.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_example_5_pytorch_worker.py: Worker for Example 5 - PyTorch ============================== In this example implements a small CNN in PyTorch to train it on MNIST. The configuration space shows the most common types of hyperparameters and even contains conditional dependencies. In this example implements a small CNN in Keras to train it on MNIST. The configuration space shows the most common types of hyperparameters and even contains conditional dependencies. We'll optimise the following hyperparameters: +-------------------------+----------------+-----------------+------------------------+ | Parameter Name | Parameter type | Range/Choices | Comment | +=========================+================+=================+========================+ | Learning rate | float | [1e-6, 1e-2] | varied logarithmically | +-------------------------+----------------+-----------------+------------------------+ | Optimizer | categorical | {Adam, SGD } | discrete choice | +-------------------------+----------------+-----------------+------------------------+ | SGD momentum | float | [0, 0.99] | only active if | | | | | optimizer == SGD | +-------------------------+----------------+-----------------+------------------------+ | Number of conv layers | integer | [1,3] | can only take integer | | | | | values 1, 2, or 3 | +-------------------------+----------------+-----------------+------------------------+ | Number of filters in | integer | [4, 64] | logarithmically varied | | the first conf layer | | | integer values | +-------------------------+----------------+-----------------+------------------------+ | Number of filters in | integer | [4, 64] | only active if number | | the second conf layer | | | of layers >= 2 | +-------------------------+----------------+-----------------+------------------------+ | Number of filters in | integer | [4, 64] | only active if number | | the third conf layer | | | of layers == 3 | +-------------------------+----------------+-----------------+------------------------+ | Dropout rate | float | [0, 0.9] | standard continuous | | | | | parameter | +-------------------------+----------------+-----------------+------------------------+ | Number of hidden units | integer | [8,256] | logarithmically varied | | in fully connected layer| | | integer values | +-------------------------+----------------+-----------------+------------------------+ Please refer to the compute method below to see how those are defined using the ConfigSpace package. The network does not achieve stellar performance when a random configuration is samples, but a few iterations should yield an accuracy of >90%. To speed up training, only 8192 images are used for training, 1024 for validation. The purpose is not to achieve state of the art on MNIST, but to show how to use PyTorch inside HpBandSter, and to demonstrate a more complicated search space. .. code-block:: python try: import torch import torch.utils.data import torch.nn as nn import torch.nn.functional as F except: raise ImportError("For this example you need to install pytorch.") try: import torchvision import torchvision.transforms as transforms except: raise ImportError("For this example you need to install pytorch-vision.") import ConfigSpace as CS import ConfigSpace.hyperparameters as CSH from hpbandster.core.worker import Worker import logging logging.basicConfig(level=logging.DEBUG) class PyTorchWorker(Worker): def __init__(self, N_train = 8192, N_valid = 1024, **kwargs): super().__init__(**kwargs) batch_size = 64 # Load the MNIST Data here train_dataset = torchvision.datasets.MNIST(root='../../data', train=True, transform=transforms.ToTensor(), download=True) test_dataset = torchvision.datasets.MNIST(root='../../data', train=False, transform=transforms.ToTensor()) train_sampler = torch.utils.data.sampler.SubsetRandomSampler(range(N_train)) validation_sampler = torch.utils.data.sampler.SubsetRandomSampler(range(N_train, N_train+N_valid)) self.train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, sampler=train_sampler) self.validation_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=1024, sampler=validation_sampler) self.test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=1024, shuffle=False) def compute(self, config, budget, working_directory, *args, **kwargs): """ Simple example for a compute function using a feed forward network. It is trained on the MNIST dataset. The input parameter "config" (dictionary) contains the sampled configurations passed by the bohb optimizer """ # device = torch.device('cpu') model = MNISTConvNet(num_conv_layers=config['num_conv_layers'], num_filters_1=config['num_filters_1'], num_filters_2=config['num_filters_2'] if 'num_filters_2' in config else None, num_filters_3=config['num_filters_3'] if 'num_filters_3' in config else None, dropout_rate=config['dropout_rate'], num_fc_units=config['num_fc_units'], kernel_size=3 ) criterion = torch.nn.CrossEntropyLoss() if config['optimizer'] == 'Adam': optimizer = torch.optim.Adam(model.parameters(), lr=config['lr']) else: optimizer = torch.optim.SGD(model.parameters(), lr=config['lr'], momentum=config['sgd_momentum']) for epoch in range(int(budget)): loss = 0 model.train() for i, (x, y) in enumerate(self.train_loader): optimizer.zero_grad() output = model(x) loss = F.nll_loss(output, y) loss.backward() optimizer.step() train_accuracy = self.evaluate_accuracy(model, self.train_loader) validation_accuracy = self.evaluate_accuracy(model, self.validation_loader) test_accuracy = self.evaluate_accuracy(model, self.test_loader) return ({ 'loss': 1-validation_accuracy, # remember: HpBandSter always minimizes! 'info': { 'test accuracy': test_accuracy, 'train accuracy': train_accuracy, 'validation accuracy': validation_accuracy, 'number of parameters': model.number_of_parameters(), } }) def evaluate_accuracy(self, model, data_loader): model.eval() correct=0 with torch.no_grad(): for x, y in data_loader: output = model(x) #test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability correct += pred.eq(y.view_as(pred)).sum().item() #import pdb; pdb.set_trace() accuracy = correct/len(data_loader.sampler) return(accuracy) @staticmethod def get_configspace(): """ It builds the configuration space with the needed hyperparameters. It is easily possible to implement different types of hyperparameters. Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter. :return: ConfigurationsSpace-Object """ cs = CS.ConfigurationSpace() lr = CSH.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value='1e-2', log=True) # For demonstration purposes, we add different optimizers as categorical hyperparameters. # To show how to use conditional hyperparameters with ConfigSpace, we'll add the optimizers 'Adam' and 'SGD'. # SGD has a different parameter 'momentum'. optimizer = CSH.CategoricalHyperparameter('optimizer', ['Adam', 'SGD']) sgd_momentum = CSH.UniformFloatHyperparameter('sgd_momentum', lower=0.0, upper=0.99, default_value=0.9, log=False) cs.add_hyperparameters([lr, optimizer, sgd_momentum]) # The hyperparameter sgd_momentum will be used,if the configuration # contains 'SGD' as optimizer. cond = CS.EqualsCondition(sgd_momentum, optimizer, 'SGD') cs.add_condition(cond) num_conv_layers = CSH.UniformIntegerHyperparameter('num_conv_layers', lower=1, upper=3, default_value=2) num_filters_1 = CSH.UniformIntegerHyperparameter('num_filters_1', lower=4, upper=64, default_value=16, log=True) num_filters_2 = CSH.UniformIntegerHyperparameter('num_filters_2', lower=4, upper=64, default_value=16, log=True) num_filters_3 = CSH.UniformIntegerHyperparameter('num_filters_3', lower=4, upper=64, default_value=16, log=True) cs.add_hyperparameters([num_conv_layers, num_filters_1, num_filters_2, num_filters_3]) # You can also use inequality conditions: cond = CS.GreaterThanCondition(num_filters_2, num_conv_layers, 1) cs.add_condition(cond) cond = CS.GreaterThanCondition(num_filters_3, num_conv_layers, 2) cs.add_condition(cond) dropout_rate = CSH.UniformFloatHyperparameter('dropout_rate', lower=0.0, upper=0.9, default_value=0.5, log=False) num_fc_units = CSH.UniformIntegerHyperparameter('num_fc_units', lower=8, upper=256, default_value=32, log=True) cs.add_hyperparameters([dropout_rate, num_fc_units]) return cs class MNISTConvNet(torch.nn.Module): def __init__(self, num_conv_layers, num_filters_1, num_filters_2, num_filters_3, dropout_rate, num_fc_units, kernel_size): super().__init__() self.conv1 = nn.Conv2d(1, num_filters_1, kernel_size=kernel_size) self.conv2 = None self.conv3 = None output_size = (28-kernel_size + 1)//2 num_output_filters = num_filters_1 if num_conv_layers > 1: self.conv2 = nn.Conv2d(num_filters_1, num_filters_2, kernel_size=kernel_size) num_output_filters = num_filters_2 output_size = (output_size - kernel_size + 1)//2 if num_conv_layers > 2: self.conv3 = nn.Conv2d(num_filters_2, num_filters_3, kernel_size=kernel_size) num_output_filters = num_filters_3 output_size = (output_size - kernel_size + 1)//2 self.dropout = nn.Dropout(p = dropout_rate) self.conv_output_size = num_output_filters*output_size*output_size self.fc1 = nn.Linear(self.conv_output_size, num_fc_units) self.fc2 = nn.Linear(num_fc_units, 10) def forward(self, x): # switched order of pooling and relu compared to the original example # to make it identical to the keras worker # seems to also give better accuracies x = F.max_pool2d(F.relu(self.conv1(x)), 2) if not self.conv2 is None: x = F.max_pool2d(F.relu(self.conv2(x)), 2) if not self.conv3 is None: x = F.max_pool2d(F.relu(self.conv3(x)), 2) x = self.dropout(x) x = x.view(-1, self.conv_output_size) x = F.relu(self.fc1(x)) x = self.dropout(x) x = self.fc2(x) return F.log_softmax(x, dim=1) def number_of_parameters(self): return(sum(p.numel() for p in self.parameters() if p.requires_grad)) if __name__ == "__main__": worker = KerasWorker(run_id='0') cs = worker.get_configspace() config = cs.sample_configuration().get_dictionary() print(config) res = worker.compute(config=config, budget=2, working_directory='.') print(res) **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_auto_examples_example_5_pytorch_worker.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: example_5_pytorch_worker.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: example_5_pytorch_worker.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_