def fprop(self, data, probs, train=TRAIN):
input = data
for i in range(len(self.layers)):
l = self.layers[i]
l.fprop(input, self.outputs[i], train)
input = self.outputs[i]
# probs.shape = self.outputs[-1].shape
gpu_copy_to(self.outputs[-1], probs)
def fprop(self, data, probs, train=TRAIN):
input = data
for i in range(len(self.layers)):
l = self.layers[i]
l.fprop(input, self.outputs[i], train)
input = self.outputs[i]
# probs.shape = self.outputs[-1].shape
gpu_copy_to(self.outputs[-1], probs)
def adjust_learning_rate(self, factor):
factors = factor
train_data = self.train_data
test_data = self.test_data
train_label = self.train_label
test_label = self.test_label
weights = []
biases = []
epsW = []
epsB = []
print 'store the weight, bias and learning rate'
for layer in self.layers:
if isinstance(layer, WeightedLayer):
weight = gpuarray.empty_like(layer.weight)
gpu_copy_to(layer.weight, weight)
weights.append(weight)
epsW.append(layer.epsW)
bias = gpuarray.empty_like(layer.bias)
gpu_copy_to(layer.bias, bias)
biases.append(bias)
epsB.append(layer.epsB)
print 'find the best learning rate'
print 'the factor list is ', factors
self.prepare_for_train(train_data, train_label)
self.fprop(self.data, self.output)
self.bprop(self.data, self.label, self.output)
self.get_batch_information()
self.update()
self.train_batch(test_data, test_label, TEST)
cost, correct, numCase = self.get_batch_information()
best = (correct, 1.0)
origin = (correct, 1.0)
print 'The normal update produce the correct', correct, 'number of case is', numCase
for factor in factors:
print 'Try the factor', factor
i = 0
for layer in self.layers:
if isinstance(layer, WeightedLayer):
gpu_copy_to(weights[i], layer.weight)
gpu_copy_to(biases[i], layer.bias)
layer.epsW = epsW[i] * factor
layer.epsB = epsB[i] * factor
i += 1
self.update()
'''
for layer in self.layers:
if isinstance(layer, WeightedLayer):
print 'epsW', layer.epsW, 'epsB', layer.epsB
printMatrix(layer.weight, layer.name + 'weight')
printMatrix(layer.bias, layer.name + 'bias')
'''
self.train_batch(test_data, test_label, TEST)
cost, correct, numCase = self.get_batch_information()
print 'Applying factor', factor, ', The correct is', correct, 'number of case is', numCase
if correct > best[0]:
best = (correct, factor)
self.adjust_info.append((best[1], best[0], origin[0]))
if best[0] / origin[0] < 1.025:
best = origin
factor = best[1]
i = 0
for layer in self.layers:
if isinstance(layer, WeightedLayer):
gpu_copy_to(weights[i], layer.weight)
gpu_copy_to(biases[i], layer.bias)
layer.epsW = epsW[i] * factor
layer.epsB = epsB[i] * factor
print 'Layer', layer.name
print 'epsW is', layer.epsW, 'epsB is', layer.epsB
i += 1
def adjust_learning_rate(self, factor):
factors = factor
train_data = self.train_data
test_data = self.test_data
train_label = self.train_label
test_label = self.test_label
weights = []
biases = []
epsW = []
epsB = []
print 'store the weight, bias and learning rate'
for layer in self.layers:
if isinstance(layer, WeightedLayer):
weight = gpuarray.empty_like(layer.weight)
gpu_copy_to(layer.weight, weight)
weights.append(weight)
epsW.append(layer.epsW)
bias = gpuarray.empty_like(layer.bias)
gpu_copy_to(layer.bias, bias)
biases.append(bias)
epsB.append(layer.epsB)
print 'find the best learning rate'
print 'the factor list is ', factors
self.prepare_for_train(train_data, train_label)
self.fprop(self.data, self.output)
self.bprop(self.data, self.label, self.output)
self.get_batch_information()
self.update()
self.train_batch(test_data, test_label, TEST)
cost, correct, numCase = self.get_batch_information()
best = (correct , 1.0)
origin = (correct, 1.0)
print 'The normal update produce the correct', correct, 'number of case is', numCase
for factor in factors:
print 'Try the factor', factor
i = 0
for layer in self.layers:
if isinstance(layer, WeightedLayer):
gpu_copy_to(weights[i], layer.weight)
gpu_copy_to(biases[i], layer.bias)
layer.epsW = epsW[i] * factor
layer.epsB = epsB[i] * factor
i += 1
self.update()
'''
for layer in self.layers:
if isinstance(layer, WeightedLayer):
print 'epsW', layer.epsW, 'epsB', layer.epsB
printMatrix(layer.weight, layer.name + 'weight')
printMatrix(layer.bias, layer.name + 'bias')
'''
self.train_batch(test_data, test_label, TEST)
cost, correct, numCase = self.get_batch_information()
print 'Applying factor', factor, ', The correct is', correct, 'number of case is', numCase
if correct > best[0]:
best = (correct, factor)
self.adjust_info.append((best[1], best[0], origin[0]))
if best[0] / origin[0] < 1.025:
best = origin
factor = best[1]
i = 0
for layer in self.layers:
if isinstance(layer, WeightedLayer):
gpu_copy_to(weights[i], layer.weight)
gpu_copy_to(biases[i], layer.bias)
layer.epsW = epsW[i] * factor
layer.epsB = epsB[i] * factor
print 'Layer', layer.name
print 'epsW is', layer.epsW, 'epsB is', layer.epsB
i += 1
