"The above code stores the weights to a collection of [checkpoint](https://www.tensorflow.org/guide/saved_model#save_and_restore_variables)-formatted files that contain only the trained weights in a binary format. Checkpoints contain:\n",
"The above code stores the weights to a collection of [checkpoint](https://www.tensorflow.org/guide/saved_model#save_and_restore_variables)-formatted files that contain only the trained weights in a binary format. Checkpoints contain:\n",
"\n",
"* One or more shards that contain your model's weights. \n",
"* One or more shards that contain your model's weights. \n",
"* An index file that indicates which weights are stored in a which shard. \n",
"* An index file that indicates which weights are stored in which shard. \n",
"\n",
"\n",
"If you are only training a model on a single machine, you'll have one shard with the suffix: `.data-00000-of-00001`"
"If you are only training a model on a single machine, you'll have one shard with the suffix: `.data-00000-of-00001`"
]
]
...
@@ -739,7 +740,7 @@
...
@@ -739,7 +740,7 @@
"* The model's configuration(architecture)\n",
"* The model's configuration(architecture)\n",
"* The optimizer configuration\n",
"* The optimizer configuration\n",
"\n",
"\n",
"Keras saves models by inspecting the architecture. Currently, it is not able to save TensorFlow optimizers (from `tf.train`). When using those you will need to re-compile the model after loading, and you will loose the state of the optimizer.\n"
"Keras saves models by inspecting the architecture. Currently, it is not able to save TensorFlow optimizers (from `tf.train`). When using those you will need to re-compile the model after loading, and you will lose the state of the optimizer.\n"