def __init__(self, config, name):
super(AE, self).__init__(config, name)
#dimension of hidden layer
self.hDim = int(self.readField(config, name, "hidden_dimension"))
#dimension of visible layer
self.vDim = int(self.readField(config, name, "visible_dimension"))
#baise for hidden layer
if self.hDim>0:
self.b1 = gp.zeros(self.hDim)
#biase for visible layer
if self.vDim>0:
self.b2 = gp.zeros(self.vDim)
#init weight: uniform between +-sqrt(6)/sqrt(v+h+1)
if self.hDim*self.vDim>0:
gp.seed_rand()
r=gp.sqrt(6)/gp.sqrt(self.hDim+self.vDim+1)
self.W1 = gp.randn(self.vDim, self.hDim) * 2 * r - r
self.W2 = gp.randn(self.hDim, self.vDim) * 2 * r - r
self.initUpdate()
self.initHyperParam(config, name)
def initRandom(self):
gp.seed_rand()
r = gp.sqrt(6) / gp.sqrt(self.hDim + self.vDim + 1)
self.W1 = gp.randn(self.vDim, self.hDim) * 2 * r - r
self.W2 = gp.randn(self.hDim, self.vDim) * 2 * r - r
self.initUpdate()
self.initHyperParam(self.config, self.name)
def epoch_update(self, input_batch, output_batch):
this_batch_size = input_batch.shape[0]
if not isinstance(input_batch, gnp.garray):
input_batch = gnp.garray(input_batch)
if not isinstance(output_batch, gnp.garray):
output_batch = gnp.garray(output_batch)
error_residual, output_result, error = self.feed_back(input_batch, output_batch)
for i, (w_grad, b_grad) in enumerate(self.gradients(self.state, error_residual)):
self.weight_grads_l2_norm[i] += (w_grad/this_batch_size - self.l2*self.weights[i]) ** 2
self.bias_gradis_l2_norm[i] += (b_grad/this_batch_size) ** 2
w_factor = 1 / gnp.sqrt(self.weight_grads_l2_norm[i])
b_factor = 1 / gnp.sqrt(self.bias_gradis_l2_norm[i])
self.weight_grads[i] = self.learning_rate * w_factor * (
w_grad/this_batch_size - self.l2*self.weights[i])
self.bias_grads[i] = (self.learning_rate*b_factor/this_batch_size) * b_grad
for i in range(len(self.weights)):
self.weights[i] += self.weight_grads[i]
self.biases[i] += self.bias_grads[i]
return error, output_result
def __init__(self, config, name):
super(AE, self).__init__(config, name)
#dimension of hidden layer
self.hDim = int(self.readField(config, name, "hidden_dimension"))
#dimension of visible layer
self.vDim = int(self.readField(config, name, "visible_dimension"))
#baise for hidden layer
if self.hDim > 0:
self.b1 = gp.zeros(self.hDim)
#biase for visible layer
if self.vDim > 0:
self.b2 = gp.zeros(self.vDim)
#init weight: uniform between +-sqrt(6)/sqrt(v+h+1)
if self.hDim * self.vDim > 0:
gp.seed_rand()
r = gp.sqrt(6) / gp.sqrt(self.hDim + self.vDim + 1)
self.W1 = gp.randn(self.vDim, self.hDim) * 2 * r - r
self.W2 = gp.randn(self.hDim, self.vDim) * 2 * r - r
self.initUpdate()
self.initHyperParam(config, name)
def initParams(self):
# crude way of random initialization (random seed) for parameters
import time
self.seed = int(time.time()) % 100000
# for tt in range(self.seed): gp.rand()
sizes = [self.inputDim] + self.layerSizes + [self.outputDim]
scales = [
gp.sqrt(6) / gp.sqrt(n + m) for n, m in zip(sizes[:-1], sizes[1:])
]
self.stack = [[gp.rand(m,n)*2*s-s,gp.zeros((m,1))] \
for n,m,s in zip(sizes[:-1],sizes[1:],scales)]
self.hActs = [gp.empty((s, self.mbSize)) for s in sizes]
if self.train:
self.deltas = [gp.empty((s, self.mbSize)) for s in sizes[1:]]
self.grad = [[gp.empty(w.shape),
gp.empty(b.shape)] for w, b in self.stack]
for tt in range(self.seed):
gp.rand()
self.stack = [[
ws[0] + .01 * gp.randn(ws[0].shape),
ws[1] + .01 * gp.randn(ws[1].shape)
] for ws in self.stack]
def _updateWeights(self, dEdW):
if self.gpu:
if self.gradientClip > 0.0:
self.dEdWnorm = gpu.sqrt(gpu.sum(dEdW**2))
if self.dEdWnorm > self.gradientClip:
dEdW *= self.gradientClip / self.dEdWnorm
if self.learningRate > 0.0:
self.lastdW = -self.learningRate * dEdW + \
self.momentum * self.lastdW
self.W += self.lastdW
if self.weightRegConst > 0.0:
a = self.learningRate * self.weightRegConst
self.W -= a * self.W
if self.weightClip > 0.0:
self.Wnorm = gpu.sqrt(gpu.sum(self.W**2))
if self.Wnorm > self.weightClip:
self.W *= self.weightClip / self.Wnorm
else:
if self.gradientClip > 0.0:
self.dEdWnorm = np.sqrt(np.sum(np.power(dEdW, 2)))
if self.dEdWnorm > self.gradientClip:
dEdW *= self.gradientClip / self.dEdWnorm
if self.learningRate > 0.0:
self.lastdW = -self.learningRate * dEdW + \
self.momentum * self.lastdW
self.W += self.lastdW
if self.weightRegConst > 0.0:
a = self.learningRate * self.weightRegConst
self.W -= a * self.W
if self.weightClip > 0.0:
self.Wnorm = np.sqrt(np.sum(np.power(self.W, 2)))
if self.Wnorm > self.weightClip:
self.W *= self.weightClip / self.Wnorm
def _updateWeights(self, dEdW):
if self.gpu:
if self.gradientClip > 0.0:
self.dEdWnorm = gpu.sqrt(gpu.sum(dEdW ** 2))
if self.dEdWnorm > self.gradientClip:
dEdW *= self.gradientClip / self.dEdWnorm
if self.learningRate > 0.0:
self.lastdW = -self.learningRate * dEdW + \
self.momentum * self.lastdW
self.W += self.lastdW
if self.weightRegConst > 0.0:
a = self.learningRate * self.weightRegConst
self.W -= a * self.W
if self.weightClip > 0.0:
self.Wnorm = gpu.sqrt(gpu.sum(self.W ** 2))
if self.Wnorm > self.weightClip:
self.W *= self.weightClip / self.Wnorm
else:
if self.gradientClip > 0.0:
self.dEdWnorm = np.sqrt(np.sum(np.power(dEdW, 2)))
if self.dEdWnorm > self.gradientClip:
dEdW *= self.gradientClip / self.dEdWnorm
if self.learningRate > 0.0:
self.lastdW = -self.learningRate * dEdW + \
self.momentum * self.lastdW
self.W += self.lastdW
if self.weightRegConst > 0.0:
a = self.learningRate * self.weightRegConst
self.W -= a * self.W
if self.weightClip > 0.0:
self.Wnorm = np.sqrt(np.sum(np.power(self.W, 2)))
if self.Wnorm > self.weightClip:
self.W *= self.weightClip / self.Wnorm
def linear_decoder_run_gpu(data, numInput, numHidden):
print "Starting Feature Abstraction..."
num_input = numInput
num_hidden = numHidden
num_output = numInput
lambda_val = 3e-3
sparsityParam = 0.035
beta = 5
inputs = data
r = gpu.sqrt(6)/gpu.sqrt(num_hidden+num_input+1)
weights1 = (gpu.rand(num_hidden,num_input+1))*2*r-r
weights2 = (gpu.rand(num_output,num_hidden+1))*2*r-r
num_weights1 = (num_input+1)*num_hidden
num_weights2 = (num_hidden+1)*num_output
#weights1 = reshape(weights1, num_weights1)
weights1 = weights1.reshape(num_weights1)
#weights2 = reshape(weights2, num_weights2)
weights2 = weights2.reshape(num_weights2)
weights = hstack((weights1.as_numpy_array(),weights2.as_numpy_array()))
args = (num_input,num_hidden,num_output,inputs,lambda_val,sparsityParam,beta)
opttheta = optimize.fmin_l_bfgs_b(costfunc_gpu, weights, fprime=grad_costfunc_gpu, args=args, maxiter=400)
weights = opttheta[0]
weights1 = reshape(weights[0:num_weights1],(num_hidden,num_input+1))
weights2 = reshape(weights[num_weights1:shape(weights)[0]], (num_output,num_hidden+1))
scipy.io.savemat('learntFeaturesGPU.mat', mdict={'learntFeatures': weights1})
return weights1
def updateParams(self,scale,update,log=False):
if log:
for w,u in zip(self.stack,update):
wrms = gp.sqrt(gp.mean(w[0]**2))
urms = gp.sqrt(gp.mean((scale*u[0])**2))
print "weight rms=%f -- update rms=%f"%(wrms,urms)
self.stack = [[ws[0]+scale*wsDelta[0],ws[1]+scale*wsDelta[1]]
for ws,wsDelta in zip(self.stack,update)]
def initParams(self):
sizes = [self.inputDim]+self.layerSizes+[self.outputDim]
scales = [gp.sqrt(6)/gp.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]
self.stack = [[gp.rand(m,n)*2*s-s,gp.zeros((m,1))] \
for n,m,s in zip(sizes[:-1],sizes[1:],scales)]
self.hActs = [gp.empty((s,self.mbSize)) for s in sizes]
if self.train:
self.deltas = [gp.empty((s,self.mbSize)) for s in sizes[1:]]
self.grad = [[gp.empty(w.shape),gp.empty(b.shape)] for w,b in self.stack]
def _dist_cosine(X, Y):
"""
d_ij = 0.5 * (1 - <x_i,y_j> / |x_i| |y_j|)
This is a variant of the cosine similarity, modified so that it is a
distance, i.e. the smaller the closer, and the distance is between 0 and 1.
"""
X_norm = gnp.sqrt((X*X).sum(axis=1)) + 1e-10
Y_norm = gnp.sqrt((Y*Y).sum(axis=1)) + 1e-10
return (1 - X.dot(Y.T) / (X_norm.reshape(-1,1).dot(Y_norm.reshape(1,-1)))) / 2
def mlpSoftmax_test():
numClasses = 10
inputSize = 28 * 28
l1Size = 100
l2Size = 20
lambda_softmax = 1e-4
lambda_hidden = 8e-5
print "Loading data..."
inputs, labels, testData, testLabels = obtain_data()
print shape(labels)
print "Done."
numCases = shape(inputs)[1]
num_weights_L1 = l1Size * (inputSize + 1)
num_weights_L2 = l2Size * (l1Size + 1)
num_weights_softmax = numClasses * l2Size
r = gpu.sqrt(6)/gpu.sqrt(inputSize+l1Size+l2Size+1)
theta_L1 = (gpu.rand(l1Size, inputSize+1))*2*r-r
theta_L2 = (gpu.rand(l2Size, l1Size+1))*2*r-r
theta_softmax = (gpu.rand(numClasses, l2Size))*2*r-r
groundTruth = ground_Truth(labels,numCases)
#theta_L1 = reshape(theta_L1, num_weights_L1)
theta_L1 = theta_L1.reshape(num_weights_L1)
#theta_L2 = reshape(theta_L2, num_weights_L2)
theta_L2 = theta_L2.reshape(num_weights_L2)
#theta_softmax = reshape(theta_softmax, num_weights_softmax)
theta_softmax = theta_softmax.reshape(num_weights_softmax)
theta = hstack((theta_L1.as_numpy_array(), theta_L2.as_numpy_array(), theta_softmax.as_numpy_array()))
args = (numClasses, inputSize, l1Size, l2Size, lambda_softmax, lambda_hidden, inputs, labels, groundTruth)
print "Starting Network Training..."
opttheta = optimize.fmin_l_bfgs_b(mlpSoftmax_costfunc, theta, fprime=mlpSoftmax_grad, args=args, maxiter=400)
theta = opttheta[0]
print "Training finished."
scipy.io.savemat('mlpSoftmax.mat', mdict={'theta': theta})
print "Now testing prediction accuracy..."
theta_L1 = reshape(theta[0:num_weights_L1], (l1Size, inputSize + 1))
theta_L2 = reshape(theta[num_weights_L1:num_weights_L2+num_weights_L1], (l2Size, l1Size + 1))
theta_softmax = reshape(theta[num_weights_L2+num_weights_L1:shape(theta)[0]], (numClasses, l2Size))
numCasesPred = shape(testData)[1]
testData = concatenate((ones((1,numCasesPred)), testData), axis = 0)
hidden_sum_L1 = dot(theta_L1, testData)
hidden_activation_L1 = 1/(1 + exp(-hidden_sum_L1))
hidden_activation_L1 = concatenate((ones((1,numCasesPred)), hidden_activation_L1), axis=0)
hidden_sum_L2 = dot(theta_L2, hidden_activation_L1)
hidden_activation_L2 = 1/(1 + exp(-hidden_sum_L2))
hidden_sum_softmax = dot(theta_softmax, hidden_activation_L2)
hidden_sum_softmax = hidden_sum_softmax - hidden_sum_softmax.max(axis = 0)
predictions = exp(hidden_sum_softmax)
predictions = predictions / predictions.sum(axis = 0)
pred = predictions.argmax(axis=0) + 1
testLabels = squeeze(testLabels)
accuracy = mean(pred == testLabels) * 100
print "Accuracy: ", accuracy, "%"
return pred, testLabels
def apply_grad(self, learn_rate=1e-2,):
"""Apply the current accumulated gradients, with adagrad."""
# Update the adagrad "momentums"
self.moms['W'] = (0.95 * self.moms['W']) + (0.05 * self.grads['W']**2.0)
self.moms['b'] = (0.95 * self.moms['b']) + (0.05 * self.grads['b']**2.0)
# Apply adagrad-style updates using current grads and moms
self.params['W'] -= learn_rate * (self.grads['W'] / \
(gp.sqrt(self.moms['W']) + ADA_EPS))
self.params['b'] -= learn_rate * (self.grads['b'] / \
(gp.sqrt(self.moms['b']) + ADA_EPS))
# Reset gradient accumulators
self.reset_grads()
return
def initParams(self):
"""
Initialize parameters using 6/sqrt(fanin+fanout)
"""
sizes = [self.inputDim]+self.layerSizes+[self.outputDim]
scales = [gp.sqrt(6)/gp.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]
self.stack = [[gp.rand(m,n)*2*s-s,gp.zeros((m,1))] \
for n,m,s in zip(sizes[:-1],sizes[1:],scales)]
self.hActs = [gp.empty((s,1)) for s in sizes]
if self.train:
self.deltas = [gp.empty((s,1)) for s in sizes[1:]]
self.grad = [[gp.empty(w.shape),gp.empty(b.shape)] for w,b in self.stack]
def mlpSoftmax_test():
numClasses = 10
inputSize = 28 * 28
l1Size = 100
lambda_softmax = 7e-4
lambda_hidden = 8e-6
print "Loading data..."
inputs, labels, testData, testLabels = obtain_data()
print shape(labels)
print "Done."
numCases = shape(inputs)[1]
num_weights_L1 = l1Size * (inputSize + 1)
num_weights_softmax = numClasses * l1Size
r = gpu.sqrt(6) / gpu.sqrt(inputSize + l1Size + 1)
theta_L1 = (gpu.rand(l1Size, inputSize + 1)) * 2 * r - r
theta_softmax = (gpu.rand(numClasses, l1Size)) * 2 * r - r
groundTruth = ground_Truth(labels, numCases)
theta_L1 = theta_L1.reshape(num_weights_L1)
theta_softmax = theta_softmax.reshape(num_weights_softmax)
theta = hstack((theta_L1.as_numpy_array(), theta_softmax.as_numpy_array()))
args = (numClasses, inputSize, l1Size, lambda_softmax, lambda_hidden,
inputs, groundTruth)
print "Starting Network Training..."
opttheta = optimize.fmin_l_bfgs_b(mlpSoftmax1Layer_costfunc,
theta,
fprime=mlpSoftmax1Layer_grad,
args=args,
maxiter=300)
theta = opttheta[0]
print "Training finished."
scipy.io.savemat('mlpSoftmax.mat', mdict={'theta': theta})
print "Now testing prediction accuracy..."
theta_L1 = reshape(theta[0:num_weights_L1], (l1Size, inputSize + 1))
theta_softmax = reshape(theta[num_weights_L1:shape(theta)[0]],
(numClasses, l1Size))
numCasesPred = shape(testData)[1]
testData = concatenate((ones((1, numCasesPred)), testData), axis=0)
hidden_sum_L1 = dot(theta_L1, testData)
#hidden_activation_L1 = log(1+exp(hidden_sum_L1))
relu_mask_hidden1 = ones(shape(hidden_sum_L1)) * (hidden_sum_L1 > 0)
hidden_activation_L1 = hidden_sum_L1 * relu_mask_hidden1
hidden_sum_softmax = dot(theta_softmax, hidden_activation_L1)
hidden_sum_softmax = hidden_sum_softmax - hidden_sum_softmax.max(axis=0)
predictions = exp(hidden_sum_softmax)
predictions = predictions / predictions.sum(axis=0)
pred = predictions.argmax(axis=0) + 1
testLabels = squeeze(testLabels)
accuracy = mean(pred == testLabels) * 100
print "Accuracy: ", accuracy, "%"
return accuracy
def multilayer_feature_learning(data, inputSize, l1Size, l2Size, sparsityParam, lambda_val, beta):
print "Now starting feature abstraction..."
num_input = inputSize
num_hidden_L1 = l1Size
num_hidden_L2 = l2Size
num_output_L1 = inputSize
num_output_L2 = num_hidden_L1
sparsityParam = sparsityParam
lambda_val = lambda_val
beta = beta
inputs = gpu.garray(data)
r = gpu.sqrt(6)/gpu.sqrt(num_hidden_L1+num_input+1)
weights1_L1 = (gpu.rand(num_hidden_L1,num_input+1))*2*r-r
weights2_L1 = (gpu.rand(num_output_L1,num_hidden_L1+1))*2*r-r
num_weights1_L1 = (num_input+1)*num_hidden_L1
num_weights2_L1 = (num_hidden_L1+1)*num_output_L1
weights1_L1 = weights1_L1.reshape(num_weights1_L1)
weights2_L1 = weights2_L1.reshape(num_weights2_L1)
weights_L1 = hstack((weights1_L1.as_numpy_array(),weights2_L1.as_numpy_array()))
print "Level 1 Abstraction Starting...."
weights_L1 = linear_decoder_run_ReLU(data, weights_L1, num_input, num_hidden_L1)
weights1_L1 = weights_L1[0:num_weights1_L1].reshape((num_hidden_L1,num_input+1))
weights2_L1 = weights_L1[num_weights1_L1:shape(weights_L1)[0]].reshape((num_output_L1,num_hidden_L1+1))
scipy.io.savemat('HiggsBosonLevel1.mat', mdict={'learntFeaturesL1_1': weights1_L1, 'learntFeaturesL1_2': weights2_L1})
L1_activation = feedforward(weights1_L1, inputs)
del weights_L1
del weights1_L1
del weights2_L1
gpu.free_reuse_cache()
v = gpu.sqrt(6)/gpu.sqrt(num_hidden_L2+num_hidden_L1+1)
weights1_L2 = (gpu.rand(num_hidden_L2,num_hidden_L1+1))*2*v-v
weights2_L2 = (gpu.rand(num_output_L2,num_hidden_L2+1))*2*v-v
num_weights1_L2 = (num_hidden_L1+1)*num_hidden_L2
num_weights2_L2 = (num_hidden_L2+1)*num_output_L2
weights1_L2 = weights1_L2.reshape(num_weights1_L2)
weights2_L2 = weights2_L2.reshape(num_weights2_L2)
weights_L2 = hstack((weights1_L2.as_numpy_array(),weights2_L2.as_numpy_array()))
print "Level 2 Abstraction Starting...."
weights_L2 = linear_decoder_run_ReLU(L1_activation, weights_L2, num_hidden_L1, num_hidden_L2)
weights1_L2 = weights_L2[0:num_weights1_L2].reshape((num_hidden_L2,num_hidden_L1+1))
weights2_L2 = weights_L2[num_weights1_L2:shape(weights_L2)[0]].reshape((num_output_L2,num_hidden_L2+1))
scipy.io.savemat('HiggsBosonLevel2.mat', mdict={'learntFeaturesL2_1': weights1_L2,'learntFeaturesL2_2': weights2_L2})
L2_activation = feedforward(weights1_L2, L1_activation)
del weights_L2
del weights1_L2
del weights2_L2
gpu.free_reuse_cache()
gpu.free_reuse_cache()
print "Abstraction completed."
return L2_activation
def checkGradientGPU():
num_input = 8 * 8 * 3
num_hidden = 10
num_output = num_input
lambda_val = 0.003
sparsityParam = 0.035
beta = 5
data = scipy.io.loadmat('stlSampledPatches.mat')
patches = data['patches']
inputs = patches[:, 0:10]
r = gpu.sqrt(6) / gpu.sqrt(num_hidden + num_input + 1)
weights1 = (gpu.rand(num_hidden, num_input + 1)) * 2 * r - r
weights2 = (gpu.rand(num_output, num_hidden + 1)) * 2 * r - r
num_weights1 = (num_input + 1) * num_hidden
num_weights2 = (num_hidden + 1) * num_output
weights1 = weights1.reshape(num_weights1)
weights2 = weights2.reshape(num_weights2)
weights = hstack((weights1.as_numpy_array(), weights2.as_numpy_array()))
args = (num_input, num_hidden, num_output, inputs, lambda_val,
sparsityParam, beta)
numgrad = zeros(size(weights))
numgrad2 = zeros(size(weights))
perturb = zeros(size(weights))
e = 1e-4
for p in range(size(weights)):
perturb[p] = e
minus_weights = weights - perturb
plus_weights = weights + perturb
loss1 = costfunc_gpuTRY(minus_weights, *args)
lossc1 = costfunc(minus_weights, *args)
loss2 = costfunc_gpu(plus_weights, *args)
lossc2 = costfunc(plus_weights, *args)
numgrad[p] = (loss2 - loss1) / (2 * e)
numgrad2[p] = (lossc2 - lossc1) / (2 * e)
perturb[p] = 0
grad = grad_costfunc_gpu(weights, *args)
grad2 = grad_costfunc(weights, *args)
diff = linalg.norm(numgrad - grad) / linalg.norm(numgrad + grad)
diff2 = linalg.norm(numgrad2 - grad2) / linalg.norm(numgrad2 + grad2)
diff3 = linalg.norm(numgrad - grad2) / linalg.norm(numgrad + grad2)
diff4 = linalg.norm(numgrad2 - grad) / linalg.norm(numgrad2 + grad)
diffnum = linalg.norm(numgrad2 - numgrad) / linalg.norm(numgrad2 + numgrad)
diffgrad = linalg.norm(grad2 - grad) / linalg.norm(grad2 + grad)
print "pure GPU difference:", diff
print "pure CPU difference:", diff2
print "GPU cost, CPU grad:", diff3
print "CPU cost, GPU grad:", diff4
print "CPU cost and GPU cost difference:", diffnum
print "CPU grad and GPU grad difference:", diffgrad
return "OK"
def checkGradientGPU():
num_input = 8*8*3
num_hidden = 10
num_output = num_input
lambda_val = 0.003
sparsityParam = 0.035
beta = 5
data = scipy.io.loadmat('stlSampledPatches.mat')
patches = data['patches']
inputs = patches[:,0:10]
r = gpu.sqrt(6)/gpu.sqrt(num_hidden+num_input+1)
weights1 = (gpu.rand(num_hidden,num_input+1))*2*r-r
weights2 = (gpu.rand(num_output,num_hidden+1))*2*r-r
num_weights1 = (num_input+1)*num_hidden
num_weights2 = (num_hidden+1)*num_output
weights1 = weights1.reshape(num_weights1)
weights2 = weights2.reshape(num_weights2)
weights = hstack((weights1.as_numpy_array(),weights2.as_numpy_array()))
args = (num_input,num_hidden,num_output,inputs,lambda_val,sparsityParam,beta)
numgrad = zeros(size(weights))
numgrad2 = zeros(size(weights))
perturb = zeros(size(weights))
e = 1e-4
for p in range(size(weights)):
perturb[p] = e;
minus_weights = weights - perturb
plus_weights = weights + perturb
loss1 = costfunc_gpuTRY(minus_weights, *args)
lossc1 = costfunc(minus_weights, *args)
loss2 = costfunc_gpuTRY(plus_weights, *args)
lossc2 = costfunc(plus_weights, *args)
numgrad[p] = (loss2 - loss1) / (2*e)
numgrad2[p] = (lossc2 - lossc1) / (2*e)
perturb[p] = 0
grad = grad_costfunc_gpu(weights, *args)
grad2 = grad_costfunc(weights, *args)
diff = linalg.norm(numgrad-grad)/linalg.norm(numgrad+grad)
diff2 = linalg.norm(numgrad2-grad2)/linalg.norm(numgrad2+grad2)
diff3 = linalg.norm(numgrad-grad2)/linalg.norm(numgrad+grad2)
diff4 = linalg.norm(numgrad2-grad)/linalg.norm(numgrad2+grad)
diffnum = linalg.norm(numgrad2-numgrad)/linalg.norm(numgrad2+numgrad)
diffgrad = linalg.norm(grad2-grad)/linalg.norm(grad2+grad)
print "pure GPU difference:",diff
print "pure CPU difference:",diff2
print "GPU cost, CPU grad:",diff3
print "CPU cost, GPU grad:",diff4
print "CPU cost and GPU cost difference:",diffnum
print "CPU grad and GPU grad difference:",diffgrad
return "OK"
def norm_trans(X, mode='ff'):
"""Compute feedforward and backprop for unit-normalization."""
EPS = 0.00000001
if (mode == 'ff'):
N = gp.sqrt(gp.sum(X**2.0, axis=1) + EPS)
N = N[:, gp.newaxis]
F = X / N
if (mode == 'bp'):
N = gp.sqrt(gp.sum(X['X']**2.0, axis=1) + EPS)
N = N[:, gp.newaxis]
V = X['dLdA'] * X['X']
V = gp.sum(V, axis=1)
V = V[:, gp.newaxis]
F = (X['dLdA'] / N) - (X['A'] * (V / (N**2.0)))
return F
def norm_trans(X, mode='ff'):
"""Compute feedforward and backprop for unit-normalization."""
EPS = 0.00000001
if (mode == 'ff'):
N = gp.sqrt(gp.sum(X**2.0, axis=1) + EPS)
N = N[:,gp.newaxis]
F = X / N
if (mode == 'bp'):
N = gp.sqrt(gp.sum(X['X']**2.0, axis=1) + EPS)
N = N[:,gp.newaxis]
V = X['dLdA'] * X['X']
V = gp.sum(V, axis=1)
V = V[:,gp.newaxis]
F = (X['dLdA'] / N) - (X['A'] * (V / (N**2.0)))
return F
def rect_sqrt(x, computeGrad = False): if (not computeGrad): f = gp.sqrt(gp.abs(x)* (x>0)) return f g = 1 / (2*x + (x<=0))*(x>0) return g
def rect_sqrt(x, computeGrad=False):
if (not computeGrad):
f = gp.sqrt(gp.abs(x) * (x > 0))
return f
g = 1 / (2 * x + (x <= 0)) * (x > 0)
return g
def constrain_weights(self):
for i, rms_limit in enumerate(self.rms_limits):
if not rms_limit:
continue
W = self.weights[i]
rms_scale = rms_limit / gnp.sqrt(gnp.mean(W*W, axis=0))
limit_rms = W * (1+(rms_scale < 1) * (rms_scale - 1))
self.weights[i] = limit_rms
def clip_params(self, max_norm=10.0):
"""Bound L2 (row-wise) norm of W by max_norm."""
M = self.params['W']
m_scales = max_norm / gp.sqrt(gp.sum(M**2.0,axis=1) + 1e-5)
mask = (m_scales < 1.0) # with gnumpy, this already comes as float32
m_scales = (m_scales * mask) + (1.0 - mask)
self.params['W'] = M * m_scales[:,gp.newaxis]
return
def cosSimilar(a, b):
global gpu
res = 0
if gpu == 1:
# print "gpu"
a = gnumpy.garray(a)
b = gnumpy.garray(b)
len_a = gnumpy.sqrt(gnumpy.dot(a,a))
len_b = gnumpy.sqrt(gnumpy.dot(b,b))
res = gnumpy.dot(a, b) / (len_a * len_b)
else:
a = numpy.array(a)
b = numpy.array(b)
len_a = numpy.sqrt(numpy.dot(a,a))
len_b = numpy.sqrt(numpy.dot(b,b))
res = numpy.dot(a, b) / (len_a * len_b)
return 0.5 + 0.5 * res
def limitColumnRMS(W, rmsLim):
"""
All columns of W with rms entry above the limit are scaled to equal the limit.
The limit can either be a row vector or a scalar.
Apply to 2-d array W.
"""
columnRMS = lambda W: gnp.sqrt(gnp.mean(W * W, axis=0))
rmsScale = rmsLim / columnRMS(W)
return W * (1 + (rmsScale < 1) * (rmsScale - 1))
def fit(self, X):
X = gnumpy.garray( X )
lens = (X ** 2).sum(-1) # precompute (squared) length of each vector
if self._metric == 'angular':
X /= gnumpy.sqrt(lens)[..., gnumpy.newaxis] # normalize index vectors to unit length
self.index = X # np.ascontiguousarray(X, dtype=self._precision)
elif self._metric == 'euclidean':
self.index = X # np.ascontiguousarray(X, dtype=self._precision)
self.lengths = lens # np.ascontiguousarray(lens, dtype=self._precision)
def _dist_euclidean(X, Y):
"""
d_ij = (x_i - y_j)^2
"""
X = gnp.as_garray(X)
Y = gnp.as_garray(Y)
X_diag = (X*X).sum(axis=1)
Y_diag = (Y*Y).sum(axis=1)
return gnp.sqrt(-2 * X.dot(Y.T) + X_diag.reshape(-1,1) + Y_diag.reshape(1,-1) + 1e-3)
def initParams(self):
# crude way of random initialization (random seed) for parameters
import time
self.seed = int(time.time()) % 100000;
# for tt in range(self.seed): gp.rand()
sizes = [self.inputDim]+self.layerSizes+[self.outputDim]
scales = [gp.sqrt(6)/gp.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]
self.stack = [[gp.rand(m,n)*2*s-s,gp.zeros((m,1))] \
for n,m,s in zip(sizes[:-1],sizes[1:],scales)]
self.hActs = [gp.empty((s,self.mbSize)) for s in sizes]
if self.train:
self.deltas = [gp.empty((s,self.mbSize)) for s in sizes[1:]]
self.grad = [[gp.empty(w.shape),gp.empty(b.shape)] for w,b in self.stack]
for tt in range(self.seed): gp.rand()
self.stack = [[ws[0]+.01 * gp.randn(ws[0].shape),ws[1]+.01 * gp.randn(ws[1].shape)]
for ws in self.stack]
def initParams(self):
"""
Initialize parameters using 6/sqrt(fanin+fanout)
"""
sizes = [self.inputDim]+self.layerSizes+[self.outputDim]
scales = [gp.sqrt(6)/gp.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]
self.stack = [[gp.rand(m,n)*2*s-s,gp.zeros((m,1))] \
for n,m,s in zip(sizes[:-1],sizes[1:],scales)]
if self.temporalLayer > 0:
rs = sizes[self.temporalLayer]
s = gp.sqrt(6)/ rs
# temporal layer stored at end of stack
self.stack.append([gp.rand(rs,rs) * 2 * s - s, gp.zeros((2,1))])
if self.train:
#TODO why store all deltas?
#self.deltas = [gp.empty((s,self.mbSize)) for s in sizes[1:]]
#NOTE if a temporal layer is used it's already added to stack so will have a grad
self.grad = [[gp.empty(w.shape),gp.empty(b.shape)] for w,b in self.stack]
def nrelu(data, wm, bias, sampling=False):
"""A noisy rectified linear unit.
"""
suff = gpu.dot(data, wm) + bias
if sampling:
sample = suff + (gpu.sqrt(suff.logistic()) * gpu.randn(suff.shape))
#sample = suff + gpu.randn(suff.shape)
sample *= (sample > 0)
else:
sample = None
suff *= (suff > 0)
return suff, sample
def forward_prop(self, X, add_noise=False, compute_loss=False):
"""
Compute the forward propagation step that maps the input data matrix X
into the output.
"""
self.mu = X.mean(axis=0)
self.sigma = gnp.sqrt(((X - self.mu)**2).mean(axis=0))
self.X_hat = (X - self.mu) / (self.sigma + 1e-10)
self.Y = self.X_hat * self.params.gamma + self.params.beta
return self.Y
def calculate_dt(v, delta_v, N_bodies, alpha):
a_max = 0.
for i in range(N_bodies):
delta_v = gpu.garray(delta_v)
a = gpu.sum(delta_v[i,:]**2)
if a > a_max:
a_max = a
a_max_index = i
v = gpu.garray(v)
v_mag = gpu.sqrt(gpu.sum(v[a_max_index,:]**2))
return alpha*v_mag/a_max
def calculate_dt(v, delta_v, N_bodies, alpha):
a_max = 0.
for i in range(N_bodies):
delta_v = gpu.garray(delta_v)
a = gpu.sum(delta_v[i, :]**2)
if a > a_max:
a_max = a
a_max_index = i
v = gpu.garray(v)
v_mag = gpu.sqrt(gpu.sum(v[a_max_index, :]**2))
return alpha * v_mag / a_max
def nrelu(data, wm, bias, sampling=False):
"""A noisy rectified linear unit.
"""
suff = gpu.dot(data, wm) + bias
if sampling:
sample = suff + (gpu.sqrt(suff.logistic()) * gpu.randn(suff.shape))
#sample = suff + gpu.randn(suff.shape)
sample *= (sample > 0)
else:
sample = None
suff *= (suff > 0)
return suff, sample
def rehu_trans(X, mode='ff'):
"""Compute feedforward and backprop for ReHu nonlinearity."""
if (mode == 'ff'):
M_quad = (X > 0.0)
M_line = (X > 0.5)
M_quad = M_quad - M_line
F = (M_line * (X - 0.25)) + (M_quad * X**2.0)
if (mode == 'bp'):
M_quad = (X['A'] < 0.25)
M_line = 1.0 - M_quad
F = (2.0 * M_quad * gp.sqrt(X['A'])) + M_line
F = F * X['dLdA']
return F
def initParams(self):
"""
Initialize parameters using 6/sqrt(fanin+fanout)
"""
sizes = [self.inputDim] + self.layerSizes + [self.outputDim]
scales = [
gp.sqrt(6) / gp.sqrt(n + m) for n, m in zip(sizes[:-1], sizes[1:])
]
self.stack = [[gp.rand(m,n)*2*s-s,gp.zeros((m,1))] \
for n,m,s in zip(sizes[:-1],sizes[1:],scales)]
if self.temporalLayer > 0:
rs = sizes[self.temporalLayer]
s = gp.sqrt(6) / rs
# temporal layer stored at end of stack
self.stack.append([gp.rand(rs, rs) * 2 * s - s, gp.zeros((2, 1))])
if self.train:
#TODO why store all deltas?
#self.deltas = [gp.empty((s,self.mbSize)) for s in sizes[1:]]
#NOTE if a temporal layer is used it's already added to stack so will have a grad
self.grad = [[gp.empty(w.shape),
gp.empty(b.shape)] for w, b in self.stack]
def rehu_trans(X, mode='ff'):
"""Compute feedforward and backprop for ReHu nonlinearity."""
if (mode == 'ff'):
M_quad = (X > 0.0)
M_line = (X > 0.5)
M_quad = M_quad - M_line
F = (M_line * (X - 0.25)) + (M_quad * X**2.0)
if (mode == 'bp'):
M_quad = (X['A'] < 0.25)
M_line = 1.0 - M_quad
F = (2.0 * M_quad * gp.sqrt(X['A'])) + M_line
F = F * X['dLdA']
return F
def output_and_cost(self, epoch, set_name = 'train'):
self.timer_logger('output_and_cost {0}'.format(set_name), time.time())
self.results['current'] = self.output(self.results['current'])
if self.problem == 'regression' and self.clip_values == 1:
# clip values into the [0,1] range
self.results['current'] = (self.results['current'] *(self.results['current'] >= 0))
self.results['current'] = (((self.results['current'] < 1)*self.results['current']) + (self.results['current'] > 1))
if set_name != 'train':
if set_name == 'no_label_test':
if 'prediction_test' not in self.results:
if self.problem == 'classification':
self.results['prediction_test'] = np.argmax(self.results['current'].as_numpy_array(),axis=1)
else:
self.results['prediction_test'] = self.results['current'].as_numpy_array()
else:
if self.problem == 'classification':
self.results['prediction_test'] = np.hstack([self.results['prediction_test'],np.argmax(self.results['current'].as_numpy_array(),axis=1)])
else:
self.results['prediction_test'] = np.vstack([self.results['prediction_test'],self.results['current'].as_numpy_array()])
elif set_name == 'cv_predict':
if self.create_cv_predictions and set_name == 'cv_predict':
if 'prediction_cv'not in self.results:
self.results['prediction_cv'] = self.results['current'].as_numpy_array()
else:
self.results['prediction_cv'] = np.vstack([self.results['prediction_cv'],self.results['current'].as_numpy_array()])
else:
if self.problem == 'classification':
self.set_error_by_epoch[set_name][epoch] += (np.sum(np.equal(np.argmax(self.results['current'].as_numpy_array(),axis=1),self.batch_y.T)))
else:
self.set_error_by_epoch[set_name][epoch] += gpu.sqrt(gpu.sum(((self.results['current']-self.batch_y)**2)*float(self.batch.shape[0]))/float(self.y.shape[1]))
if self.cost == 'auc':
if self.problem == 'regression':
if set_name + ' roc_auc' not in self.results:
self.results[set_name + ' roc_auc'] = ([np.matrix(self.results['current'].as_numpy_array()).T, np.matrix(self.batch_y).T])
else:
self.results[set_name + ' roc_auc'] = [np.hstack([self.results[set_name + ' roc_auc'][0],np.matrix(self.results['current'].as_numpy_array()).T]),
np.hstack([self.results[set_name + ' roc_auc'][1],np.matrix(self.batch_y).T])]
else:
if set_name + ' roc_auc' not in self.results:
self.results[set_name + ' roc_auc'] = ([np.matrix(self.results['current'].as_numpy_array()[:,1]).T, np.matrix(self.batch_y)])
else:
self.results[set_name + ' roc_auc'] = [np.vstack([np.matrix(self.results[set_name + ' roc_auc'][0]),np.matrix(self.results['current'].as_numpy_array()[:,1]).T]),
np.vstack([self.results[set_name + ' roc_auc'][1],np.matrix(self.batch_y)])]
self.timer_logger('output_and_cost {0}'.format(set_name), time.time())
def euclidSimilar(a, b):
global gpu
res = 0
if gpu == 1:
# print "gpu"
a = gnumpy.garray(a)
b = gnumpy.garray(b)
c = a - b
res = gnumpy.sqrt(gnumpy.dot(c, c))
else:
a = numpy.array(a)
b = numpy.array(b)
c = a - b
res = numpy.sqrt(numpy.dot(c, c))
return 1.0 / (1 + res)
def rmssd(z, targets, predict=False, error=False, addon=0):
"""
Root mean sum of squares.
"""
if predict:
return z
n, m = z.shape
err = z - targets
per_sample = gpu.sqrt(gpu.sum(err**2, axis=1) + 1e-8)
if error:
# rec. error + first deriv
return gpu.sum(per_sample) / n + addon, err / (
n * per_sample[:, gpu.newaxis])
else:
# only return reconstruction error
return gpu.sum(per_sample) / n + addon
def bound_weights(self, Wm, wt_bnd):
"""Bound L2 (row-wise) norm of the weights in Wm by wt_bnd.
This returns a garray if passed a garray, and performs all ops on the
GPU if that is the case. Otherwise, it returns a numpy array, or if
something besides an ndarray/garray was passed, it crashes (probably).
"""
EPS = 0.00000001
# Compute L2 norm of weights inbound to each node in this layer
w_norms = gp.sqrt(gp.sum(Wm**2,axis=1) + EPS)
# Compute scales based on norms and the upperbound set by wt_bnd
w_scales = wt_bnd / w_norms
mask = (w_scales < 1.0)
w_scales = (w_scales * mask) + (1.0 - mask)
w_scales = w_scales[:,gp.newaxis]
# Rescale weights to meet the bound set by wt_bnd
Wm = Wm * w_scales
return Wm
def bound_weights(self, Wm, wt_bnd):
"""Bound L2 (row-wise) norm of the weights in Wm by wt_bnd.
This returns a garray if passed a garray, and performs all ops on the
GPU if that is the case. Otherwise, it returns a numpy array, or if
something besides an ndarray/garray was passed, it crashes (probably).
"""
EPS = 0.00000001
# Compute L2 norm of weights inbound to each node in this layer
w_norms = gp.sqrt(gp.sum(Wm**2, axis=1) + EPS)
# Compute scales based on norms and the upperbound set by wt_bnd
w_scales = wt_bnd / w_norms
mask = (w_scales < 1.0)
w_scales = (w_scales * mask) + (1.0 - mask)
w_scales = w_scales[:, gp.newaxis]
# Rescale weights to meet the bound set by wt_bnd
Wm = Wm * w_scales
return Wm
def computeStat(self):
print 'Computing stats (mean and std)...'
means = gp.zeros((self.numbatches, self.dim))
variances = gp.zeros((self.numbatches, self.dim))
i = 0
while True:
batch = self.cache.getOneBatch()
if batch == None:
break
means[i] = batch.mean(axis=0)
variances[i] = gp.std(batch, axis=0)**2
i += 1
assert (i == self.numbatches)
mean = means.mean(axis=0)
std = gp.sqrt(variances.mean(axis=0) + gp.std(means, axis=0)**2)
mean_std = std.mean()
std += (std == 0.0) * mean_std
self.reset()
print 'Finish stats computing'
return mean, std + 1e-10
def computeStat(self):
print 'Computing stats (mean and std)...'
means=gp.zeros((self.numbatches, self.dim))
variances=gp.zeros((self.numbatches, self.dim))
i=0
while True:
batch=self.cache.getOneBatch()
if batch==None:
break
means[i]=batch.mean(axis=0)
variances[i]=gp.std(batch,axis=0)**2
i+=1
assert(i==self.numbatches)
mean=means.mean(axis=0)
std=gp.sqrt(variances.mean(axis=0)+gp.std(means,axis=0)**2)
mean_std=std.mean()
std+=(std==0.0)*mean_std
self.reset()
print 'Finish stats computing'
return mean, std+1e-10
def unigram_partition(data_path, num_ensembles, model_name, method = 'none', train = True, random_training_order = False, reverse_order = False):
algo_name = 'unigram' + '_' + 'partition'
raw_data = reader.ptb_raw_data(data_path)
train_data, valid_data, test_data, _, word_to_id = raw_data
if reverse_order:
train_data.reverse()
valid_data.reverse()
test_data.reverse()
algo_name = 'reverse ' + algo_name
eos_id = word_to_id['<eos>']
case_weight_length = len(train_data)-1
train_case_weights = np.repeat(1.0/case_weight_length, case_weight_length).tolist()
train_sentence_list = reader.get_sentence_list(train_data, eos_id, reverse_order)
if random_training_order:
#train_sentence_list = reader.get_sentence_list(train_data, eos_id)
perm = range(len(train_sentence_list))
np.random.shuffle(perm)
train_data = []
for idx in perm:
train_data += train_sentence_list[idx]
train_sentence_list = reader.get_sentence_list(train_data, eos_id, reverse_order)
num_sent = len(train_sentence_list)
train_sentence_weights = np.repeat(1.0/num_sent, num_sent).tolist()
new_train_data = train_data
FLAGS.model = model_name
config = get_config()
eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1
alpha_t_list = []
full_test_set_logits = []
for i in range(len(test_data)-1):
full_test_set_logits.append(np.zeros((1,eval_config.vocab_size)))
sentence_starters = []
id_to_sentence_num_dict = {}
for i in range(num_sent):
if reverse_order:
desired_id = train_sentence_list[i][-1]
else:
desired_id = train_sentence_list[i][0]
if desired_id in id_to_sentence_num_dict:
id_to_sentence_num_dict[desired_id].append(i)
else:
id_to_sentence_num_dict[desired_id] = [i]
sentence_starters.append(desired_id)
id_to_model = {}
for idx in sentence_starters:
id_to_model[idx] = [1]
id_to_weight = {}
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.5)
for iii in range(num_ensembles):
with tf.Graph().as_default(), tf.Session(config = tf.ConfigProto(gpu_options = gpu_options)) as session:
initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.variable_scope("model", reuse=None, initializer=initializer):
sess = tf.Session(config = tf.ConfigProto(gpu_options = gpu_options))
m = PTBModel(is_training=True, config=config)
sess.close()
with tf.variable_scope("model", reuse=True, initializer=initializer):
mvalid = PTBModel(is_training=False, config=config)
mtest = PTBModel(is_training=False, config=eval_config)
m2 = PTBModel(is_training=False, config=eval_config)
tf.initialize_all_variables().run()
saver = tf.train.Saver()
if iii > 0:
np.savetxt(checkpoint_dir + 'test_set_probs_no_alpha.out', np.squeeze(test_set_probs_no_alpha), delimiter = ',')
if random_training_order:
new_folder = 'random training order ' + algo_name + '_' + model_name + '/' + 'ensemble' + str(iii + 1)
else:
new_folder = algo_name + '_' + model_name + '/' + 'ensemble' + str(iii + 1)
checkpoint_dir = 'simple-examples/ckpt/' + new_folder + '/'
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if iii > 0:
for k,v in id_to_sentence_num_dict.items():
total_wt = 0
num_sentences = len(v)
for sent_idx in v:
total_wt += train_sentence_weights[sent_idx]
id_to_weight[k] = total_wt/num_sentences
sorted_id_to_weight = sorted(id_to_weight.items(), key = operator.itemgetter(1), reverse = True)
np.savetxt(checkpoint_dir + 'sorted_id_to_weight', sorted_id_to_weight, delimiter = ',')
new_train_data = []
i = 0
sent_included = 0
while sent_included < np.floor(num_sent/2):
start_key = sorted_id_to_weight[i][0]
sentence_additions = id_to_sentence_num_dict[start_key]
for idx in sentence_additions:
new_train_data += train_sentence_list[idx]
sent_included += 1
id_to_model[start_key].append(iii + 1)
i += 1
if train:
np.savetxt(checkpoint_dir + 'train_case_weights.out', train_case_weights, delimiter = ',')
np.savetxt(checkpoint_dir + 'train_sentence_weights.out', train_sentence_weights, delimiter = ',')
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
m.assign_lr(session, config.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
train_perplexity = run_epoch(session, m, new_train_data, m.train_op, verbose=False)
print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
valid_perplexity = run_epoch(session, mvalid, valid_data, tf.no_op())
print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))
if (i+1) % 5 == 0 or (i+1) == config.max_max_epoch:
saver.save(session, checkpoint_dir + 'model.ckpt', global_step = i+1)
else:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(session, ckpt.model_checkpoint_path)
if train:
case_scores = output_training_set_error_for_boosting(session, m2, train_data, tf.no_op())
score = sum(case_scores)
norm = len(case_scores) * -1 * gpu.log(float(1.0) / eval_config.vocab_size)
epsilon_t = (1 - (norm - score) / norm) / 2.0
alpha_t = 0.5 * gpu.log((1 - epsilon_t)/ epsilon_t)
alpha_t_list.append(alpha_t)
if iii == 0:
shutil.rmtree('simple-examples/ckpt/' + 'random training order ' + algo_name + '_' + model_name + '/' + 'alpha_t.out', ignore_errors = True)
with open('simple-examples/ckpt/' + 'random training order ' + algo_name + '_' + model_name + '/' + 'alpha_t.out', 'ab') as f:
f.write(str(alpha_t))
f.write(',')
train_case_weights = gpu.sqrt((1 - epsilon_t)/ epsilon_t) * np.multiply(train_case_weights, np.asarray(case_scores))
train_case_weights = np.ravel(normalize(np.asarray(train_case_weights).reshape(1,-1), norm = 'l1'))
if method == 'stddev':
new_train_case_weights = reject_outliers(train_case_weights)
elif method == 'sqrt':
new_train_case_weights = sqrt_norm(train_case_weights)
else:
new_train_case_weights = train_case_weights
start_idx = 0
for i in range(len(train_sentence_list)):
this_sentence_length = len(train_sentence_list[i])
sentence_tokens = [v for v in new_train_case_weights[start_idx:(this_sentence_length+start_idx)] if np.isfinite(v)]
if len(sentence_tokens) == 0:
this_sentence_weights[i] = 0
else:
train_sentence_weights[i] = np.mean([v for v in new_train_case_weights[start_idx:(this_sentence_length+start_idx)] if np.isfinite(v)])
start_idx += this_sentence_length
train_sentence_weights = np.ravel(normalize(np.asarray(train_sentence_weights).reshape(1,-1), norm = 'l1'))
for k,v in id_to_sentence_num_dict.items():
total_wt = 0
num_sentences = len(v)
for sent_idx in v:
total_wt += train_sentence_weights[sent_idx]
id_to_weight[k] = total_wt/num_sentences
test_set_probs = output_test_set_probs(session, mtest, test_data, tf.no_op(), partition = True)
test_set_probs_no_alpha = test_set_probs
np.savetxt(checkpoint_dir + 'test_set_probs_no_alpha.out', np.squeeze(test_set_probs_no_alpha), delimiter = ',')
train_set_probs = output_test_set_probs(session, m2, train_data, tf.no_op(), partition = True)
train_set_probs_no_alpha = train_set_probs
np.savetxt(checkpoint_dir + 'train_set_probs_no_alpha.out', np.squeeze(train_set_probs_no_alpha), delimiter = ',')
test_perplexity = run_epoch(session, mtest, test_data, tf.no_op())
print("Test Perplexity: %.3f" % test_perplexity)
if random_training_order:
new_folder = 'random training order ' + algo_name + '_' + model_name
else:
new_folder = algo_name + '_' + model_name
checkpoint_dir = 'simple-examples/ckpt/' + new_folder + '/'
with open(checkpoint_dir + 'id_to_model.out', 'w') as f:
for k,v in id_to_model.items():
f.write(str(k) + ',' + ','.join(str(id_to_model[k])) + '\n')
with open(checkpoint_dir + 'id_to_sent_num.out', 'w') as ff:
for k,v in id_to_sentence_num_dict.items():
ff.write(str(k) + ',' + ','.join(str(id_to_sentence_num_dict[k])) + '\n')
print('Test PPL: ' + str(evaluate_unigram_partition(data = test_data, batch_size = 1, num_steps = 1, num_ensembles = num_ensembles, eos_id = eos_id, fp = 'simple-examples/ckpt/random training order unigram_partition_small/', probs_fn = 'test_set_probs_no_alpha.out')))
print('Train PPL: ' + str(evaluate_unigram_partition(data = train_data, batch_size = 1, num_steps = 1, num_ensembles = num_ensembles, eos_id = eos_id, fp = 'simple-examples/ckpt/random training order unigram_partition_small/', probs_fn = 'train_set_probs_no_alpha.out')))
def columnRMS(W):
return gnp.sqrt(gnp.mean(W*W,axis=0))
def sqrt(x):
check_type(x)
if is_np(x):
return np.sqrt(x)
else:
return gp.sqrt(x)
def task_loss(self, Y, Z, A=None, task_loss_fn=None):
# root mean square error
if task_loss_fn == None:
return gnp.sqrt(((Y-Z)**2).mean())
else:
return task_loss_fn(Y, Z, A)
def sampleStates(self, acts):
if self.krizNoise:
return self.activate(acts + gnp.randn(*acts.shape))
tiny = 1e-30
stddev = gnp.sqrt(acts.sigmoid() + tiny)
return self.activate( acts + stddev*gnp.randn(*acts.shape) )
def compute_kernel_matrix(self, x):
x = x if isinstance(x, gnp.garray) else gnp.garray(x)
x_norm = gnp.sqrt((x**2).sum(axis=1))
x_norm = x_norm[:, gnp.newaxis] + x_norm[gnp.newaxis, :] + 1e-20
return x.dot(x.T) / x_norm
def sampleStates(self, acts):
if self.krizNoise:
return self.activate(acts + gnp.randn(*acts.shape))
tiny = 1e-30
stddev = gnp.sqrt(acts.sigmoid() + tiny)
return self.activate(acts + stddev * gnp.randn(*acts.shape))
def ABISS(data_path, num_ensembles, model_name, method = 'stddev', train = True, random_training_order = False):
algo_name = method + ' ' + 'ABISS'
raw_data = reader.ptb_raw_data(data_path)
train_data, valid_data, test_data, _, word_to_id = raw_data
eos_id = word_to_id['<eos>']
case_weight_length = len(train_data)-1
train_case_weights = np.repeat(1.0/case_weight_length, case_weight_length).tolist()
train_sentence_list = reader.get_sentence_list(train_data, eos_id)
if random_training_order:
perm = range(len(train_sentence_list))
np.random.shuffle(perm)
train_data = []
for idx in perm:
train_data += train_sentence_list[idx]
train_sentence_list = reader.get_sentence_list(train_data, eos_id)
num_sent = len(train_sentence_list)
train_sentence_weights = np.repeat(1.0/num_sent, num_sent).tolist()
new_train_data = reader.weighted_sentence_selection(train_sentence_list, train_sentence_weights, random_training_order)
FLAGS.model = model_name
config = get_config()
eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1
alpha_t_list = []
full_test_set_logits = []
for i in range(len(test_data)-1):
full_test_set_logits.append(np.zeros((1,eval_config.vocab_size)))
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.33)
for iii in range(num_ensembles):
with tf.Graph().as_default(), tf.Session(config = tf.ConfigProto(gpu_options = gpu_options)) as session:
initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.variable_scope("model", reuse=None, initializer=initializer):
sess = tf.Session(config = tf.ConfigProto(gpu_options = gpu_options))
m = PTBModel(is_training=True, config=config)
sess.close()
with tf.variable_scope("model", reuse=True, initializer=initializer):
mvalid = PTBModel(is_training=False, config=config)
mtest = PTBModel(is_training=False, config=eval_config)
#config.batch_size = 1
m2 = PTBModel(is_training=False, config=eval_config)
tf.initialize_all_variables().run()
saver = tf.train.Saver()
if random_training_order:
new_folder = 'random training order' + algo_name + '_' + model_name + '/' + 'ensemble' + str(iii + 1)
else:
new_folder = algo_name + '_' + model_name + '/' + 'ensemble' + str(iii + 1)
checkpoint_dir = 'simple-examples/ckpt/' + new_folder + '/'
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if train:
np.savetxt(checkpoint_dir + 'train_case_weights.out', train_case_weights, delimiter = ',')
np.savetxt(checkpoint_dir + 'train_sentence_weights.out', train_sentence_weights, delimiter = ',')
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
m.assign_lr(session, config.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
train_perplexity = run_epoch(session, m, new_train_data, m.train_op, verbose=False)
print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
valid_perplexity = run_epoch(session, mvalid, valid_data, tf.no_op())
print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))
if (i+1) % 5 == 0 or (i+1) == config.max_max_epoch:
saver.save(session, checkpoint_dir + 'model.ckpt', global_step = i+1)
else:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(session, ckpt.model_checkpoint_path)
case_scores = output_training_set_error_for_boosting(session, m2, train_data, tf.no_op())
score = sum(case_scores)
norm = len(case_scores) * -1 * gpu.log(float(1.0) / eval_config.vocab_size)
epsilon_t = (1 - (norm - score) / norm) / 2.0
alpha_t = 0.5 * gpu.log((1 - epsilon_t)/ epsilon_t)
alpha_t_list.append(alpha_t)
if iii == 0:
shutil.rmtree('simple-examples/ckpt/' + algo_name + '_' + model_name + '/' + 'alpha_t.out', ignore_errors = True)
with open('simple-examples/ckpt/' + algo_name + '_' + model_name + '/' + 'alpha_t.out', 'ab') as f:
f.write(str(alpha_t))
f.write(',')
train_case_weights = gpu.sqrt((1 - epsilon_t)/ epsilon_t) * np.multiply(train_case_weights, np.asarray(case_scores))
train_case_weights = np.ravel(normalize(np.asarray(train_case_weights).reshape(1,-1), norm = 'l1'))
if method == 'stddev':
new_train_case_weights = reject_outliers(train_case_weights)
elif method == 'sqrt':
new_train_case_weights = sqrt_norm(train_case_weights)
start_idx = 0
for i in range(len(train_sentence_list)):
this_sentence_length = len(train_sentence_list[i])
sentence_tokens = [v for v in new_train_case_weights[start_idx:(this_sentence_length+start_idx)] if np.isfinite(v)]
if len(sentence_tokens) == 0:
this_sentence_weights[i] = 0
else:
train_sentence_weights[i] = np.mean([v for v in new_train_case_weights[start_idx:(this_sentence_length+start_idx)] if np.isfinite(v)])
start_idx += this_sentence_length
train_sentence_weights = np.ravel(normalize(np.asarray(train_sentence_weights).reshape(1,-1), norm = 'l1'))
new_train_data = reader.weighted_sentence_selection(train_sentence_list, train_sentence_weights, random_training_order)
test_set_probs = output_test_set_probs(session, mtest, test_data, tf.no_op())
for i in range(len(test_set_probs)):
test_set_probs[i] = test_set_probs[i] * alpha_t
full_test_set_logits[i] += test_set_probs[i]
test_perplexity = run_epoch(session, mtest, test_data, tf.no_op())
print("Test Perplexity: %.3f" % test_perplexity)
alpha_t_sum = np.sum(alpha_t_list)
for i in range(len(full_test_set_logits)):
full_test_set_logits[i] = full_test_set_logits[i] / alpha_t_sum
ensemble_perplexity = classify_ensemble(test_data, full_test_set_logits, 1, 1)
print(ensemble_perplexity)
def compute_kernel_matrix(self, x):
x = x if isinstance(x, gnp.garray) else gnp.garray(x)
x_norm = gnp.sqrt((x**2).sum(axis=1))
x_norm = x_norm[:,gnp.newaxis] + x_norm[gnp.newaxis,:] + 1e-20
return x.dot(x.T) / x_norm
def output_and_cost(self, epoch, set_name='train'):
self.timer_logger('output_and_cost {0}'.format(set_name), time.time())
self.results['current'] = self.output(self.results['current'])
if self.problem == 'regression' and self.clip_values == 1:
# clip values into the [0,1] range
self.results['current'] = (self.results['current'] *
(self.results['current'] >= 0))
self.results['current'] = ((
(self.results['current'] < 1) * self.results['current']) +
(self.results['current'] > 1))
if set_name != 'train':
if set_name == 'no_label_test':
if 'prediction_test' not in self.results:
if self.problem == 'classification':
self.results['prediction_test'] = np.argmax(
self.results['current'].as_numpy_array(), axis=1)
else:
self.results['prediction_test'] = self.results[
'current'].as_numpy_array()
else:
if self.problem == 'classification':
self.results['prediction_test'] = np.hstack([
self.results['prediction_test'],
np.argmax(self.results['current'].as_numpy_array(),
axis=1)
])
else:
self.results['prediction_test'] = np.vstack([
self.results['prediction_test'],
self.results['current'].as_numpy_array()
])
elif set_name == 'cv_predict':
if self.create_cv_predictions and set_name == 'cv_predict':
if 'prediction_cv' not in self.results:
self.results['prediction_cv'] = self.results[
'current'].as_numpy_array()
else:
self.results['prediction_cv'] = np.vstack([
self.results['prediction_cv'],
self.results['current'].as_numpy_array()
])
else:
if self.problem == 'classification':
self.set_error_by_epoch[set_name][epoch] += (np.sum(
np.equal(
np.argmax(self.results['current'].as_numpy_array(),
axis=1), self.batch_y.T)))
else:
self.set_error_by_epoch[set_name][epoch] += gpu.sqrt(
gpu.sum(((self.results['current'] - self.batch_y)**2) *
float(self.batch.shape[0])) /
float(self.y.shape[1]))
if self.cost == 'auc':
if self.problem == 'regression':
if set_name + ' roc_auc' not in self.results:
self.results[set_name + ' roc_auc'] = ([
np.matrix(self.results['current'].
as_numpy_array()).T,
np.matrix(self.batch_y).T
])
else:
self.results[set_name + ' roc_auc'] = [
np.hstack([
self.results[set_name + ' roc_auc'][0],
np.matrix(self.results['current'].
as_numpy_array()).T
]),
np.hstack([
self.results[set_name + ' roc_auc'][1],
np.matrix(self.batch_y).T
])
]
else:
if set_name + ' roc_auc' not in self.results:
self.results[set_name + ' roc_auc'] = ([
np.matrix(
self.results['current'].as_numpy_array()
[:, 1]).T,
np.matrix(self.batch_y)
])
else:
self.results[set_name + ' roc_auc'] = [
np.vstack([
np.matrix(self.results[set_name +
' roc_auc'][0]),
np.matrix(self.results['current'].
as_numpy_array()[:, 1]).T
]),
np.vstack([
self.results[set_name + ' roc_auc'][1],
np.matrix(self.batch_y)
])
]
self.timer_logger('output_and_cost {0}'.format(set_name), time.time())
