def main():
height, width = <NUM_LIT>, <NUM_LIT><EOL>inputs = tf.Variable(tf.random_uniform((<NUM_LIT:1>, height, width, <NUM_LIT:3>)), name='<STR_LIT:input>')<EOL>inputs = tf.identity(inputs, "<STR_LIT>")<EOL>with slim.arg_scope(overfeat.overfeat_arg_scope()):<EOL><INDENT>net, end_points = overfeat.overfeat(inputs, is_training = False)<EOL><DEDENT>print("<STR_LIT>")<EOL>for n in end_points:<EOL><INDENT>print(n + "<STR_LIT>" + str(end_points[n]))<EOL><DEDENT>net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[<NUM_LIT:2>].split('<STR_LIT:U+002C>'))<EOL>run_model(net_outputs, argv[<NUM_LIT:1>], '<STR_LIT>', argv[<NUM_LIT:3>] == '<STR_LIT:True>')<EOL>
You can also run these commands manually to generate the pb file
1. git clone https://github.com/tensorflow/models.git
2. export PYTHONPATH=Path_to_your_model_folder
3. python alexnet.py