def main_okapi(): from OkapiV2.Core import Model, Branch from OkapiV2.Layers.Basic import FullyConnected, Dropout from OkapiV2.Layers.Activations import ActivationLayer, PReLULayer from OkapiV2.Layers.Convolutional import Convolutional, MaxPooling from OkapiV2 import Activations, Datasets from OkapiV2 import Optimizers X_train, y_train, X_val, y_val, X_test, y_test = Datasets.load_mnist() tree = Branch() tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad)) # tree.add_layer(BatchNorm()) tree.add_layer(PReLULayer()) tree.add_layer(MaxPooling(pool_size, pool_size)) tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad)) # tree.add_layer(BatchNorm()) tree.add_layer(PReLULayer()) tree.add_layer(MaxPooling(pool_size, pool_size)) tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad)) # tree.add_layer(BatchNorm()) tree.add_layer(PReLULayer()) tree.add_layer(MaxPooling(pool_size, pool_size)) '''tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad)) # tree.add_layer(BatchNorm()) tree.add_layer(PReLULayer()) tree.add_layer(MaxPooling(pool_size, pool_size))''' '''tree.add_layer(Dropout(0.25)) tree.add_layer(FullyConnected((h_size, 1, 1, 1))) tree.add_layer(PReLULayer()) # tree.add_layer(BatchNorm())''' tree.add_layer(Dropout(0.5)) tree.add_layer(FullyConnected()) tree.add_layer(ActivationLayer(Activations.alt_softmax)) tree.add_input(X_train) model = Model() model.set_tree(tree) model.set_optimizer(Optimizers.RMSprop(learning_rate=learning_rate, momentum=momentum)) model.train([X_train], y_train, num_epochs=num_epochs, batch_size=batch_size) # ok.save_model(model, 'okapi_mnist.pk') okapi_accuracy = model.get_accuracy([X_test], y_test) print("Test Accuracy: {}%" .format(round(okapi_accuracy, 2))) return okapi_accuracy
from OkapiV2.Core import Model, Branch from OkapiV2.Layers.Basic import FullyConnected from OkapiV2.Layers.Activations import ActivationLayer, PReLULayer from OkapiV2 import Activations, Datasets, Optimizers x_train, y_train, x_val, y_val, x_test, y_test = Datasets.load_mnist() tree = Branch() tree.add_layer(FullyConnected((512, 1, 1, 1))) tree.add_layer(PReLULayer()) tree.add_layer(FullyConnected((512, 1, 1, 1))) tree.add_layer(PReLULayer()) tree.add_layer(FullyConnected((512, 1, 1, 1))) tree.add_layer(PReLULayer()) tree.add_layer(FullyConnected()) tree.add_layer(ActivationLayer(Activations.softmax)) tree.add_input(x_train) model = Model() model.set_tree(tree) model.set_optimizer(Optimizers.RMSprop(learning_rate=0.00005)) index = 60000 model.train([x_train[:index, :, :, :]], y_train[:index, :], 24) accuracy = model.get_accuracy([x_train[:index, :, :, :]], y_train[:index]) print("Accuracy: {}%".format(accuracy)) test_accuracy = model.get_accuracy([x_test], y_test) print("Test accuracy: {}%".format(test_accuracy))