imagepypelines.builtin_pipelines.SimpleImageClassifier(neurons=512, dropout=0.5, num_hidden=2, learning_rate=0.01, decay=1e-06, momentum=0.9, batch_size=128, label_type='integer', validation=0.0, num_epochs=1, pca_components=256, pretrained_network='densenet121', pooling_type='avg')[source]

returns a simple image classifier pipeline composed of the following blocks. This pipeline will take in image filenames and return the predicted label

PretrainedNetwork –> PCA –> MultilayerPerceptron

  • neurons (int) – the number of neurons in each of the first and hidden layers
  • dropout (float) – the fraction of neurons dropped out after each layer to mitigate network overfitting
  • num_hidden (int) – number of layers containing ‘neurons’ fully-connected neurons between the first and last layers. This is the parameter to tweak to make the network _deeper_
  • learning_rate (float) – initial learning rate for the SGD optimizer
  • decay (float) – learning rate decay for the SGD optimizer
  • momentum (float) – momentum of the SGD optimizer, this affects the descent rate and oscillation dampening
  • batch_size (int) – number of datums to train on in each batch, larger will improve speed, but increase memory footprint default is 128
  • label_type (string) – the type of labels passed in, must be either ‘categorical’ (one-hot) labels or ‘integer’ labels default is integer
  • validation (float) – the fraction of training data that will be used for validating the model. default is 0.0
  • num_epochs (int) – the number of epochs to train this model for (the number of times the model is trained on the training data). higher usually yields better results but linearly increases training time. default is 1
  • pca_components (int) – the number of dimensions to reduce the feature vector to. default is 256
  • network_name (str) – name of network to extract features from Default is ‘densenet121’
  • pooling_type (str) – the type of pooling to perform on the features, must be one of [‘max’,’avg’]. Default is ‘avg’

a pipeline that will process and classify

input imagery after training

Return type: