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))
def main_keras():
import numpy as np
# from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
# from keras.utils import np_utils
from keras.layers.advanced_activations import PReLU
from keras.layers.normalization import BatchNormalization
from keras.optimizers import RMSprop
from OkapiV2 import Datasets
'''(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 1, img_size, img_size)
X_test = X_test.reshape(X_test.shape[0], 1, img_size, img_size)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, num_classes)
Y_test = np_utils.to_categorical(y_test, num_classes)'''
X_train, y_train, X_val, y_val, X_test, y_test = Datasets.load_mnist()
model = Sequential()
model.add(Convolution2D(num_filters, filter_size, filter_size,
border_mode='same',
input_shape=(1, img_size, img_size)))
# model.add(BatchNormalization())
model.add(PReLU())
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))
model.add(Convolution2D(num_filters, filter_size, filter_size,
border_mode='same'))
# model.add(BatchNormalization())
model.add(PReLU())
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))
model.add(Convolution2D(num_filters, filter_size, filter_size,
border_mode='same'))
# model.add(BatchNormalization())
model.add(PReLU())
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))
'''model.add(Convolution2D(num_filters, filter_size, filter_size,
border_mode='same'))
# model.add(BatchNormalization())
model.add(PReLU())
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))'''
'''model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(h_size))
model.add(PReLU())
# model.add(BatchNormalization())'''
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
rmsprop = RMSprop(lr=learning_rate, rho=momentum)
model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=num_epochs)
score = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0)
keras_accuracy = score[1]
print('Test accuracy:', keras_accuracy)
return keras_accuracy
