def test_ftz(self): daz.set_ftz() np.testing.assert_equal(self.normal / self.scale, 0) np.testing.assert_equal(self.denormal * self.scale, self.normal) assert np.all(self.denormal != 0) daz.unset_ftz() self.check_normal()
def test_ftz(self): daz.set_ftz() assert self.normal / self.scale == 0 assert self.denormal * self.scale == self.normal assert self.denormal != 0 daz.unset_ftz() self.check_normal()
), training=MiniBatchTraining(batch_size=32, optimizer=AdamOptimizer(0.0001), listener=CostListener())) def show_image(i, x, y): plt.imshow(x[i]) plt.show() print("y = " + str(np.squeeze(y[:, i]))) if __name__ == "__main__": import daz daz.set_ftz() np.seterr(under='warn') plt.rcParams['figure.figsize'] = (7.0, 4.0) plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' # load image dataset: blue/red dots in circles train_x_orig, train_y_orig, test_x_orig, test_y_orig, classes = \ load_dataset() # show_image(0, train_x_orig, train_y_orig) # Pre-processing train_x_flat = train_x_orig.reshape(train_x_orig.shape[0], -1).T test_x_flat = test_x_orig.reshape(test_x_orig.shape[0], -1).T train_x = train_x_flat / 255.