records in the test data set
for record in test_data_list:
# split the record by the ',' commas
all_values = record.split(',')
# correct answer is first value
correct_label = int(all_values[0])
# scale and shift the inputs
inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
# query the network
outputs = n.query(inputs)
# the index of the highest value corresponds to the label
label = np.argmax(outputs)
# append correct or incorrect to list
if (label == correct_label):
# network's answer matches correct answer, add 1 to scorecard
scorecard.append(1)
else:
# network's answer doesn't match correct answer, add 0 to scorecard
scorecard.append(0)
pass
pass
# calculate the performance score, the fraction of correct answers
scorecard_array = np.asarray(scorecard)
