def __init__(self, cnt):
n = Network()
n.add( Layer('input', 2) )
n.add( Layer('hidden', 3) )
n.add( Layer('output', 1) )
n.connect('input', 'hidden')
n.connect('hidden', 'output')
n.setInputs([[0.0, 0.0],
[0.0, 1.0],
[1.0, 0.0],
[1.0, 1.0]])
n.setOutputs([[0.0],
[1.0],
[1.0],
[0.0]])
n.setVerbosity(0)
n.setTolerance(.4)
n.setLearning(0)
g = n.arrayify()
self.network = n
GA.__init__(self,
Population(cnt, Gene, size=len(g), verbose=1,
min=-10, max=10, maxStep = 1,
imin=-10, imax=10,
elitePercent = .01),
mutationRate=0.05, crossoverRate=0.6,
maxGeneration=400, verbose=1)
def test_dataset():
"""
Load 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.dataset.get("mnist")
assert net is not None
def import_keras_model(model, network_name):
"""
Import a keras model into conx.
"""
from .network import Network
import inspect
import conx
network = Network(network_name)
network.model = model
conx_layers = {
name: layer
for (name, layer) in inspect.getmembers(conx.layers, inspect.isclass)
}
# First, make all of the conx layers:
for layer in model.layers:
clayer_class = conx_layers[layer.__class__.__name__ + "Layer"]
if clayer_class.__name__ == "InputLayerLayer":
clayer = conx.layers.InputLayer(layer.name, None)
#clayer.make_input_layer_k = lambda layer=layer: layer
clayer.shape = None
clayer.params["batch_shape"] = layer.get_config(
)["batch_input_shape"]
#clayer.params = layer.get_config()
clayer.k = clayer.make_input_layer_k()
clayer.keras_layer = clayer.k
else:
clayer = clayer_class(**layer.get_config())
clayer.k = layer
clayer.keras_layer = layer
network.add(clayer)
# Next, connect them up:
for layer_from in model.layers:
for node in layer.outbound_nodes:
network.connect(layer_from, node.outbound_layer.name)
print("connecting:", layer_from, node.outbound_layer.name)
# Connect them all up, and set input banks:
network.connect()
for clayer in network.layers:
clayer.input_names = network.input_bank_order
# Finally, make the internal models:
for clayer in network.layers:
## FIXME: the appropriate inputs:
if clayer.kind() != "input":
clayer.model = keras.models.Model(
inputs=model.layers[0].input,
outputs=clayer.keras_layer.output)
return network
def test_xor1():
"""
Standard XOR.
"""
net = Network("XOR")
net.add(Layer("input", 2))
net.add(Layer("hidden", 5))
net.add(Layer("output", 1))
net.connect("input", "hidden")
net.connect("hidden", "output")
net.compile(error="binary_crossentropy", optimizer="adam")
net.summary()
net.model.summary()
net.dataset.load([[[0, 0], [0]], [[0, 1], [1]], [[1, 0], [1]], [[1, 1],
[0]]])
net.train(epochs=2000, accuracy=1, report_rate=25)
net.test()
net.save_weights("/tmp")
net.load_weights("/tmp")
svg = net.build_svg()
assert net is not None
def test_xor1():
"""
Standard XOR.
"""
net = Network("XOR")
net.add(Layer("input", 2))
net.add(Layer("hidden", 5))
net.add(Layer("output", 1))
net.connect("input", "hidden")
net.connect("hidden", "output")
net.compile(error="binary_crossentropy", optimizer="adam")
net.summary()
net.model.summary()
net.dataset.load([[[0, 0], [0]],
[[0, 1], [1]],
[[1, 0], [1]],
[[1, 1], [0]]])
net.train(epochs=2000, accuracy=1, report_rate=25, plot=False)
net.evaluate(show=True)
net.save_weights("/tmp")
net.load_weights("/tmp")
svg = net.to_svg()
assert net is not None
def test_images():
net = Network("MNIST")
net.dataset.get("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_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_xor2():
"""
Two inputs, two outputs.
"""
net = Network("XOR2")
net.add(Layer("input1", shape=1))
net.add(Layer("input2", shape=1))
net.add(Layer("hidden1", shape=2, activation="sigmoid"))
net.add(Layer("hidden2", shape=2, activation="sigmoid"))
net.add(Layer("shared-hidden", shape=2, activation="sigmoid"))
net.add(Layer("output1", shape=1, activation="sigmoid"))
net.add(Layer("output2", shape=1, activation="sigmoid"))
net.connect("input1", "hidden1")
net.connect("input2", "hidden2")
net.connect("hidden1", "shared-hidden")
net.connect("hidden2", "shared-hidden")
net.connect("shared-hidden", "output1")
net.connect("shared-hidden", "output2")
net.compile(error='mean_squared_error',
optimizer=SGD(lr=0.3, momentum=0.9))
net.dataset.load([
([[0],[0]], [[0],[0]]),
([[0],[1]], [[1],[1]]),
([[1],[0]], [[1],[1]]),
([[1],[1]], [[0],[0]])
])
net.train(2000, report_rate=10, accuracy=1, plot=False)
net.test()
net.propagate_to("shared-hidden", [[1], [1]])
net.propagate_to("output1", [[1], [1]])
net.propagate_to("output2", [[1], [1]])
net.propagate_to("hidden1", [[1], [1]])
net.propagate_to("hidden2", [[1], [1]])
net.propagate_to("output1", [[1], [1]])
net.propagate_to("output2", [[1], [1]])
net.save_weights("/tmp")
net.load_weights("/tmp")
net.test()
svg = net.to_svg()
assert net is not None
def test_xor2():
"""
Two inputs, two outputs.
"""
net = Network("XOR2")
net.add(Layer("input1", shape=1))
net.add(Layer("input2", shape=1))
net.add(Layer("hidden1", shape=2, activation="sigmoid"))
net.add(Layer("hidden2", shape=2, activation="sigmoid"))
net.add(Layer("shared-hidden", shape=2, activation="sigmoid"))
net.add(Layer("output1", shape=1, activation="sigmoid"))
net.add(Layer("output2", shape=1, activation="sigmoid"))
net.connect("input1", "hidden1")
net.connect("input2", "hidden2")
net.connect("hidden1", "shared-hidden")
net.connect("hidden2", "shared-hidden")
net.connect("shared-hidden", "output1")
net.connect("shared-hidden", "output2")
net.compile(error='mean_squared_error',
optimizer=SGD(lr=0.3, momentum=0.9))
net.dataset.load([
([[0],[0]], [[0],[0]]),
([[0],[1]], [[1],[1]]),
([[1],[0]], [[1],[1]]),
([[1],[1]], [[0],[0]])
])
net.train(2000, report_rate=10, accuracy=1, plot=False)
net.evaluate(show=True)
net.propagate_to("shared-hidden", [[1], [1]])
net.propagate_to("output1", [[1], [1]])
net.propagate_to("output2", [[1], [1]])
net.propagate_to("hidden1", [[1], [1]])
net.propagate_to("hidden2", [[1], [1]])
net.propagate_to("output1", [[1], [1]])
net.propagate_to("output2", [[1], [1]])
net.save_weights("/tmp")
net.load_weights("/tmp")
net.evaluate(show=True)
svg = net.to_svg()
assert net is not None
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_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.dataset.get("cifar10")
net.dashboard()
net.dataset.slice(10)
net.dataset.shuffle()
net.dataset.split(.5)
net.train()
net.propagate(net.dataset.inputs[0])
from conx import Network, Layer, SGD
net = Network("XOR2")
net.add(Layer("input1", 2))
net.add(Layer("input2", 2))
net.add(Layer("hidden1", 2, activation="sigmoid"))
net.add(Layer("hidden2", 2, activation="sigmoid"))
net.add(Layer("shared-hidden", 2, activation="sigmoid"))
net.add(Layer("output1", 2, activation="sigmoid"))
net.add(Layer("output2", 2, activation="sigmoid"))
net.connect("input1", "hidden1")
net.connect("input2", "hidden2")
net.connect("hidden1", "shared-hidden")
net.connect("hidden2", "shared-hidden")
net.connect("shared-hidden", "output1")
net.connect("shared-hidden", "output2")
net.compile(loss='mean_squared_error', optimizer=SGD(lr=0.3, momentum=0.9))
ds = [([[0, 0], [0, 0]], [[0, 0], [0, 0]]), ([[0, 0], [1, 1]], [[1, 1], [1,
1]]),
([[1, 1], [0, 0]], [[1, 1], [1, 1]]), ([[1, 1], [1, 1]], [[0, 0], [0,
0]])]
net.dataset.load(ds)
net.train(2000, report_rate=10, accuracy=1)
net.test()
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()