print("- Test-set:\t\t{}".format(len(data.test.labels)))
model = Network(img_shape=(50, 50, 1))
model.add_flat_layer()
model.add_fc_layer(size=50 * 50, use_relu=True)
model.add_fc_layer(size=16, use_relu=True)
model.add_fc_layer(size=2, use_relu=False)
model.finish_setup()
model.set_data(data)
model_path = os.path.join(cwd, 'results', 'models', 'crater_model_nn.ckpt')
#model.restore(model_path)
model.print_test_accuracy()
model.optimize(epochs=20)
model.save(model_path)
model.print_test_accuracy()
model.print_test_accuracy(show_example_errors=True)
model.print_test_accuracy(show_example_errors=True, show_confusion_matrix=True)
image1 = data.test.images[7]
plot_image(image1)
image2 = data.test.images[14]
plot_image(image2)
model = Network(img_shape=(50, 50, 1))
model.add_convolutional_layer(5, 16)
model.add_convolutional_layer(5, 36)
model.add_flat_layer()
model.add_fc_layer(size=64, use_relu=True)
model.add_fc_layer(size=16, use_relu=True)
model.add_fc_layer(size=2, use_relu=False)
model.finish_setup()
model.set_data(data)
model_path = os.path.join(cwd, 'models', 'cnn', 'crater_model_cnn_mask.ckpt') # the models with _th indicate that they use positive samples extracted form theresholding images.
#model.restore(model_path)
model.print_test_accuracy()
model.optimize(epochs=100)
model.save(model_path)
model.print_test_accuracy()
model.print_test_accuracy(show_example_errors=True)
model.print_test_accuracy(show_example_errors=True,
show_confusion_matrix=True)
image1 = data.test.images[7]
plot_image(image1)
image2 = data.test.images[14]
plot_image(image2)