Magic
Experiment with any imaginable function using our Magic Object
Wherever you are, whatever strange function you are building up, Innocuous Book experiment.
Magic Object allows you to easily develop and interact with💀
Instantiate
Magic Object before use.mj = MagicObj()
Then, enjoy Innocuous Book Magic power
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get_path(filename, path)
Search the data by
filename from path , if exist, return the data path.Arguments | |
filename | String (name of data with extension which you want to download) |
path | String (directory where data put) |
Example:
data_path = mj.get_path(
filename = "boston_housing.csv",
path = "/home/project/workspace/dataset")
data = pd.read_csv(data_path)
log(**kwargs)
Log metrics then you can choose one as optimal standard.
Do not use
log within a Trainable class.Arguments | |
**kwargs | Arguments to log which must have key |
Example:
mj.log(loss=running_loss, eval_loss=eval_loss)
so u can read the data with that save ur checkpoints no matter what framework api for every single framework log you metrics
torch_get_checkpoint_path(path, epoch)
Return the path of checkpoints during each epoch or whenever you want.
Arguments | |
path | String (directory where you wnat to save checkpoints) |
epoch | integer (number of epochs to save the model as checkpoint. An epoch is an iteration over the entire x and y data provided) |
Example:
mj.torch_get_checkpoint_path(path='/home/project/workspace/results', epoch=epoch)
torch_save(checkpoint, path, epoch)
Save checkpoint during each epoch.
Arguments | |
checkpoint | Dictionary or OrderDictionary (model, weights or anything to save as checkpoint) |
path | String (directory where you wnat to save checkpoints) |
epoch | Integer (number of epochs to save the model as checkpoint. epoch would iterate over the entire x and y data provided) |
Example:
mj.torch_save(
checkpoint = model.state_dict(),
path = '/home/project/workspace/results',
epoch = epoch)
tensor_save(checkpoint, path)
Save checkpoint whenever you want.
Arguments | |
checkpoint | Sequential object (model to save as checkpoint) |
path | String (directory where you wnat to save checkpoints) |
Example:
mj.tensor_save(checkpoint=model, path='/home/project/workspace/results')
callback(metrics, path, frequency, on, filename=None)
Save checkpoint with the opportunity by
on during each epoch and sArguments | |
metrics | Metrics in dictionary to be evaluated by the model during training and testing. Each can be a string (name of a built-in function), function or a tf.keras.metrics.Metric instance |
path | String (directory where you wnat to save checkpoints) |
frequency | Integer or List (if an integer n, checkpoints are saved every n times of each hook. if a list, it specifies the checkpoint frequencies for each hook individually.) |
on | Integer or List (when to trigger checkpoint creations. must be one of the Keras event hooks (less the on_), e.g. "train_start", or "predict_end". defaults to "epoch_end".) |
filename | String (name of checkpoint you want to save) |
Example:
model.fit(
X_train, Y_train,
initial_epoch=1, epochs=epochs, batch_size=16, verbose=0,
callbacks=[m.callback(
metrics={"accuracy":"accuracy"},
filename="modelcheckpoint01",
path="/home/pj/ray_results",
frequency=1,
on="epoch_end")]
)
Last modified 1yr ago