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")
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()
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()
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_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
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()
from conx import Network
inputs = [[0, 0],
[0, 1],
[1, 0],
[1, 1]]
def xor(inputs):
a = inputs[0]
b = inputs[1]
return [[0.1, 0.9][int((a or b) and not(a and b))]]
net = Network(2, 2, 1)
net.set_inputs(inputs)
net.set_target_function(xor)
net.train()
net.test()
net = Network(2, 2, 2, 1)
net.set_inputs(inputs)
net.set_target_function(xor)
net.train(max_training_epochs=10000)
net.test()
inputs = [[[0, 0], [0, 0]],
[[0, 1], [1, 1]],
[[1, 0], [1, 1]],
[[1, 1], [0, 0]]]
net = Network(2, 10, 2)
net.set_inputs(inputs)
net = Network(784, 100, 1)
inputs = [train_set[0][i] for i in range(len(train_set[0]))]
targets = [[train_set[1][i]/9.0] for i in range(len(train_set[0]))]
inputs = inputs[:100]
targets = targets[:100]
def display_digit(vector):
for r in range(28):
for c in range(28):
v = int(vector[r * 28 + c] * 10)
ch = " .23456789"[v]
print(ch, end="")
print()
net.display_test_input = display_digit
net.set_inputs(list(zip(inputs, targets)))
net.test(2)
net.train(report_rate=1, tolerance=0.05)
net.test(2)
for i in range(100):
output = net.propagate(inputs[i])
target = int(targets[i][0] * 9)
print("target:", target,
"output:", output,
"correct?", int(output * 10) == target)
