Sequential Classification Task¶

• Sequential classification of a tabular MNIST dataset (Task 3573) using a simple neural network.

In [ ]:

# openml imports
import openml
import openml_pytorch as op
from openml_pytorch.callbacks import TestCallback
from openml_pytorch.metrics import accuracy

# pytorch imports
import torch

# other imports
import logging
import warnings

# set up logging
openml.config.logger.setLevel(logging.DEBUG)
openml_pytorch.config.logger.setLevel(logging.DEBUG)
warnings.simplefilter(action='ignore')

# openml imports import openml import openml_pytorch as op from openml_pytorch.callbacks import TestCallback from openml_pytorch.metrics import accuracy # pytorch imports import torch # other imports import logging import warnings # set up logging openml.config.logger.setLevel(logging.DEBUG) openml_pytorch.config.logger.setLevel(logging.DEBUG) warnings.simplefilter(action='ignore')

Data¶

Configure the Data Module and Choose a Task¶

• Make sure the data is present in the file_dir directory, and the filename_col is correctly set along with this column correctly pointing to where your data is stored.

In [ ]:

data_module = op.OpenMLDataModule(
    type_of_data="dataframe",
    filename_col="class",
    target_mode="categorical",
)

# Download the OpenML task for the mnist 784 dataset.
task = openml.tasks.get_task(3573)

data_module = op.OpenMLDataModule( type_of_data="dataframe", filename_col="class", target_mode="categorical", ) # Download the OpenML task for the mnist 784 dataset. task = openml.tasks.get_task(3573)

Model¶

In [ ]:

############################################################################
# Define a sequential network that does the initial image reshaping
# and normalization model.
processing_net = torch.nn.Sequential(
    op.layers.Functional(function=torch.Tensor.reshape,
                                                shape=(-1, 1, 28, 28)),
    torch.nn.BatchNorm2d(num_features=1)
)
############################################################################

############################################################################
# Define a sequential network that does the extracts the features from the
# image.
features_net = torch.nn.Sequential(
    torch.nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5),
    torch.nn.LeakyReLU(),
    torch.nn.MaxPool2d(kernel_size=2),
    torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5),
    torch.nn.LeakyReLU(),
    torch.nn.MaxPool2d(kernel_size=2),
)
############################################################################

############################################################################
# Define a sequential network that flattens the features and compiles the
# results into probabilities for each digit.
results_net = torch.nn.Sequential(
    op.layers.Functional(function=torch.Tensor.reshape,
                                                shape=(-1, 4 * 4 * 64)),
    torch.nn.Linear(in_features=4 * 4 * 64, out_features=256),
    torch.nn.LeakyReLU(),
    torch.nn.Dropout(),
    torch.nn.Linear(in_features=256, out_features=10),
)
############################################################################
# openml.config.apikey = 'key'

############################################################################
# The main network, composed of the above specified networks.
model = torch.nn.Sequential(
    processing_net,
    features_net,
    results_net
)
############################################################################

############################################################################ # Define a sequential network that does the initial image reshaping # and normalization model. processing_net = torch.nn.Sequential( op.layers.Functional(function=torch.Tensor.reshape, shape=(-1, 1, 28, 28)), torch.nn.BatchNorm2d(num_features=1) ) ############################################################################ ############################################################################ # Define a sequential network that does the extracts the features from the # image. features_net = torch.nn.Sequential( torch.nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5), torch.nn.LeakyReLU(), torch.nn.MaxPool2d(kernel_size=2), torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5), torch.nn.LeakyReLU(), torch.nn.MaxPool2d(kernel_size=2), ) ############################################################################ ############################################################################ # Define a sequential network that flattens the features and compiles the # results into probabilities for each digit. results_net = torch.nn.Sequential( op.layers.Functional(function=torch.Tensor.reshape, shape=(-1, 4 * 4 * 64)), torch.nn.Linear(in_features=4 * 4 * 64, out_features=256), torch.nn.LeakyReLU(), torch.nn.Dropout(), torch.nn.Linear(in_features=256, out_features=10), ) ############################################################################ # openml.config.apikey = 'key' ############################################################################ # The main network, composed of the above specified networks. model = torch.nn.Sequential( processing_net, features_net, results_net ) ############################################################################

Train your model on the data¶

• Note that by default, OpenML runs a 10 fold cross validation on the data. You cannot change this for now.

In [ ]:

trainer = op.OpenMLTrainerModule(
    experiment_name= "MNIST",
    data_module=data_module,
    verbose=True,
    epoch_count=1,
    metrics= [accuracy],
    # remove the TestCallback when you are done testing your pipeline. Having it here will make the pipeline run for a very short time.
    callbacks=[
        TestCallback,
    ],
    opt = torch.optim.Adam,
)
op.config.trainer = trainer
run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

trainer = op.OpenMLTrainerModule( experiment_name= "MNIST", data_module=data_module, verbose=True, epoch_count=1, metrics= [accuracy], # remove the TestCallback when you are done testing your pipeline. Having it here will make the pipeline run for a very short time. callbacks=[ TestCallback, ], opt = torch.optim.Adam, ) op.config.trainer = trainer run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

View information about your run¶

Learning rate and loss plot¶

In [ ]:

trainer.plot_loss()

trainer.plot_loss()

In [ ]:

trainer.plot_lr()

trainer.plot_lr()

Class labels¶

In [ ]:

trainer.model_classes

trainer.model_classes

Model Vizualization¶

• Sometimes you may want to visualize the model. You can either use netron or tensorboard for this purpose.

Netron¶

In [ ]:

trainer.export_to_netron()

trainer.export_to_netron()

Tensorboard¶

• By default, openml will log the tensorboard logs in the tensorboard_logs directory. You can view the logs by running tensorboard --logdir tensorboard_logs in the terminal.

Publish your model to OpenML¶

• This is Optional, but publishing your model to OpenML will allow you to track your experiments and compare them with others.
• Make sure to set your apikey first.
  • You can find your apikey on your OpenML account page.

In [ ]:

openml.config.apikey = ''
run = op.add_experiment_info_to_run(run=run, trainer=trainer) 
run.publish()

openml.config.apikey = '' run = op.add_experiment_info_to_run(run=run, trainer=trainer) run.publish()