class MnistNetMiniBatch:
def __init__(self):
self.d1_layer = Dense(784, 100)
self.a1_layer = ReLu()
self.drop1_layer = Dropout(0.5)
self.d2_layer = Dense(100, 50)
self.a2_layer = ReLu()
self.drop2_layer = Dropout(0.25)
self.d3_layer = Dense(50, 10)
self.a3_layer = Softmax()
def forward(self, x, train=True):
net = self.d1_layer.forward(x)
net = self.a1_layer.forward(net)
net = self.drop1_layer.forward(net, train)
net = self.d2_layer.forward(net)
net = self.a2_layer.forward(net)
net = self.drop2_layer.forward(net, train)
net = self.d3_layer.forward(net)
net = self.a3_layer.forward(net)
return (net)
def backward(self,
dz,
learning_rate=0.01,
mini_batch=True,
update=False,
len_mini_batch=None):
dz = self.a3_layer.backward(dz)
dz = self.d3_layer.backward(dz,
learning_rate=learning_rate,
mini_batch=mini_batch,
update=update,
len_mini_batch=len_mini_batch)
dz = self.drop2_layer.backward(dz)
dz = self.a2_layer.backward(dz)
dz = self.d2_layer.backward(dz,
learning_rate=learning_rate,
mini_batch=mini_batch,
update=update,
len_mini_batch=len_mini_batch)
dz = self.drop1_layer.backward(dz)
dz = self.a1_layer.backward(dz)
dz = self.d1_layer.backward(dz,
learning_rate=learning_rate,
mini_batch=mini_batch,
update=update,
len_mini_batch=len_mini_batch)
return dz
def test_softmax_calculate_gradient(self):
# Given
pre_activation = np.array([[1, 2, 3, 6], [2, 4, 5, 6], [3, 8, 7, 6]])
target = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
softmax = Softmax()
# When
activation = softmax.apply_activation(pre_activation)
grad = softmax.calculate_gradient(activation, target)
def __init__(self):
self.d1_layer = Dense(784, 100)
self.a1_layer = ReLu()
self.drop1_layer = Dropout(0.5)
self.d2_layer = Dense(100, 50)
self.a2_layer = ReLu()
self.drop2_layer = Dropout(0.25)
self.d3_layer = Dense(50, 10)
self.a3_layer = Softmax()
def test_softmax_apply_activation(self):
# Given
pre_activation = np.array([[1, 2, 3, 6], [2, 4, 5, 6], [3, 8, 7, 6]])
softmax = Softmax()
# When
activation = softmax.apply_activation(pre_activation)
# Then
sum_of_columns = 4.0
self.assertTrue(
np.isclose(sum_of_columns, np.sum(np.sum(activation, axis=0)),
1e-3))
def build_model():
model = NeuralNetwork(loss=CrossEntropy(),
n_iterations=800,
learning_rate=0.13)
model.add(Layer(4, 10, activation=Relu()))
model.add(Layer(10, 3, activation=Softmax()))
return model
def sequential(self,
network=[1, 1, 1],
activation=[ReLU(), Softmax()],
loss=Cross_entropy(),
regu=Default(),
weight_type='default'):
self.net = network
self.activation1 = activation[0]
self.activation2 = activation[1]
self.loss = loss
self.regu = regu
self.init_weight(weight_type)
def __init__(self):
self.layers = []
self.activation = Softmax()
self.init()
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
val = df.drop(train.index)
yr = train.iloc[:, 0].to_numpy()
X_train, y_train = train.iloc[:, 1:].to_numpy() / 255.0, onehotcode(yr)
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
y_train = y_train.reshape((y_train.shape[0], y_train.shape[1], 1))
print(X_train.shape, y_train.shape)
X_val, y_val = val.iloc[:, 1:].to_numpy() / 255.0, val.iloc[:, 0].to_numpy()
X_val = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))
print(X_val.shape, y_val.shape)
nn = NN()
nn.sequential(network=[784, 128, 10],
activation=[Tanh(), Softmax()],
loss=Cross_entropy(),
regu=Ridge(n=X_train.shape[0], lmda=5),
weight_type='glorot_normal')
nn.load_model('tanL2128')
#nn.fit(X_train,y_train,X_val,y_val,32,2)
#nn.save_model('tanL2128')
nn.weight_heatmap()
df2 = pd.read_csv('mnist_test.csv')
X_test, y_test = df2.iloc[:, 1:].to_numpy() / 255.0, df2.iloc[:, 0].to_numpy()
X_test = X_test.reshape((X_test.shape[0], 28, 28))
print(X_test.shape, y_test.shape)
nn.annote_test(X_test[:100], 10, 10)
def __init__(self):
self.layers = []
self.activation = Softmax()
self.init()
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