fix load_model() in keras

load_model_fixes.py

## in model.load_model:
# add import
from .utils.io_utils import ask_to_proceed_with_overwrite, update_config

# At 225 add a version check and call an updating function
model_config = json.loads(model_config.decode('utf-8'))

k_version = float(f.attrs.get('keras_version')[0])
if k_version < 2:
    # Have to rename params to load old models
    model_config = update_config(model_config)

model = model_from_config(model_config, custom_objects=custom_objects)

## in utils.io_utils:
# add imports
from ..legacy.interfaces import all_conversions, all_value_conversions, raise_duplicate_arg_error

# update function
def update_config(model_config):
    """Update outdated config parameters from <2.0

    # Arguments
        model_config: json-decoded model_config from load_model()

    # Returns
        model_config: update model_config
    """



    for l in model_config['config']['layers']:

        # Specific to Convolution2D
        if l['class_name'] == "Convolution2D":
            l['config']['kernel_size'] = [l['config'].pop('nb_row'),
                                          l['config'].pop('nb_col')]

        if 'W_regularizer' in l['config'].keys():
            if l['config']['W_regularizer']:
                l['config']['W_regularizer'].pop('name')
                l1_val = l['config']['W_regularizer'].pop('l1')
                l2_val = l['config']['W_regularizer'].pop('l2')
                l['config']['W_regularizer'][u'class_name'] = u'L1L2'
                l['config']['W_regularizer'][u'config'] = {u'l1': l1_val,
                                                           u'l2': l2_val}

        for key in all_value_conversions:
            if key in l['config']:
                old_value = l['config'][key]
                if old_value in all_value_conversions[key]:
                    l['config'][key] = all_value_conversions[key][old_value]
        for old_name, new_name in all_conversions:
            if old_name in l['config']:
                value = l['config'].pop(old_name)
                if new_name in l['config']:
                    raise_duplicate_arg_error(old_name, new_name)
                l['config'][new_name] = value

    return model_config
  
## in legacy.interfaces
# make list/dict of common conversions/value conversions
  
all_conversions = [('output_dim', 'units'),
                 ('init', 'kernel_initializer'),
                 ('W_regularizer', 'kernel_regularizer'),
                 ('b_regularizer', 'bias_regularizer'),
                 ('W_constraint', 'kernel_constraint'),
                 ('b_constraint', 'bias_constraint'),
                 ('bias', 'use_bias'),
                 ('p', 'rate'),
                 ('pool_length', 'pool_size'),
                 ('stride', 'strides'),
                 ('border_mode', 'padding'),
                 ('sigma', 'stddev'),
                 ('nb_filter', 'filters'),
                 ('subsample', 'strides'),
                 ('border_mode', 'padding'),
                 ('dim_ordering', 'data_format'),
                 ('init', 'kernel_initializer'),
                 ('W_regularizer', 'kernel_regularizer'),
                 ('b_regularizer', 'bias_regularizer'),
                 ('W_constraint', 'kernel_constraint'),
                 ('b_constraint', 'bias_constraint'),
                 ('bias', 'use_bias'),
                 ('input_dtype', 'dtype),
                 ('beta_init', 'beta_initializer'),
                 ('gamma_init', 'gamma_initializer')]

all_value_conversions = {'dim_ordering': {'tf': 'channels_last',
                                        'th': 'channels_first',
                                        'default': None}}

I upgraded to Keras 2.0.7
I need to make prediction with models saved in Keras 1.2.2
When I load the saved trained mode,
I get an error on the input shape.

ValueError: Error when checking : expected convolution2d_5_input to have 4 dimensions, but got array with shape (4L, 33L, 33L)

I think this script solves a different problem,
but I tried to run it and get an error:
TypeError Traceback (most recent call last)
in ()
11 v = out_h5.attrs.get("model_config")
12 config = json.loads(v)
---> 13 for i, l in enumerate(config["config"]["layers"]):
14 dtype = l["config"].pop("input_dtype", None)
15 if dtype is not None:

TypeError: list indices must be integers, not str

im having the same issue....