.

+import os
+import numpy as np
+
+from keras import backend as K
+from keras.legacy.interfaces import generate_legacy_interface, recurrent_args_preprocessor
+from keras.models import model_from_json
+
+legacy_prednet_support = generate_legacy_interface(
+    allowed_positional_args=['stack_sizes', 'R_stack_sizes',
+                            'A_filt_sizes', 'Ahat_filt_sizes', 'R_filt_sizes'],
+    conversions=[('dim_ordering', 'data_format'),
+                 ('consume_less', 'implementation')],
+    value_conversions={'dim_ordering': {'tf': 'channels_last',
+                                        'th': 'channels_first',
+                                        'default': None},
+                        'consume_less': {'cpu': 0,
+                                        'mem': 1,
+                                        'gpu': 2}},
+    preprocessor=recurrent_args_preprocessor)
+
+# Convert old Keras (1.2) json models and weights to Keras 2.0
+def convert_model_to_keras2(old_json_file, old_weights_file, new_json_file, new_weights_file):
+    from prednet import PredNet
+    # If using tensorflow, it doesn't allow you to load the old weights.
+    if K.backend() != 'theano':
+        os.environ['KERAS_BACKEND'] = backend
+        reload(K)
+
+    f = open(old_json_file, 'r')
+    json_string = f.read()
+    f.close()
+    model = model_from_json(json_string, custom_objects = {'PredNet': PredNet})
+    model.load_weights(old_weights_file)
+
+    weights = model.layers[1].get_weights()
+    if weights[0].shape[0] == model.layers[1].stack_sizes[1]:
+        for i, w in enumerate(weights):
+            if w.ndim == 4:
+                weights[i] = np.transpose(w, (2, 3, 1, 0))
+        model.set_weights(weights)
+
+    model.save_weights(new_weights_file)
+    json_string = model.to_json()
+    with open(new_json_file, "w") as f:
+        f.write(json_string)
+
+
+if __name__ == '__main__':
+    old_dir = './model_data/'
+    new_dir = './model_data_keras2/'
+    if not os.path.exists(new_dir):
+        os.mkdir(new_dir)
+    for w_tag in ['', '-Lall', '-extrapfinetuned']:
+        m_tag = '' if w_tag == '-Lall' else w_tag
+        convert_model_to_keras2(old_dir + 'prednet_kitti_model' + m_tag + '.json',
+                                old_dir + 'prednet_kitti_weights' + w_tag + '.hdf5',
+                                new_dir + 'prednet_kitti_model' + m_tag + '.json',
+                                new_dir + 'prednet_kitti_weights' + w_tag + '.hdf5')

+DATA_DIR = './data3/'
+
+# Where model weights and config will be saved if you run train.py
+WEIGHTS_DIR = './model_data_keras2/'
+
+# Where results (prediction plots and evaluation file) will be saved.
+RESULTS_SAVE_DIR = './results/'
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