arthur/handson-ml3
1
0
Fork 0
mirror of https://github.com/ArthurDanjou/handson-ml3.git synced 2026-01-14 12:14:36 +01:00
Code Issues Packages Projects Releases Wiki Activity

Replace HDF5 with TF format

Browse Source
This commit is contained in:
Aurélien Geron
2022-09-12 11:47:36 +12:00
parent ace7a972b9
commit 7a551f5fb1
2 changed files with 10 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
19_training_and_deploying_at_scale.ipynb
View File

@@ -229,7 +229,7 @@
"model_name = \"my_mnist_model\"\n",
"model_version = \"0001\"\n",
"model_path = Path(model_name) / model_version\n",
"model.save(model_path)"
"model.save(model_path, save_format=\"tf\")"
]
},
{
@@ -706,7 +706,7 @@
"source": [
"model_version = \"0002\"\n",
"model_path = Path(model_name) / model_version\n",
"model.save(model_path)"
"model.save(model_path, save_format=\"tf\")"
]
},
{
@@ -1830,7 +1830,7 @@
"source": [
"# extra code – shows that saving a model does not preserve its distribution\n",
"# strategy\n",
"model.save(\"my_mirrored_model\")\n",
"model.save(\"my_mirrored_model\", save_format=\"tf\")\n",
"model = tf.keras.models.load_model(\"my_mirrored_model\")\n",
"type(model.weights[0])"
]
@@ -2151,10 +2151,10 @@
"model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=10)\n",
"\n",
"if resolver.task_id == 0: # the chief saves the model to the right location\n",
" model.save(\"my_mnist_multiworker_model\")\n",
" model.save(\"my_mnist_multiworker_model\", save_format=\"tf\")\n",
"else:\n",
" tmpdir = tempfile.mkdtemp() # other workers save to a temporary directory\n",
" model.save(tmpdir)\n",
" model.save(tmpdir, save_format=\"tf\")\n",
" tf.io.gfile.rmtree(tmpdir) # and we can delete this directory at the end!"
]
},
@@ -2319,7 +2319,7 @@
"\n",
"model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=10,\n",
" callbacks=callbacks)\n",
"model.save(model_dir)"
"model.save(model_dir, save_format=\"tf\")"
]
},
{
@@ -2471,7 +2471,7 @@
"model = build_model(args)\n",
"history = model.fit(X_train, y_train, validation_data=(X_valid, y_valid),\n",
" epochs=10, callbacks=callbacks)\n",
"model.save(model_dir) # extra code\n",
"model.save(model_dir, save_format=\"tf\") # extra code\n",
"\n",
"import hypertune\n",
"\n",
@@ -2771,7 +2771,7 @@
"if tuner_id == \"chief\":\n",
" best_hp = hyperband_tuner.get_best_hyperparameters()[0]\n",
" best_model = hyperband_tuner.hypermodel.build(best_hp)\n",
" best_model.save(os.getenv(\"AIP_MODEL_DIR\"))"
" best_model.save(os.getenv(\"AIP_MODEL_DIR\"), save_format=\"tf\")"
]
},
{