(self, paths)
| 145 | return loss |
| 146 | |
| 147 | def predict(self, paths): |
| 148 | fig, axs = plt.subplots(1, 2) |
| 149 | for i, path in enumerate(paths): |
| 150 | x = load_img(path) |
| 151 | length = self.sess.run(self.out, {self.tfx: x}) |
| 152 | axs[i].imshow(x[0]) |
| 153 | axs[i].set_title('Len: %.1f cm' % length) |
| 154 | axs[i].set_xticks(()); axs[i].set_yticks(()) |
| 155 | plt.show() |
| 156 | |
| 157 | def save(self, path='./for_transfer_learning/model/transfer_learn'): |
| 158 | saver = tf.train.Saver() |