class Network(object):
def __init__(self):
self.layers = []
self.activation = Softmax()
self.init()
def init(self):
for layer in self.layers:
layer.init()
def predict(self, x):
_ = x
for layer in self.layers:
_ = layer.forward(_)
self.y = self.activation.forward(_)
return self.y
def train(self, x, target):
y = self.predict(x)
_ = self.activation.backward(y, target)
for layer in reversed(self.layers):
_ = layer.backward(_)
layer.update()
return self.activation.loss(y, target)
def dump_params(self):
odict = {}
odict["layers"] = [(l.W, l.b) for l in self.layers]
return odict
def load_params(self, idict):
for l, p in zip(self.layers, idict["layers"]):
l.W, l.b = p
def __test(self):
"""
>>> from layers import Fullconnect
>>> from nonlinears import ReLU, Tanh
>>> from activations import Softmax, Sigmoid, Identity
>>> from updaters import GradientDescent, NotUpdate
>>>
>>> learning_rate = 0.01
>>> np.random.seed(0xC0FFEE)
>>>
>>> # Multiclass classification
>>> n = Network()
>>> n.layers.append( Fullconnect(2, 10, ReLU.function, ReLU.derivative, updater=GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 2, updater=GradientDescent(learning_rate)) )
>>> n.activation = Softmax()
>>>
>>> for epoch in range(0, 20):
... loss = n.train( x = np.array([ [1, 2, 1, 2, 5, 6, 5, 6],
... [5, 4, 4, 5, 1, 2, 2, 1]]).T,
... target = np.array([ [1, 1, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 1, 1, 1, 1]]).T )
... if epoch%5 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:6.64
epoch:0005 loss:0.65
epoch:0010 loss:0.36
epoch:0015 loss:0.25
>>>
>>> y = n.predict( np.array( [[1, 6, 3], [5, 1, 4]] ).T )
>>> print ['%.2f'%_ for _ in y[0]]
['0.99', '0.01']
>>> [_ for _ in np.argmax(y, -1)]
[0, 1, 0]
>>>
>>> # Multiple-class classification
>>> n = Network()
>>> n.layers.append( Fullconnect(2, 10, ReLU.function, ReLU.derivative, updater=GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 2, updater=GradientDescent(learning_rate)) )
>>> n.activation = Sigmoid()
>>>
>>> for epoch in range(0, 20):
... loss = n.train( x = np.array([ [1, 2, 1, 2, 4, 5, 4, 5, 5, 6, 5, 6],
... [5, 4, 4, 5, 5, 4, 5, 4, 1, 2, 2, 1]]).T,
... target = np.array([ [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]).T )
... if epoch%5 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:29.39
epoch:0005 loss:4.78
epoch:0010 loss:3.38
epoch:0015 loss:2.64
>>>
>>> y = n.predict( np.array( [[5, 6, 3], [4, 1, 4]] ).T )
>>> print ['%.2f'%_ for _ in y[0]]
['0.86', '0.96']
>>>
>>> # Regression
>>> n = Network()
>>> n.layers.append( Fullconnect(2, 10, ReLU.function, ReLU.derivative, updater=GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 2, updater=GradientDescent(learning_rate)) )
>>> n.activation = Identity()
>>>
>>> for epoch in range(0, 20):
... loss = n.train( x = np.array([ [1, 2, 1, 2, 5, 6, 5, 6],
... [5, 4, 4, 5, 1, 2, 2, 1]]).T,
... target = np.array([ [1, 1, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 1, 1, 1, 1]]).T )
... if epoch%5 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:52.82
epoch:0005 loss:1.81
epoch:0010 loss:1.26
epoch:0015 loss:0.89
>>>
>>> y = n.predict( np.array( [[1, 6, 5], [5, 1, 4]] ).T )
>>> print ['%.2f'%_ for _ in y[0]]
['1.19', '-0.00']
>>>
>>> # Auto-encoder
>>> n = Network()
>>> n.layers.append( Fullconnect( 2, 10, Tanh.function, Tanh.derivative, GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 10, Tanh.function, Tanh.derivative, GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 10, updater=NotUpdate()) )
>>> n.layers.append( Fullconnect(10, 2, updater=NotUpdate()) )
>>> n.activation = Identity()
>>>
>>> # for auto-encoder (weight share)
>>> n.layers[2].W = n.layers[1].W.T
>>> n.layers[3].W = n.layers[0].W.T
>>>
>>> x = np.array( [[1, 2, 1, 2, 5, 6, 5, 6, 5, 6, 5, 6],
... [5, 4, 4, 5, 5, 4, 5, 4, 1, 2, 2, 1]] ).T
>>>
>>> for epoch in range(0, 301):
... loss = n.train( x=x, target=x )
... if epoch%100 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:98.38
epoch:0100 loss:9.83
epoch:0200 loss:1.79
epoch:0300 loss:1.82
"""
pass
class Network(object):
def __init__(self):
self.layers = []
self.activation = Softmax()
self.init()
def init(self):
for layer in self.layers:
layer.init()
def predict(self, x):
_ = x
for layer in self.layers:
_ = layer.forward(_)
self.y = self.activation.forward(_)
return self.y
def train(self, x, target):
y = self.predict(x)
_ = self.activation.backward(y, target)
for layer in reversed(self.layers):
_ = layer.backward(_)
layer.update()
return self.activation.loss(y, target)
def dump_params(self):
odict = {}
odict['layers'] = [(l.W, l.b) for l in self.layers]
return odict
def load_params(self, idict):
for l, p in zip(self.layers, idict['layers']):
l.W, l.b = p
def __test(self):
'''
>>> from layers import Fullconnect
>>> from nonlinears import ReLU, Tanh
>>> from activations import Softmax, Sigmoid, Identity
>>> from updaters import GradientDescent, NotUpdate
>>>
>>> learning_rate = 0.01
>>> np.random.seed(0xC0FFEE)
>>>
>>> # Multiclass classification
>>> n = Network()
>>> n.layers.append( Fullconnect(2, 10, ReLU.function, ReLU.derivative, updater=GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 2, updater=GradientDescent(learning_rate)) )
>>> n.activation = Softmax()
>>>
>>> for epoch in range(0, 20):
... loss = n.train( x = np.array([ [1, 2, 1, 2, 5, 6, 5, 6],
... [5, 4, 4, 5, 1, 2, 2, 1]]).T,
... target = np.array([ [1, 1, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 1, 1, 1, 1]]).T )
... if epoch%5 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:6.64
epoch:0005 loss:0.65
epoch:0010 loss:0.36
epoch:0015 loss:0.25
>>>
>>> y = n.predict( np.array( [[1, 6, 3], [5, 1, 4]] ).T )
>>> print ['%.2f'%_ for _ in y[0]]
['0.99', '0.01']
>>> [_ for _ in np.argmax(y, -1)]
[0, 1, 0]
>>>
>>> # Multiple-class classification
>>> n = Network()
>>> n.layers.append( Fullconnect(2, 10, ReLU.function, ReLU.derivative, updater=GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 2, updater=GradientDescent(learning_rate)) )
>>> n.activation = Sigmoid()
>>>
>>> for epoch in range(0, 20):
... loss = n.train( x = np.array([ [1, 2, 1, 2, 4, 5, 4, 5, 5, 6, 5, 6],
... [5, 4, 4, 5, 5, 4, 5, 4, 1, 2, 2, 1]]).T,
... target = np.array([ [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]).T )
... if epoch%5 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:29.39
epoch:0005 loss:4.78
epoch:0010 loss:3.38
epoch:0015 loss:2.64
>>>
>>> y = n.predict( np.array( [[5, 6, 3], [4, 1, 4]] ).T )
>>> print ['%.2f'%_ for _ in y[0]]
['0.86', '0.96']
>>>
>>> # Regression
>>> n = Network()
>>> n.layers.append( Fullconnect(2, 10, ReLU.function, ReLU.derivative, updater=GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 2, updater=GradientDescent(learning_rate)) )
>>> n.activation = Identity()
>>>
>>> for epoch in range(0, 20):
... loss = n.train( x = np.array([ [1, 2, 1, 2, 5, 6, 5, 6],
... [5, 4, 4, 5, 1, 2, 2, 1]]).T,
... target = np.array([ [1, 1, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 1, 1, 1, 1]]).T )
... if epoch%5 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:52.82
epoch:0005 loss:1.81
epoch:0010 loss:1.26
epoch:0015 loss:0.89
>>>
>>> y = n.predict( np.array( [[1, 6, 5], [5, 1, 4]] ).T )
>>> print ['%.2f'%_ for _ in y[0]]
['1.19', '-0.00']
>>>
>>> # Auto-encoder
>>> n = Network()
>>> n.layers.append( Fullconnect( 2, 10, Tanh.function, Tanh.derivative, GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 10, Tanh.function, Tanh.derivative, GradientDescent(learning_rate)) )
>>> n.layers.append( Fullconnect(10, 10, updater=NotUpdate()) )
>>> n.layers.append( Fullconnect(10, 2, updater=NotUpdate()) )
>>> n.activation = Identity()
>>>
>>> # for auto-encoder (weight share)
>>> n.layers[2].W = n.layers[1].W.T
>>> n.layers[3].W = n.layers[0].W.T
>>>
>>> x = np.array( [[1, 2, 1, 2, 5, 6, 5, 6, 5, 6, 5, 6],
... [5, 4, 4, 5, 5, 4, 5, 4, 1, 2, 2, 1]] ).T
>>>
>>> for epoch in range(0, 301):
... loss = n.train( x=x, target=x )
... if epoch%100 == 0:
... print 'epoch:%04d loss:%.2f'%(epoch, loss)
epoch:0000 loss:98.38
epoch:0100 loss:9.83
epoch:0200 loss:1.79
epoch:0300 loss:1.82
'''
pass
