def test_to_model_params_conv_net_values_are_close_to_original(self): model = conv_model(mnist_single_items()) sut, model_weights, is_bias, biases = self.get_sut_and_test_input( model) input = self.weights_as_single_vector(model_weights) recreated_params = sut.to_model_params(input) recreated_params = self.unwrap_list(recreated_params) for weights_matrix, is_b, bias, recreated_weights in zip( model_weights, is_bias, biases, recreated_params): self.is_close(recreated_weights, is_b, bias, weights_matrix)
def test_two_plots(): model = conv_model(mnist_single_items()) _, _, x_test, y_test = mnist_single_items() plot_loss_3D(model, ("levels", "3d"), x_test, y_test)
def test_unlearned_conv_model(): model = conv_model(mnist_single_items()) _, _, x_test, y_test = mnist_single_items() plot_loss_3D(model, "levels", x_test, y_test)
def test_unlearned_conv_model(): model = conv_model(mnist_single_items()) _, _, x_test, y_test = mnist_single_items() plot_loss(model, (x_test, y_test))
def setup(cls): if not hasattr( cls, 'initialized'): # avoid learning networks multiple times cls.initialized = True cls.dense = dense_model(toy_dataset()) cls.conv = conv_model(mnist_dataset())
def test_conv_mnist_model(): model = conv_model(mnist_dataset()) _, _, x_test, y_test = mnist_dataset() plot_loss_3D(model, "levels", x_test, y_test)