class MLP:
def __init__(self, input, label, n_in, n_hidden, n_out, rng=None):
self.x = input
self.y = label
if rng is None:
rng = numpy.random.RandomState(1234)
# construct hidden_layer
self.hidden_layer = HiddenLayer(input=self.x,
n_in=n_in,
n_out=n_hidden,
rng=rng,
activation=tanh)
# construct log_layer
self.log_layer = LogisticRegression(input=self.hidden_layer.output,
label=self.y,
n_in=n_hidden,
n_out=n_out)
def train(self):
# forward hidden_layer
layer_input = self.hidden_layer.forward()
self.log_layer.train(input=layer_input)
# backward hidden_layer
self.hidden_layer.backward(prev_layer=self.log_layer)
def predict(self, x):
x = self.hidden_layer.output(input=x)
return self.log_layer.predict(x)
class DBN:
def __init__(self, input=None, label=None, n_ins=2, hidden_layer_sizes=[3, 3], n_outs=2, rng=None):
self.x = input
self.y = label
self.sigmoid_layers = []
self.rbm_layers = []
self.n_layers = len(hidden_layer_sizes) # = len(self.rbm_layers)
if rng is None:
rng = numpy.random.RandomState(1234)
assert self.n_layers > 0
# construct multi-layer
for i in xrange(self.n_layers):
# layer_size
if i == 0:
input_size = n_ins
else:
input_size = hidden_layer_sizes[i - 1]
# layer_input
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[-1].sample_h_given_v()
# construct sigmoid_layer
sigmoid_layer = HiddenLayer(input=layer_input,
n_in=input_size,
n_out=hidden_layer_sizes[i],
rng=rng,
activation=sigmoid)
self.sigmoid_layers.append(sigmoid_layer)
# construct rbm_layer
rbm_layer = RBM(input=layer_input,
n_visible=input_size,
n_hidden=hidden_layer_sizes[i],
W=sigmoid_layer.W, # W, b are shared
hbias=sigmoid_layer.b)
self.rbm_layers.append(rbm_layer)
# layer for output using Logistic Regression
self.log_layer = LogisticRegression(input=self.sigmoid_layers[-1].sample_h_given_v(),
label=self.y,
n_in=hidden_layer_sizes[-1],
n_out=n_outs)
# finetune cost: the negative log likelihood of the logistic regression layer
self.finetune_cost = self.log_layer.negative_log_likelihood()
def pretrain(self, lr=0.1, k=1, epochs=100):
# pre-train layer-wise
for i in xrange(self.n_layers):
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[i-1].sample_h_given_v(layer_input)
rbm = self.rbm_layers[i]
for epoch in xrange(epochs):
rbm.contrastive_divergence(lr=lr, k=k, input=layer_input)
def finetune(self, lr=0.1, epochs=100):
layer_input = self.sigmoid_layers[-1].sample_h_given_v()
# train log_layer
epoch = 0
done_looping = False
while (epoch < epochs) and (not done_looping):
self.log_layer.train(lr=lr, input=layer_input)
lr *= 0.95
epoch += 1
def predict(self, x):
layer_input = x
for i in xrange(self.n_layers):
sigmoid_layer = self.sigmoid_layers[i]
layer_input = sigmoid_layer.output(input=layer_input)
out = self.log_layer.predict(layer_input)
return out
class SdA:
def __init__(self,
input=None,
label=None,
n_ins=2,
hidden_layer_sizes=[3, 3],
n_outs=2,
rng=None):
self.x = input
self.y = label
self.sigmoid_layers = []
self.dA_layers = []
self.n_layers = len(hidden_layer_sizes) # = len(self.rbm_layers)
if rng is None:
rng = numpy.random.RandomState(1234)
assert self.n_layers > 0
# construct multi-layer
for i in xrange(self.n_layers):
# layer_size
if i == 0:
input_size = n_ins
else:
input_size = hidden_layer_sizes[i - 1]
# layer_input
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[-1].sample_h_given_v()
# construct sigmoid_layer
sigmoid_layer = HiddenLayer(input=layer_input,
n_in=input_size,
n_out=hidden_layer_sizes[i],
rng=rng,
activation=sigmoid)
self.sigmoid_layers.append(sigmoid_layer)
# construct dA_layers
dA_layer = dA(input=layer_input,
n_visible=input_size,
n_hidden=hidden_layer_sizes[i],
W=sigmoid_layer.W,
hbias=sigmoid_layer.b)
self.dA_layers.append(dA_layer)
# layer for output using Logistic Regression
self.log_layer = LogisticRegression(
input=self.sigmoid_layers[-1].sample_h_given_v(),
label=self.y,
n_in=hidden_layer_sizes[-1],
n_out=n_outs)
# finetune cost: the negative log likelihood of the logistic regression layer
self.finetune_cost = self.log_layer.negative_log_likelihood()
def pretrain(self, lr=0.1, corruption_level=0.3, epochs=100):
for i in xrange(self.n_layers):
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[i - 1].sample_h_given_v(
layer_input)
da = self.dA_layers[i]
for epoch in xrange(epochs):
da.train(lr=lr,
corruption_level=corruption_level,
input=layer_input)
def finetune(self, lr=0.1, epochs=100):
layer_input = self.sigmoid_layers[-1].sample_h_given_v()
# train log_layer
epoch = 0
while epoch < epochs:
self.log_layer.train(lr=lr, input=layer_input)
lr *= 0.95
epoch += 1
def predict(self, x):
layer_input = x
for i in xrange(self.n_layers):
sigmoid_layer = self.sigmoid_layers[i]
layer_input = sigmoid_layer.output(input=layer_input)
return self.log_layer.predict(layer_input)
class CNN:
def __init__(self,
N,
label,
n_hidden,
n_out,
image_size,
channel,
n_kernels,
kernel_sizes,
pool_sizes,
rng=None,
activation=ReLU):
if rng is None:
rng = numpy.random.RandomState(1234)
self.N = N
self.n_hidden = n_hidden
self.n_kernels = n_kernels
self.pool_sizes = pool_sizes
self.conv_layers = []
self.conv_sizes = []
# construct 1st conv_layer
conv_layer0 = ConvPoolLayer(N, image_size, channel, n_kernels[0],
kernel_sizes[0], pool_sizes[0], rng,
activation)
self.conv_layers.append(conv_layer0)
conv_size = [
(image_size[0] - kernel_sizes[0][0] + 1) / pool_sizes[0][0],
(image_size[1] - kernel_sizes[0][1] + 1) / pool_sizes[0][1]
]
self.conv_sizes.append(conv_size)
# construct 2nd conv_layer
conv_layer1 = ConvPoolLayer(N, conv_size, n_kernels[0], n_kernels[1],
kernel_sizes[1], pool_sizes[1], rng,
activation)
self.conv_layers.append(conv_layer1)
conv_size = [
(conv_size[0] - kernel_sizes[1][0] + 1) / pool_sizes[1][0],
(conv_size[1] - kernel_sizes[1][0] + 1) / pool_sizes[1][1]
]
self.conv_sizes.append(conv_size)
# construct hidden_layer
self.hidden_layer = HiddenLayer(
None, n_kernels[-1] * conv_size[0] * conv_size[1], n_hidden, None,
None, rng, activation)
# construct log_layer
self.log_layer = LogisticRegression(None, label, n_hidden, n_out)
# def train(self, epochs, learning_rate, input=None):
def train(self, epochs, learning_rate, input, test_input=None):
for epoch in xrange(epochs):
if (epoch + 1) % 5 == 0:
print 'iter = %d/%d' % (epoch + 1, epochs)
print
print '------------------'
print 'TEST PROCESSING...'
print self.predict(test_input)
print '------------------'
print
# forward first conv layer
pooled_X = self.conv_layers[0].forward(input=input)
# forward second conv layer
pooled_X = self.conv_layers[1].forward(input=pooled_X)
# flatten input
layer_input = self.flatten(pooled_X)
# forward hidden layer
layer_input = self.hidden_layer.forward(input=layer_input)
# forward & backward logistic layer
self.log_layer.train(lr=learning_rate, input=layer_input)
# backward hidden layer
self.hidden_layer.backward(prev_layer=self.log_layer,
lr=learning_rate)
flatten_size = self.n_kernels[-1] * self.conv_sizes[-1][
0] * self.conv_sizes[-1][1]
delta_flatten = numpy.zeros((self.N, flatten_size))
for n in xrange(self.N):
for i in xrange(flatten_size):
for j in xrange(self.n_hidden):
delta_flatten[n][i] += self.hidden_layer.W[i][
j] * self.hidden_layer.d_y[n][j]
# unflatten delta
delta = numpy.zeros(
(len(delta_flatten), self.n_kernels[-1],
self.conv_sizes[-1][0], self.conv_sizes[-1][1]))
for n in xrange(len(delta)):
index = 0
for k in xrange(self.n_kernels[-1]):
for i in xrange(self.conv_sizes[-1][0]):
for j in xrange(self.conv_sizes[-1][1]):
delta[n][k][i][j] = delta_flatten[n][index]
index += 1
# backward second conv layer
delta = self.conv_layers[1].backward(delta, self.conv_sizes[1],
learning_rate)
# backward first conv layer
self.conv_layers[0].backward(delta, self.conv_sizes[0],
learning_rate)
def flatten(self, input):
flatten_size = self.n_kernels[-1] * self.conv_sizes[-1][
0] * self.conv_sizes[-1][1]
flattened_input = numpy.zeros((len(input), flatten_size))
for n in xrange(len(flattened_input)):
index = 0
for k in xrange(self.n_kernels[-1]):
for i in xrange(self.conv_sizes[-1][0]):
for j in xrange(self.conv_sizes[-1][1]):
flattened_input[n][index] = input[n][k][i][j]
index += 1
# print flattened_input
return flattened_input
def predict(self, x):
pooled_X = self.conv_layers[0].forward(input=x)
pooled_X = self.conv_layers[1].forward(input=pooled_X)
layer_input = self.flatten(pooled_X)
x = self.hidden_layer.output(input=layer_input)
return self.log_layer.predict(x)