I want to see the variables that are saved in a TensorFlow checkpoint along with their values. How can I find the variable names that are saved in a TensorFlow checkpoint?
I used tf.train.NewCheckpointReader
which is explained here. But, it is not given in the documentation of TensorFlow. Is there any other way?
ModelCheckpoint callback is used in conjunction with training using model. fit() to save a model or weights (in a checkpoint file) at some interval, so the model or weights can be loaded later to continue the training from the state saved.
Example usage:
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file import os checkpoint_path = os.path.join(model_dir, "model.ckpt") # List ALL tensors example output: v0/Adam (DT_FLOAT) [3,3,1,80] print_tensors_in_checkpoint_file(file_name=checkpoint_path, tensor_name='') # List contents of v0 tensor. # Example output: tensor_name: v0 [[[[ 9.27958265e-02 7.40226209e-02 4.52989563e-02 3.15700471e-02 print_tensors_in_checkpoint_file(file_name=checkpoint_path, tensor_name='v0') # List contents of v1 tensor. print_tensors_in_checkpoint_file(file_name=checkpoint_path, tensor_name='v1')
Update: all_tensors
argument was added to print_tensors_in_checkpoint_file
since Tensorflow 0.12.0-rc0 so you may need to add all_tensors=False
or all_tensors=True
if required.
Alternative method:
from tensorflow.python import pywrap_tensorflow import os checkpoint_path = os.path.join(model_dir, "model.ckpt") reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: print("tensor_name: ", key) print(reader.get_tensor(key)) # Remove this is you want to print only variable names
Hope it helps.
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