I'm trying to restore TensorFlow model. I followed this example: http://nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/ At the end of the code in the example I added these lines: saver = tf.train.Saver() save_path = saver.save(sess, "model.ckpt") print("Model saved in file: %s" % save_path) Two files were created: checkpoint and model.ckpt. In a new python file (tomas_bees_predict.py), I have this code: import tensorflow as tf saver = tf.train.Saver() with tf.Session() as sess: # Restore variables from disk. saver.restore(sess, "model.ckpt") print("Model restored.") However when I execute the code, I get this error: Traceback (most recent call last): File "tomas_bees_predict.py", line 3, in <module> saver = tf.train.Saver() File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 705, in __init__ raise ValueError("No variables to save") ValueError: No variables to save Is there a way to read mode.ckpt file and see what variables are saved? Or maybe someone can help with saving the model and restoring it based on the example described above? EDIT 1: I think I tried running the same code in order to recreate model structure and I was getting the error. I think it could be related to the fact that code described here isn't using named variables: http://nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/ def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) So I did this experiment. I wrote two versions of the code (with and without named variables) to save the model and the code to restore the model. tensor_save_named_vars.py: import tensorflow as tf # Create some variables. v1 = tf.Variable(1, name="v1") v2 = tf.Variable(2, name="v2") # Add an op to initialize the variables. init_op = tf.initialize_all_variables() # Add ops to save and restore all the variables. saver = tf.train.Saver() # Later, launch the model, initialize the variables, do some work, save the # variables to disk. with tf.Session() as sess: sess.run(init_op) print "v1 = ", v1.eval() print "v2 = ", v2.eval() # Save the variables to disk. save_path = saver.save(sess, "/tmp/model.ckpt") print "Model saved in file: ", save_path tensor_save_not_named_vars.py: import tensorflow as tf # Create some variables. v1 = tf.Variable(1) v2 = tf.Variable(2) # Add an op to initialize the variables. init_op = tf.initialize_all_variables() # Add ops to save and restore all the variables. saver = tf.train.Saver() # Later, launch the model, initialize the variables, do some work, save the # variables to disk. with tf.Session() as sess: sess.run(init_op) print "v1 = ", v1.eval() print "v2 = ", v2.eval() # Save the variables to disk. save_path = saver.save(sess, "/tmp/model.ckpt") print "Model saved in file: ", save_path tensor_restore.py: import tensorflow as tf # Create some variables. v1 = tf.Variable(0, name="v1") v2 = tf.Variable(0, name="v2") # Add ops to save and restore all the variables. saver = tf.train.Saver() # Later, launch the model, use the saver to restore variables from disk, and # do some work with the model. with tf.Session() as sess: # Restore variables from disk. saver.restore(sess, "/tmp/model.ckpt") print "Model restored." print "v1 = ", v1.eval() print "v2 = ", v2.eval() Here is what I get when I execute this code: $ python tensor_save_named_vars.py I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4 I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4 v1 = 1 v2 = 2 Model saved in file: /tmp/model.ckpt $ python tensor_restore.py I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4 I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4 Model restored. v1 = 1 v2 = 2 $ python tensor_save_not_named_vars.py I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4 I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4 v1 = 1 v2 = 2 Model saved in file: /tmp/model.ckpt $ python tensor_restore.py I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4 I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4 W tensorflow/core/common_runtime/executor.cc:1076] 0x7ff953881e40 Compute status: Not found: Tensor name "v2" not found in checkpoint files /tmp/model.ckpt [[Node: save/restore_slice_1 = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice_1/tensor_name, save/restore_slice_1/shape_and_slice)]] W tensorflow/core/common_runtime/executor.cc:1076] 0x7ff953881e40 Compute status: Not found: Tensor name "v1" not found in checkpoint files /tmp/model.ckpt [[Node: save/restore_slice = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice/tensor_name, save/restore_slice/shape_and_slice)]] Traceback (most recent call last): File "tensor_restore.py", line 14, in <module> saver.restore(sess, "/tmp/model.ckpt") File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 891, in restore sess.run([self._restore_op_name], {self._filename_tensor_name: save_path}) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 368, in run results = self._do_run(target_list, unique_fetch_targets, feed_dict_string) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 444, in _do_run e.code) tensorflow.python.framework.errors.NotFoundError: Tensor name "v2" not found in checkpoint files /tmp/model.ckpt [[Node: save/restore_slice_1 = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice_1/tensor_name, save/restore_slice_1/shape_and_slice)]] Caused by op u'save/restore_slice_1', defined at: File "tensor_restore.py", line 8, in <module> saver = tf.train.Saver() File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 713, in __init__ restore_sequentially=restore_sequentially) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 432, in build filename_tensor, vars_to_save, restore_sequentially, reshape) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 191, in _AddRestoreOps values = self.restore_op(filename_tensor, vs, preferred_shard) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 106, in restore_op preferred_shard=preferred_shard) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/io_ops.py", line 189, in _restore_slice preferred_shard, name=name) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_io_ops.py", line 271, in _restore_slice preferred_shard=preferred_shard, name=name) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 664, in apply_op op_def=op_def) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1834, in create_op original_op=self._default_original_op, op_def=op_def) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1043, in __init__ self._traceback = _extract_stack() So perhaps the original code (see the external link above) could be modified to something like this: def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) weight_var = tf.Variable(initial, name="weight_var") return weight_var def bias_variable(shape): initial = tf.constant(0.1, shape=shape) bias_var = tf.Variable(initial, name="bias_var") return bias_var But then the question I have: is restoring weight_var and bias_var variables sufficient to implement the prediction? I did the training on the powerful machine with GPU and I would like to copy the model to the less powerful computer without GPU to run predictions. Continue reading...
