def test_dataset():
"""
Load Virtual MNIST dataset after network creation.
"""
net = Network("MNIST")
net.add(Layer("input", shape=784, vshape=(28, 28), colormap="hot", minmax=(0,1)))
net.add(Layer("hidden1", shape=512, vshape=(16,32), activation='relu', dropout=0.2))
net.add(Layer("hidden2", shape=512, vshape=(16,32), activation='relu', dropout=0.2))
net.add(Layer("output", shape=10, activation='softmax'))
net.connect('input', 'hidden1')
net.connect('hidden1', 'hidden2')
net.connect('hidden2', 'output')
net.compile(optimizer="adam", error="binary_crossentropy")
net.get_dataset("mnist")
assert net is not None
net.dataset.clear()
def test_images():
net = Network("MNIST")
net.get_dataset("mnist")
assert net.dataset.inputs.shape == [(28,28,1)]
net.add(Layer("input", shape=(28, 28, 1), colormap="hot", minmax=(0,1)))
net.add(FlattenLayer("flatten"))
net.add(Layer("hidden1", shape=512, vshape=(16,32), activation='relu', dropout=0.2))
net.add(Layer("hidden2", shape=512, vshape=(16,32), activation='relu', dropout=0.2))
net.add(Layer("output", shape=10, activation='softmax'))
net.connect('input', 'flatten')
net.connect('flatten', 'hidden1')
net.connect('hidden1', 'hidden2')
net.connect('hidden2', 'output')
net.compile(optimizer="adam", error="binary_crossentropy")
svg = net.to_svg()
assert svg is not None
net.dataset.clear()
def test_cifar10():
"""
Test the cifar10 API and training.
"""
from conx import Network, Layer, Conv2DLayer, MaxPool2DLayer, FlattenLayer
batch_size = 32
num_classes = 10
epochs = 200
data_augmentation = True
num_predictions = 20
net = Network("CIRAR10")
net.add(Layer("input", (32, 32, 3)))
net.add(Conv2DLayer("conv1", 32, (3, 3), padding='same', activation='relu'))
net.add(Conv2DLayer("conv2", 32, (3, 3), activation='relu'))
net.add(MaxPool2DLayer("pool1", pool_size=(2, 2), dropout=0.25))
net.add(Conv2DLayer("conv3", 64, (3, 3), padding='same', activation='relu'))
net.add(Conv2DLayer("conv4", 64, (3, 3), activation='relu'))
net.add(MaxPool2DLayer("pool2", pool_size=(2, 2), dropout=0.25))
net.add(FlattenLayer("flatten"))
net.add(Layer("hidden1", 512, activation='relu', vshape=(16, 32), dropout=0.5))
net.add(Layer("output", num_classes, activation='softmax'))
net.connect()
# initiate RMSprop optimizer
opt = RMSprop(lr=0.0001, decay=1e-6)
net.compile(error='categorical_crossentropy',
optimizer=opt)
net.get_dataset("cifar10")
widget = net.dashboard()
widget.goto("begin")
widget.goto("next")
widget.goto("end")
widget.goto("prev")
widget.prop_one()
net.dataset.slice(10)
net.dataset.shuffle()
net.dataset.split(.5)
net.train(plot=False)
net.propagate(net.dataset.inputs[0])
net.dataset.clear()