def fpropDropout(self, inputBatch, weightsToStopBefore = None ):
"""
Perform a (possibly partial) forward pass through the
network. Updates self.state which, on a full forward pass,
holds the input followed by each hidden layer's activation and
finally the net input incident on the output layer. For a full
forward pass, we return the actual output unit activations. In
a partial forward pass we return None.
"""
inputBatch = inputBatch if isinstance(inputBatch, gnp.garray) else gnp.garray(inputBatch)
if weightsToStopBefore == None:
weightsToStopBefore = len(self.weights)
#self.state holds everything before the output nonlinearity, including the net input to the output units
sample = (gnp.rand(*inputBatch.shape) > self.dropouts[0])
self.state = [inputBatch * sample]
for i in range(min(len(self.weights) - 1, weightsToStopBefore)):
dropoutMultiplier = 1.0/(1.0-self.dropouts[i])
curActs = self.hidActFuncts[i].activation(gnp.dot(dropoutMultiplier*self.state[-1], self.weights[i]) + self.biases[i])
sample = (gnp.rand(*curActs.shape) > self.dropouts[i+1])
self.state.append(curActs * sample)
if weightsToStopBefore >= len(self.weights):
dropoutMultiplier = 1.0/(1.0-self.dropouts[-1])
self.state.append(gnp.dot(dropoutMultiplier*self.state[-1], self.weights[-1]) + self.biases[-1])
self.acts = self.outputActFunct.activation(self.state[-1])
return self.acts
#we didn't reach the output units
# To return the first set of hidden activations, we would set
# weightsToStopBefore to 1.
return self.state[weightsToStopBefore]
def R_forward_pass(self, state, R):
"""
Apply the R-operator on RNN. R is an RNN object which represents the
vector we multiply by. Note that it needs to know the RNN's state, so
that it doesn't have to unnecessarily recompute the state.
"""
V, H, OX = state
if V[0] is not None:
V = [None] + V
assert V[0] is None
T = len(V)-1
batch_size = len(V[1])
R_OX, R_HX = [[None]*(T+1) for _ in range(2)]
import numpy as np
R_H_t = g.tile(R.h_init, (batch_size, 1))
for t in range(1, T+1):
R_H_1t = R_H_t
R_HX[t] = g.dot(R_H_1t, self.W_hh) + g.dot(H[t-1], R.W_hh) + g.dot(V[t], R.W_vh)
R_H_t = self.hid_nonlin.grad_y(H[t]) * R_HX[t]
R_OX[t] = g.dot(H[t], R.W_ho) + g.dot(R_H_t, self.W_ho)
# \/---(for the structured reg).
return (R_HX, R_OX[1:])
def backprop(self):
self.timer_logger('backprop', time.time())
self.results['grads'] = []
self.results['bias_grads'] = []
if self.problem == 'classification':
#assumes softmax + cross entropy so that both gradients cancel out to give: error = y-t
self.results['error'] = self.results['current'] - gpu.garray(
self.util.create_t_dataset(self.batch_y))
else:
#assumes linear unit + squared error cost function so that both gradients cancel out to give: error = y-t
self.results['error'] = (self.results['current'] -
gpu.garray(self.batch_y))
for pair in self.results['activations']:
activation = pair[0]
weight = pair[1]
gradient = self.activation_gradient(activation)
self.results['grads'].insert(
0, gpu.dot(activation.T, self.results['error']))
self.results['bias_grads'].insert(
0,
gpu.dot(gpu.ones((1, self.results['error'].shape[0])),
self.results['error']))
self.results['error'] = gpu.dot(self.results['error'],
weight.T) * gradient
self.timer_logger('backprop', time.time())
def forward_pass(self, batch, O=None):
if len(batch)==2 and type(batch)==tuple:
V,O=batch
assert len(V)==len(O)
elif len(batch)==3 and type(batch)==tuple:
V,O,M=batch
assert len(V)==len(O)==len(M)
else:
V=batch
if V[0] is not None:
V = [None] + V
T = len(V)-1
batch_size = len(V[1])
A, B, H, OX = [[None]*(T+1) for _ in range(4)]
H[0] = g.tile(self.h_init, (batch_size, 1))
for t in range(1, T+1):
B[t] = g.dot(V[t], self.W_vf).tanh()
A[t] = g.dot(H[t-1], self.W_hf)
C_t = g.dot(V[t], self.W_vh) # + hh stuff
AB = A[t]*(B[t] + self.f_bias)
HX_t = g.dot(AB, self.W_fh) + C_t
H[t] = self.hid_nonlin(HX_t)
OX[t] = g.dot(H[t], self.W_ho)
return (V[1:], A, B, H, OX[1:])
def costfunc_gpu_ReLU(x, *args):
num_input,num_hidden,num_output,inputs,lambda_val,sparsityParam,beta = args
num_weights1 = (num_input+1)*num_hidden
x = gpu.garray(x)
inputs = gpu.garray(inputs)
#weights1 = gpu.garray(reshape(x[0:num_weights1],(num_hidden,num_input+1)))
weights1 = x[0:num_weights1].reshape((num_hidden,num_input+1))
#weights2 = gpu.garray(reshape(x[num_weights1:shape(x)[0]], (num_output,num_hidden+1)))
weights2 = x[num_weights1:shape(x)[0]].reshape((num_output,num_hidden+1))
nData = shape(inputs)[1]
data = gpu.concatenate((gpu.ones((1,nData)), inputs), axis = 0)
hidden_sum = gpu.dot(weights1, data)
hidden_activation = gpu.log(1+hidden_sum.exp())
p_avg = gpu.sum(hidden_activation,axis=1)/nData
hidden_activation = gpu.concatenate((gpu.ones((1,nData)), hidden_activation), axis = 0)
output = gpu.dot(weights2, hidden_activation)
regularized_penalty1 = weights1[:,1:shape(weights1)[1]]
regularized_penalty2 = weights2[:,1:shape(weights2)[1]]
regularized_penalty1 = regularized_penalty1 * regularized_penalty1
regularized_penalty2 = regularized_penalty2 * regularized_penalty2
output_target_diff = (output - inputs)*(output - inputs)
KL = gpu.sum(sparsityParam*gpu.log(sparsityParam/p_avg) + (1-sparsityParam)*gpu.log((1-sparsityParam)/(1-p_avg)))
cost = gpu.sum(output_target_diff)/(2*nData) + 0.5 * lambda_val * (gpu.sum(regularized_penalty1) + gpu.sum(regularized_penalty2)) + beta*KL
print 'ReLU Linear Decoder Cost: ', cost
return cost
def dbn_forward_pass(ws_vh, ws_v, ws_h, x, y=None):
"""
Deep belief net forward pass.
x: input data (N x D matrix)
y: Class label (1-of-K coded, N x K matrix). If not None, it is concatenated
to the input for top layer RBM when calculating the output of the DBN.
ws_vh: list of layer weights (L x D x H)
ws_v: list of layer input biases (L x D x 1)
ws_h: list of layer output biases (L x H x 1)
Returns activations (continuous) and outputs (0-1, sigmoid(activations)) of
top layer
"""
L = len(ws_vh)
h = x.T
# forward (bottom-up) pass
for l in range(L - 1):
ah = gnp.dot(ws_vh[l].T, h) + ws_h[l]
h = gnp.logistic(ah)
# if supervised, concatenate class labels to input to top layer RBM
if y is not None:
h = gnp.concatenate((y.T, h))
ah = gnp.dot(ws_vh[-1].T, h) + ws_h[-1]
h = gnp.logistic(ah)
return ah.T, h.T
def exact_fisher_information_biases(rbm, batch_units=10, show_progress=False):
batch_size = 2 ** batch_units
nvis, nhid = rbm.nvis, rbm.nhid
num_params = nvis + nhid
s = gnp.zeros(num_params)
G = gnp.zeros((num_params, num_params))
for hid, p in iter_configurations(rbm, batch_units=batch_units, show_progress=show_progress):
g = gnp.zeros((batch_size, num_params))
cond_vis = gnp.logistic(rbm.vis_inputs(hid))
g[:, :nvis] = cond_vis
g[:, nvis:] = hid
s += gnp.dot(p, g)
G += gnp.dot(g.T * p, g)
diag_term = gnp.dot(p, g * (1. - g))
G += np.diag(diag_term.as_numpy_array())
G -= s[:, nax] * s[nax, :]
return G
def forward(self, X, test=False):
"""
Feed-forward pass through the model
X: ('batchsize' x 'context') matrix of word indices
"""
batchsize = X.shape[0]
R = self.R
C = self.C
bw = self.bw
# Obtain word features
tmp = R.as_numpy_array()[:, X.flatten()].flatten(order='F')
tmp = tmp.reshape((batchsize, self.K * self.context))
words = np.zeros((batchsize, self.K, self.context))
for i in range(batchsize):
words[i, :, :] = tmp[i, :].reshape((self.K, self.context),
order='F')
words = gpu.garray(words)
# Compute the hidden layer (predicted next word representation)
acts = gpu.zeros((batchsize, self.K))
for i in range(self.context):
acts = acts + gpu.dot(words[:, :, i], C[i, :, :])
acts = gpu.concatenate((acts, gpu.ones((batchsize, 1))), 1)
# Compute softmax
preds = gpu.dot(acts, gpu.concatenate((R, bw)))
preds = gpu.exp(preds - preds.max(1).reshape(batchsize, 1))
denom = preds.sum(1).reshape(batchsize, 1)
preds = gpu.concatenate((preds / denom, gpu.ones((batchsize, 1))), 1)
return (words, acts, preds.as_numpy_array())
def backward(self, Y, preds, acts, words, X):
"""
Backward pass through the network
"""
batchsize = preds.shape[0]
# Compute part of df/dR
Ix = gpu.garray(preds[:, :-1] - Y) / batchsize
delta = gpu.dot(acts.T, Ix)
dR = delta[:-1, :] + self.gamma_r * self.R
db = delta[-1, :]
dR = dR.as_numpy_array()
# Compute df/dC and word inputs for df/dR
Ix = gpu.dot(Ix, self.R.T)
dC = gpu.zeros(np.shape(self.C))
for i in range(self.context):
delta = gpu.dot(words[:, :, i].T, Ix)
dC[i, :, :] = delta + self.gamma_c * self.C[i, :, :]
delta = gpu.dot(Ix, self.C[i, :, :].T)
delta = delta.as_numpy_array()
for j in range(X.shape[0]):
dR[:, X[j, i]] = dR[:, X[j, i]] + delta.T[:, j]
self.dR = gpu.garray(dR)
self.db = db
self.dC = dC
def backprop(self, X, y_target) :
# forward
activity = []
result = X
for i in range(len(self.weights)):
p = self.dropout_probability[i]
mask = (g.rand(result.shape) >= p)
result = result * mask
del mask
activity.append(result)
w,b = self.weights[i]
result = g.dot(result,w) + b
result = self.activation[i](result)
# backward
gradientNodes = []
lastGradient = self.gradient[-1](result, y_target)
gradientNodes.append(lastGradient)
for i in reversed(range(1,len(self.weights))):
w,b = self.weights[i]
lastGradient = g.dot(lastGradient, w.T) * self.gradient[i-1](activity[i])
gradientNodes.append(lastGradient)
# get gradient
resultGradient = []
for i in range(len(self.weights)):
gradW = (g.dot(activity[i].T,gradientNodes[-(i+1)]) / len(X))
assert(gradW.shape == self.weights[i][0].shape)
gradB = (g.sum(gradientNodes[-(i+1)],axis=0) / len(X))
assert(gradB.shape == self.weights[i][1].shape)
resultGradient.append([gradW,gradB])
del gradientNodes
return resultGradient
def bprop(self, outputErrSignal, MLerr, fpropState=None):
"""
Perform a backward pass through the network. fpropState
defaults to self.state (set during fprop) and outputErrSignal
should be self.outputActFunct.dErrordNetInput(...).
"""
# Manifold learning
ml_sense = [None for i in range(len(self.weights))]
pivt_sense = [None for i in range(len(self.weights))]
ml_sense[-1] = MLerr * self.actsML * (1 - self.actsML)
pivt_sense[-1] = outputErrSignal - MLerr * self.actsMLpvt * (
1 - self.actsMLpvt)
for i in reversed(range(len(self.weights) - 1)):
ml_sense[i] = gnp.dot(
ml_sense[i + 1],
self.weights[i + 1].T) * self.hidActFuncts[i].dEdNetInput(
self.stateML[i + 1])
pivt_sense[i] = gnp.dot(pivt_sense[i + 1], self.weights[
i + 1].T) * self.hidActFuncts[i].dEdNetInput(self.pivt[i + 1])
return ml_sense, pivt_sense
def backward(self, dEdY):
N = dEdY.shape[0]
S = self.windowSize
T = dEdY.shape[1] + S - 1
F = dEdY.shape[2]
D = self.X.shape[2]
dEdY = dEdY.reshape(N * (T - S + 1), F)
dEdX = np.zeros(self.X.shape, self.X.dtype)
if self.gpu:
gdEdY = gpu.as_garray(dEdY.astype('float32'))
self.dEdW = gpu.dot(self.Z.transpose(), gdEdY)
else:
self.dEdW = np.dot(self.Z.transpose(), dEdY)
if self.outputdEdX:
if self.gpu:
gdEdZ = gpu.dot(gdEdY, self.W.transpose())
dEdZ = gpu.as_numpy_array(gdEdZ)
else:
dEdZ = np.dot(dEdY, self.W.transpose())
dEdZ = dEdZ.reshape(N, T - S + 1, S, D)
for t in range(0, T):
if t <= S - 1:
dEdX[:, t, :] = np.sum(dEdZ[:, range(0, t + 1), range(t, -1, -1), :], axis=1)
elif t >= T - S + 1:
dEdX[:, t, :] = np.sum(dEdZ[:, range(t - S + 1, T - S + 1), range(S - 1, S - (T - t) - 1, -1), :], axis=1)
else:
dEdX[:, t, :] = np.sum(dEdZ[:, range(t - S + 1, t + 1), range(S - 1, -1, -1), :], axis=1)
return dEdX
def backward_pass(self, state, dOX, compute_grad2 = False):
grad = self.unpack(self.pack() * 0)
if compute_grad2:
grad2 = self.unpack(self.pack() * 0)
else:
grad2 = None
dY = dOX
for i in reversed(range(len(self.sizes) - 1)):
dX = self.nonlins[i].grad_y(state[i + 1]) * dY
X = state[i]
#state[i + 1] = self.hid_nonlin(g.dot(X, self.W[i]) + self.b[i])
grad.b[i] += dX.sum(0)
grad.W[i] += g.dot(X.T, dX)
if compute_grad2:
grad2.b[i] += (dX*dX).sum(0)
grad2.W[i] += g.dot((X*X).T, dX*dX)
## backprop the gradient:
if i > 0: # typically the first multiplication is the costliest.
dY = g.dot(dX, self.W[i].T)
return grad, grad2
def rbm_sample(w_vh, w_v, w_h, x, k=1, clamped=None):
"""
Sample from RBM with k steps of Gibbs sampling
w_vh: Weights between visible and hidden units (matrix of size DxH)
w_v: Visible unit biases (column vector of size Dx1)
w_h: Hidden unit biases (column vector of size Hx1)
x: Input (column vector of size DxN)
k: Number of Gibbs steps. Default is 1.
clamped: If not None, keeps the given elements of x clamped (constant)
while sampling
clamped is a two-tuple that gives the start and end indices of clamped elements
Returns hidden unit and visible unit activations (matrices of size HxN, DxN)
"""
if clamped is not None:
cx = x[clamped[0] : clamped[1], :]
v = x
for i in range(k):
# sample hiddens
ah = gnp.dot(w_vh.T, v) + w_h
h = gnp.logistic(ah)
hs = h > gnp.rand(h.shape[0], h.shape[1])
# sample visibles
av = gnp.dot(w_vh, hs) + w_v
v = gnp.logistic(av)
if clamped is not None:
v[clamped[0] : clamped[1], :] = cx
return h, v
def fprop(self, inputBatch, weightsToStopBefore=None):
"""
Perform a (possibly partial) forward pass through the
network. Updates self.state which, on a full forward pass,
holds the input followed by each hidden layer's activation and
finally the net input incident on the output layer. For a full
forward pass, we return the actual output unit activations. In
a partial forward pass we return None.
"""
inputBatch = inputBatch if isinstance(
inputBatch, gnp.garray) else gnp.garray(inputBatch)
if weightsToStopBefore == None:
weightsToStopBefore = len(self.weights)
#self.state holds everything before the output nonlinearity, including the net input to the output units
self.state = [inputBatch]
for i in range(min(len(self.weights) - 1, weightsToStopBefore)):
curActs = self.hidActFuncts[i].activation(
gnp.dot(self.state[-1], self.weights[i]) + self.biases[i])
self.state.append(curActs)
if weightsToStopBefore >= len(self.weights):
self.state.append(
gnp.dot(self.state[-1], self.weights[-1]) + self.biases[-1])
self.acts = self.outputActFunct.activation(self.state[-1])
return self.acts
#we didn't reach the output units
# To return the first set of hidden activations, we would set
# weightsToStopBefore to 1.
return self.state[weightsToStopBefore]
def forward(self, X, test=False):
"""
Feed-forward pass through the model
X: ('batchsize' x 'context') matrix of word indices
"""
batchsize = X.shape[0]
R = self.R
C = self.C
bw = self.bw
# Obtain word features
tmp = R.as_numpy_array()[:,X.flatten()].flatten(order='F') # flatten(), default in row-major order, order='F' means Fortran(column-major) order
tmp = tmp.reshape((batchsize, self.K * self.context)) # reshape(), in row-major order
words = np.zeros((batchsize, self.K, self.context))
for i in range(batchsize):
words[i,:,:] = tmp[i,:].reshape((self.K, self.context), order='F')
words = gpu.garray(words)
# Compute the hidden layer (predicted next word representation)
acts = gpu.zeros((batchsize, self.K))
for i in range(self.context):
acts = acts + gpu.dot(words[:,:,i], C[i,:,:]) # the dot() of 2-D matrix is equiverlent to multiply
acts = gpu.concatenate((acts, gpu.ones((batchsize, 1))), 1)
# Compute softmax
preds = gpu.dot(acts, gpu.concatenate((R, bw)))
preds = gpu.exp(preds - preds.max(1).reshape(batchsize, 1))
denom = preds.sum(1).reshape(batchsize, 1)
preds = gpu.concatenate((preds / denom, gpu.ones((batchsize, 1))), 1)
return (words, acts, preds.as_numpy_array())
def backward(self, Y, preds, acts, words, X):
"""
Backward pass through the network
"""
batchsize = preds.shape[0]
# Compute part of df/dR
Ix = gpu.garray(preds[:,:-1] - Y) / batchsize
delta = gpu.dot(acts.T, Ix)
dR = delta[:-1,:] + self.gamma_r * self.R
db = delta[-1,:]
dR = dR.as_numpy_array()
# Compute df/dC and word inputs for df/dR
Ix = gpu.dot(Ix, self.R.T)
dC = gpu.zeros(np.shape(self.C))
for i in range(self.context):
delta = gpu.dot(words[:,:,i].T, Ix)
dC[i,:,:] = delta + self.gamma_c * self.C[i,:,:]
delta = gpu.dot(Ix, self.C[i,:,:].T)
delta = delta.as_numpy_array()
for j in range(X.shape[0]):
dR[:,X[j,i]] = dR[:,X[j,i]] + delta.T[:,j]
self.dR = gpu.garray(dR)
self.db = db
self.dC = dC
def get_all_dists(self, query_id):
v = self.index[query_id] # normalized
if self._metric == 'angular':
dists = -gnumpy.dot(self.index, v)
elif self._metric == 'euclidean':
dists = self.lengths - 2 * gnumpy.dot(self.index, v)
return dists.as_numpy_array()
def get_output(self, input):
past = []
# append past_input
if self.input is not None:
past.append(self.input)
# set input
self.input = input
# append past_hidden
if self.hidden is not None:
past.append(self.hidden)
# set hidden
if self.hidden is None:
self.hidden = np.zeros(self.n_hidden)
#print self.hidden
self.hidden = self.a_hidden(gnp.dot(self.u.T, self.input) +
gnp.dot(self.w.T, self.hidden))
# set output
#print "dot", np.dot(self.v.T, self.hidden)
self.output = self.a_output(gnp.dot(self.v.T, self.hidden))
# append past_data (store (self.trun + 1) past's)
if len(past) != 0:
self.past_data.append(past)
if len(self.past_data) > (self.trun + 1):
self.past_data.pop(0)
return self.output
def _cd_update_terms(self, vis, model_vis, model_p_vis):
"""Returns (weights update, visible bias update, hidden bias update) given
visible states from the data vis, visible states sampled from the
model model_vis and the probability of the visible units being active
from the model."""
#print "vis.shape: ", vis.shape
#print "p_hid(vis).shape: ", self.p_hid(vis).shape
#print "model_p_vis.shape: ", model_p_vis.shape
#print "p_hid(model_p_vis).shape: ", self.p_hid(model_p_vis).shape
# my update rule:
#dweights = (gp.dot(vis.T, self.p_hid(vis)) -
# gp.dot(model_p_vis.T, self.p_hid(model_vis)))
#dbias_vis = gp.sum(vis, axis=0) - gp.sum(model_p_vis, axis=0)
#dbias_hid = (gp.sum(self.p_hid(vis), axis=0) -
# gp.sum(self.p_hid(model_vis), axis=0))
# deep learning update rule:
dweights = (gp.dot(vis.T, self.p_hid_given_vis(vis)) -
gp.dot(model_vis.T, self.p_hid_given_vis(model_vis)))
dbias_vis = gp.sum(vis, axis=0) - gp.sum(model_vis, axis=0)
dbias_hid = (gp.sum(self.p_hid_given_vis(vis), axis=0) -
gp.sum(self.p_hid_given_vis(model_vis), axis=0))
n_samples = vis.shape[0]
return (dweights / n_samples,
dbias_vis / n_samples,
dbias_hid / n_samples)
def mlpSingleOutput1Layer_costfunc(x, *args):
inputSize, l1Size, lambda_hidden, inputs, targets = args
numCases = shape(inputs)[1]
num_weights_L1 = l1Size * (inputSize + 1)
inputs = gpu.garray(inputs)
targets = gpu.garray(targets)
theta_L1 = gpu.garray(reshape(x[0:num_weights_L1], (l1Size, inputSize + 1)))
theta_output = gpu.garray(reshape(x[num_weights_L1:shape(x)[0]], (1, l1Size+1)))
inputs = gpu.concatenate((gpu.ones((1,numCases)), inputs), axis = 0)
hidden_sum_L1 = gpu.dot(theta_L1, inputs)
hidden_activation_L1 = hidden_sum_L1.logistic()
hidden_activation_L1 = gpu.concatenate((gpu.ones((1,numCases)), hidden_activation_L1), axis = 0)
#hidden_activation_L1 = hidden_activation_L1 * dropout_prob
hidden_sum_output = gpu.dot(theta_output, hidden_activation_L1)
outputs = hidden_sum_output.logistic()
output_target_diff = (outputs - targets)**2
regularized_penalty_output = theta_output[:,1:shape(theta_output)[1]]
regularized_penalty_output = regularized_penalty_output * regularized_penalty_output
regularized_penalty_L1 = theta_L1[:,1:shape(theta_L1)[1]]
regularized_penalty_L1 = regularized_penalty_L1 * regularized_penalty_L1
cost = gpu.sum(output_target_diff)/(2*numCases) + 0.5 * lambda_hidden*(gpu.sum(regularized_penalty_L1)+gpu.sum(regularized_penalty_output))
print 'Multilayer Preceptron Cost:', cost
del inputs
del theta_L1
del hidden_sum_L1
del hidden_activation_L1
del regularized_penalty_output
del regularized_penalty_L1
gpu.free_reuse_cache()
return cost
def fpropDropout(self, inputBatch, weightsToStopBefore=None):
"""
Perform a (possibly partial) forward pass through the
network. Updates self.state which, on a full forward pass,
holds the input followed by each hidden layer's activation and
finally the net input incident on the output layer. For a full
forward pass, we return the actual output unit activations. In
a partial forward pass we return None.
"""
if weightsToStopBefore == None:
weightsToStopBefore = len(self.weights)
#self.state holds everything before the output nonlinearity, including the net input to the output units
self.state = [
inputBatch * (gnp.rand(*inputBatch.shape) > self.dropouts[0])
]
for i in range(min(len(self.weights) - 1, weightsToStopBefore)):
dropoutMultiplier = 1.0 / (1.0 - self.dropouts[i])
curActs = self.hidActFuncts[i].activation(
gnp.dot(dropoutMultiplier * self.state[-1], self.weights[i]) +
self.biases[i])
self.state.append(
curActs * (gnp.rand(*curActs.shape) > self.dropouts[i + 1]))
if weightsToStopBefore >= len(self.weights):
dropoutMultiplier = 1.0 / (1.0 - self.dropouts[-1])
self.state.append(
gnp.dot(dropoutMultiplier * self.state[-1], self.weights[-1]) +
self.biases[-1])
self.acts = self.outputActFunct.activation(self.state[-1])
def fprop_xf(self, inputBatch, weightsToStopBefore=None):
"""
Only used during feature dumping after the network has been trained.
Perform a (possibly partial) forward pass through the
network. Updates self.state which, on a full forward pass,
holds the input followed by each hidden layer's activation and
finally the net input incident on the output layer. Note that state does NOT constrain
the activation of the output layer. For a full
forward pass, we return the actual output unit activations. In
a partial forward pass we return None.
"""
if weightsToStopBefore == None:
weightsToStopBefore = len(self.weights)
#self.state holds everything before the output nonlinearity, including the net input to the output units
self.state = [inputBatch]
for i in range(min(len(self.weights) - 1, weightsToStopBefore)):
curActs = self.hidActFuncts[i].activation(
gnp.dot(self.state[-1], self.weights[i]) + self.biases[i])
self.state.append(curActs)
if weightsToStopBefore >= len(self.weights):
self.state.append(
gnp.dot(self.state[-1], self.weights[-1]) + self.biases[-1])
self.acts = self.outputActFunct.activation(self.state[-1])
return self.acts
#we didn't reach the output units
# To return the first set of hidden activations, we would set
# weightsToStopBefore to 1.
return self.state[weightsToStopBefore]
def update(self):
self.w *= self.l2reg
if self.dropout > 0:
self.w -= gpu.dot((self.x * self.r).T, self.d) * self.learn # / self.q
else:
self.w -= gpu.dot(self.x.T, self.d) * self.learn # / self.q
self.b *= self.l2reg
self.b -= gpu.sum(self.d, 0) * self.learn
def energy(self, vis, hid):
assert hid.ndim == 2
#return (vis * self.vbias[nax, :]).sum(1) + \
# (hid * self.hbias[nax, :]).sum(1) + \
# (vis[:, :, nax] * self.weights[nax, :, :] * hid[:, nax, :]).sum(2).sum(1)
return gnp.dot(vis, self.vbias) + \
gnp.dot(hid, self.hbias) + \
gnp.sum(vis * gnp.dot(hid, self.weights.T), 1)
def fobos_nn(self, w):
nu = self.tau * self.lr
u, s, vt = linalg.svd(w, full_matrices=0, compute_uv=1)
sdash = np.maximum(s - nu, 0)
sdashzeros = np.diag(sdash)
# sdashzeros = np.zeros(u.shape, dtype=np.float)
# sdashzeros[:sdashtemp.shape[0], :sdashtemp.shape[1]] = sdashtemp
return gnp.dot(gnp.garray(u), gnp.dot(gnp.garray(sdashzeros), gnp.garray(vt))).as_numpy_array(), s
def energy(self, vis, hid):
assert hid.ndim == 2
#return (vis * self.vbias[nax, :]).sum(1) + \
# (hid * self.hbias[nax, :]).sum(1) + \
# (vis[:, :, nax] * self.weights[nax, :, :] * hid[:, nax, :]).sum(2).sum(1)
return gnp.dot(vis, self.vbias) + \
gnp.dot(hid, self.hbias) + \
gnp.sum(vis * gnp.dot(hid, self.weights.T), 1)
def fobos_nn(self, w):
nu = self.tau * self.lr
u, s, vt = randomized_svd(w, w.shape[0])
sdash = np.maximum(s - nu, 0)
sdashtemp = np.diag(sdash)
sdashzeros = np.zeros(u.shape, dtype=np.float)
sdashzeros[:sdashtemp.shape[0], :sdashtemp.shape[1]] = sdashtemp
return gnp.dot(gnp.garray(u), gnp.dot(gnp.garray(sdashzeros), gnp.garray(vt))).as_numpy_array(), s
def run_gnumpy(a, b):
st_g = time()
len_a = gnumpy.dot(a, a)
len_b = gnumpy.dot(b, b)
res = gnumpy.dot(a, b) / (len_a * len_b)
et_g = time()
print res
return et_g - st_g
def grad_costfunc_gpu_ReLU(x, *args):
num_input, num_hidden, num_output, inputs, lambda_val, sparsityParam, beta = args
num_weights1 = (num_input + 1) * num_hidden
num_weights2 = (num_hidden + 1) * num_output
x = gpu.garray(x)
inputs = gpu.garray(inputs)
weights1 = x[0:num_weights1].reshape((num_hidden, num_input + 1))
weights2 = x[num_weights1:shape(x)[0]].reshape(
(num_output, num_hidden + 1))
nData = shape(inputs)[1]
data = gpu.concatenate((gpu.ones((1, nData)), inputs), axis=0)
hidden_sum = gpu.dot(weights1, data)
#hidden_activation = gpu.log(1+hidden_sum.exp())
relu_mask_hidden1 = gpu.ones(shape(hidden_sum)) * (hidden_sum > 0)
hidden_activation = hidden_sum * relu_mask_hidden1
#hidden_derivative = hidden_sum.logistic()
hidden_derivative = relu_mask_hidden1
hidden_activation = gpu.concatenate((gpu.ones(
(1, nData)), hidden_activation),
axis=0)
hidden_derivative = gpu.concatenate((gpu.ones(
(1, nData)), hidden_derivative),
axis=0)
outputs = gpu.dot(weights2, hidden_activation)
weights1_grad = gpu.zeros(shape(weights1))
weights2_grad = gpu.zeros(shape(weights2))
p = outputs - inputs
weights2_grad += gpu.dot(
p, gpu.garray(transpose(hidden_activation.as_numpy_array())))
q_temp = gpu.dot(gpu.garray(transpose(weights2.as_numpy_array())), p)
#q = multiply(multiply(q_temp,hidden_activation),(1-hidden_activation))
q = q_temp * hidden_derivative
delta2 = gpu.dot(q, gpu.garray(transpose(data.as_numpy_array())))
weights1_grad += delta2[1:shape(delta2)[0], :]
weights1_grad = weights1_grad / nData
weights2_grad = weights2_grad / nData
weights1_grad[:, 1:shape(weights1_grad)[1]] = weights1_grad[:, 1:shape(
weights1_grad)[1]] + weights1[:, 1:shape(weights1)[1]] * lambda_val
weights2_grad[:, 1:shape(weights2_grad)[1]] = weights2_grad[:, 1:shape(
weights2_grad)[1]] + weights2[:, 1:shape(weights2)[1]] * lambda_val
#weights1_grad = reshape(weights1_grad, num_weights1)
weights1_grad = weights1_grad.reshape(num_weights1)
#weights2_grad = reshape(weights2_grad, num_weights2)
weights2_grad = weights2_grad.reshape(num_weights2)
del x
del inputs
del data
del p
del q_temp
del q
del delta2
del hidden_sum
del hidden_activation
del weights1
del weights2
gpu.free_reuse_cache()
return hstack(
(weights1_grad.as_numpy_array(), weights2_grad.as_numpy_array()))
def mlpSoftmax1Layer_grad(x, *args):
numClasses, inputSize, l1Size, lambda_softmax, lambda_hidden, inputs, groundTruth = args
numCases = shape(inputs)[1]
num_weights_L1 = l1Size * (inputSize + 1)
num_weights_softmax = numClasses * l1Size
inputs = gpu.garray(inputs)
theta_L1 = gpu.garray(reshape(x[0:num_weights_L1],
(l1Size, inputSize + 1)))
theta_softmax = gpu.garray(
reshape(x[num_weights_L1:shape(x)[0]], (numClasses, l1Size)))
theta_L1_grad = gpu.zeros(shape(theta_L1))
inputs = gpu.concatenate((gpu.ones((1, numCases)), inputs), axis=0)
hidden_sum_L1 = gpu.dot(theta_L1, inputs)
#hidden_activation_L1 = gpu.log(1+hidden_sum_L1.exp())
#hidden_derivative_L1 = hidden_sum_L1.logistic()
relu_mask_hidden1 = gpu.ones(shape(hidden_sum_L1)) * (hidden_sum_L1 > 0)
hidden_activation_L1 = hidden_sum_L1 * relu_mask_hidden1
#hidden_activation_L1 = hidden_sum_L1.logistic()
hidden_derivative_L1 = relu_mask_hidden1
hidden_sum_softmax_imd = gpu.dot(theta_softmax, hidden_activation_L1)
hidden_sum_softmax = hidden_sum_softmax_imd - hidden_sum_softmax_imd.max(
axis=0)
predictions = hidden_sum_softmax.exp()
predictions = predictions / gpu.sum(predictions, axis=0)
softmax_imd = groundTruth - predictions
theta_softmax_grad = -1 * gpu.dot(
softmax_imd,
gpu.garray(transpose(hidden_activation_L1.as_numpy_array()))
) / numCases + lambda_softmax * theta_softmax
deltaOut = -softmax_imd
delta_L1_imd = gpu.dot(
gpu.garray(transpose(theta_softmax.as_numpy_array())), deltaOut)
delta_L1_imd2 = delta_L1_imd * hidden_derivative_L1
#delta_L1_imd2 = (delta_L1_imd*hidden_activation_L1)*(1-hidden_activation_L1)
delta_L1 = gpu.dot(delta_L1_imd2,
gpu.garray(transpose(inputs.as_numpy_array())))
theta_L1_grad += delta_L1
theta_L1_grad = theta_L1_grad / numCases
theta_L1_grad[:, 1:shape(theta_L1_grad)[1]] = theta_L1_grad[:, 1:shape(
theta_L1_grad)[1]] + theta_L1[:, 1:shape(theta_L1)[1]] * lambda_hidden
theta_L1_grad = reshape(theta_L1_grad.as_numpy_array(), num_weights_L1)
theta_softmax_grad = reshape(theta_softmax_grad.as_numpy_array(),
num_weights_softmax)
del inputs
del theta_L1
del theta_softmax
del hidden_sum_L1
del hidden_activation_L1
del hidden_sum_softmax
del predictions
del softmax_imd
del deltaOut
del delta_L1_imd
del delta_L1_imd2
del delta_L1
gpu.free_reuse_cache()
return hstack((theta_L1_grad, theta_softmax_grad))
def update(self):
self.w *= self.l2reg
if self.dropout > 0:
self.w -= gpu.dot(
(self.x * self.r).T, self.d) * self.learn # / self.q
else:
self.w -= gpu.dot(self.x.T, self.d) * self.learn # / self.q
self.b *= self.l2reg
self.b -= gpu.sum(self.d, 0) * self.learn
def input_to_hidden(self, set_name = 'train'):
self.timer_logger('input_to_hidden {0}'.format(type), time.time())
self.results['activations'] = []
if set_name == 'train':
self.results['activations'].append([self.batch, self.w[0], self.b[0]])
dropped_out = self.batch * (gpu.rand(self.current_batch_size,self.X.shape[1]) > self.dropout[0])
self.results['current'] = gpu.dot(dropped_out,self.w[0])+self.b[0]
else:
self.results['current'] = gpu.dot(self.batch,self.w[0]) + self.b[0]
self.timer_logger('input_to_hidden {0}'.format(type), time.time())
def mlpSoftmax_costfunc(x, *args):
numClasses, inputSize, l1Size, l2Size, lambda_softmax, lambda_hidden, inputs, labels, groundTruth = args
numCases = shape(inputs)[1]
num_weights_L1 = l1Size * (inputSize + 1)
num_weights_L2 = l2Size * (l1Size + 1)
#x = gpu.garray(x)
inputs = gpu.garray(inputs)
theta_L1 = gpu.garray(reshape(x[0:num_weights_L1],
(l1Size, inputSize + 1)))
#theta_L1 = x[0:num_weights_L1].reshape((l1Size, inputSize + 1))
#print numClasses, l2Size
theta_L2 = gpu.garray(
reshape(x[num_weights_L1:num_weights_L2 + num_weights_L1],
(l2Size, l1Size + 1)))
#theta_L2 = x[num_weights_L1:num_weights_L2+num_weights_L1].reshape((l2Size, l1Size + 1))
theta_softmax = gpu.garray(
reshape(x[num_weights_L2 + num_weights_L1:shape(x)[0]],
(numClasses, l2Size)))
#theta_softmax = x[num_weights_L2+num_weights_L1:shape(x)[0]].reshape((numClasses, l2Size))
inputs = gpu.concatenate((gpu.ones((1, numCases)), inputs), axis=0)
hidden_sum_L1 = gpu.dot(theta_L1, inputs)
hidden_activation_L1 = hidden_sum_L1.logistic()
hidden_activation_L1 = gpu.concatenate((gpu.ones(
(1, numCases)), hidden_activation_L1),
axis=0)
hidden_sum_L2 = gpu.dot(theta_L2, hidden_activation_L1)
hidden_activation_L2 = hidden_sum_L2.logistic()
hidden_sum_softmax = gpu.dot(theta_softmax, hidden_activation_L2)
hidden_sum_softmax = hidden_sum_softmax - hidden_sum_softmax.max(axis=0)
predictions = hidden_sum_softmax.exp()
predictions = predictions / gpu.sum(predictions, axis=0)
temp = groundTruth * gpu.log(predictions)
regularized_penalty_L1 = theta_L1[:, 1:shape(theta_L1)[1]]
regularized_penalty_L2 = theta_L2[:, 1:shape(theta_L2)[1]]
regularized_penalty_L1 = regularized_penalty_L1 * regularized_penalty_L1
regularized_penalty_L2 = regularized_penalty_L2 * regularized_penalty_L2
cost = -1 * gpu.sum(temp) / numCases + 0.5 * lambda_hidden * (
gpu.sum(regularized_penalty_L1) + gpu.sum(regularized_penalty_L2)
) + 0.5 * lambda_softmax * gpu.sum(theta_softmax * theta_softmax)
print 'Multilayer Softmax Cost:', cost
del inputs
del theta_L1
del theta_L2
del theta_softmax
del hidden_sum_L1
del hidden_activation_L1
del hidden_sum_L2
del hidden_activation_L2
del hidden_sum_softmax
del predictions
del temp
del regularized_penalty_L1
del regularized_penalty_L2
gpu.free_reuse_cache()
return cost
def feedforward(self, train_set_x):
self.activations = []
self.activations.append(train_set_x)
for i in range(self.n_layers):
current_activations = gnp.tanh(gnp.dot(self.activations[i], self.W_params[i]) + self.b_params[i])
self.activations.append(current_activations)
#output layers
self.final_layer_output = gnp.dot(self.activations[self.n_layers], self.W_params[self.n_layers]) + self.b_params[self.n_layers]
def parameter_prediction(self, test_set_x):
test_set_x = gnp.as_garray(test_set_x)
current_activations = test_set_x
for i in range(self.n_layers):
current_activations = gnp.tanh(gnp.dot(current_activations, self.W_params[i]) + self.b_params[i])
final_layer_output = gnp.dot(current_activations, self.W_params[self.n_layers]) + self.b_params[self.n_layers]
return final_layer_output.as_numpy_array()
def feedforward(self, train_set_x):
self.activations = []
self.activations.append(train_set_x)
for i in range(self.n_layers):
current_activations = gnp.tanh(gnp.dot(self.activations[i], self.W_params[i]) + self.b_params[i])
self.activations.append(current_activations)
#output layers
self.final_layer_output = gnp.dot(self.activations[self.n_layers], self.W_params[self.n_layers]) + self.b_params[self.n_layers]
def nn_forward_pass(x, w, b, return_all=True):
"""
Forward pass for multilayer feed-forward sigmoid neural network
Hidden units have sigmoid non-linearity.
Output is soft-max.
x: DxN matrix of input data
w: Weights. List of weight matrices for each layer.
b: Biases. List of bias vectors for each layer
return_all: If True, returns hidden unit activations for each layer. If False
just returns the output layer activations
Returns a list h where each element is a matrix containing the activations
for that layer. h[0] is input data x.
"""
# ---- TEMP HACK --------------
# I should find a more seamless way of running in mixed (some operations
# with numpy, some with gnumpy) mode.
# I had to resort to this, because i needed the validation classification
# step in nn_train to run on CPU with numpy. GPU ran out of memory.
if isinstance(x, gnp.garray):
use_gpu = True
else:
use_gpu = False
layer_count = len(w)
if return_all:
hs = [x] # unit activations for each layer
h = x
# all layers except the output layer
for l in range(layer_count - 1):
if use_gpu:
a = gnp.dot(w[l].T, h) + b[l]
h = gnp.logistic(a)
else:
a = np.dot(gnp.as_numpy_array(w[l]).T, h) + gnp.as_numpy_array(b[l])
h = 1.0 / (1 + np.exp(-a))
if return_all:
hs.append(h)
# output layer
if use_gpu:
h = gnp.dot(w[-1].T, h) + b[-1]
h = gnp.exp(h) / gnp.sum(gnp.exp(h), axis=0) # soft-max
else:
h = np.dot(gnp.as_numpy_array(w[-1]).T, h) + gnp.as_numpy_array(b[-1])
h = np.exp(h) / np.sum(np.exp(h), axis=0) # soft-max
if return_all:
hs.append(h)
return hs
else:
return h
def parameter_prediction(self, test_set_x):
test_set_x = gnp.as_garray(test_set_x)
current_activations = test_set_x
for i in range(self.n_layers):
current_activations = gnp.tanh(gnp.dot(current_activations, self.W_params[i]) + self.b_params[i])
final_layer_output = gnp.dot(current_activations, self.W_params[self.n_layers]) + self.b_params[self.n_layers]
return final_layer_output.as_numpy_array()
def backward_pass(self, state, dOX, R_HX=None, mu_times_lambda=0.):
"""
The backward pass (or the L-op). Given the gradients wrt the output
units and the state, compute the implied derivative wrt the parameters.
If R_HX is given, then structural damping will be added.
"""
V, H, OX = state
if V[0] is not None:
V = [None] + V
if OX[0] is not None:
OX = [None] + OX
if dOX[0] is not None:
dOX = [None] + dOX
assert V[0] is None
T = len(V)-1
grad = self.unpack(self.pack() * 0)
dH_1t = H[-1] * 0
for t in reversed(range(1, T+1)):
dH_t = dH_1t
dH_t += g.dot(dOX[t], self.W_ho.T)
grad.W_ho += g.dot(H[t].T, dOX[t])
## backpropagate the nonlinearity: at this point, dHX_t, the gradinet
## wrt the total inputs to H_t, is correct.
dHX_t = dH_t * self.hid_nonlin.grad_y(H[t])
## THIS IS THE ONLY LINE THAT HAS ANYTHING TO DO WITH STRUCTURAL
## DAMPING. Pretty cool :-)
if R_HX is not None:
dHX_t += float(mu_times_lambda) * \
self.struct_damp_nonlin.H_prod(R_HX[t], H[t], 1)
dH_1t = g.dot(dHX_t, self.W_hh.T)
grad.W_hh += g.dot(H[t-1].T, dHX_t)
grad.W_vh += g.dot(V[t].T, dHX_t)
grad.h_init += dH_1t.sum(0)
return grad
def dbn_supervised_predict_exact(ws_vh, ws_v, ws_h, x):
"""
Predict the class label of input x from supervised DBN
Uses the exact method mentioned in section 6.2 of Hinton, Osindero, Teh 2006
The free energy formula is taken from http://deeplearning.net/tutorial/rbm.html
x: Input data. (NxD matrix)
"""
L = len(ws_vh)
N = x.shape[0]
# make a forward pass to get from input layer to visible layer of top level
# RBM
h_prev = x.T
# forward (bottom-up) pass, (use deterministic (we pass the activations, not
# the stochastically sampled steps) forward pass)
for l in range(L - 1):
ah = gnp.dot(ws_vh[l].T, h_prev) + ws_h[l]
h_prev = gnp.logistic(ah)
H = ws_vh[-1].shape[0] # number of visible units top level RBM
Hx = h_prev.shape[0] # number of hidden units in the penultimate layer
K = H - Hx
# (H - Hx) is the number of supervised inputs to top level RBM
# for every class, assume it is the correct label and calculate its free energy
y = gnp.zeros((K, N))
free_energy = gnp.zeros((N, K)) # we actually calculate -free_energy
for k in range(K):
# set the current assumed class label
y[k, :] = 1.0
# visible unit vector
v = gnp.concatenate((y, h_prev))
e_v = gnp.dot(ws_v[-1].T, v) # bias energy term
ah = gnp.dot(ws_vh[-1].T, v) + ws_h[-1]
e_h = gnp.sum(gnp.log(gnp.exp(ah) + 1.0), axis=0)
free_energy[:, k] = e_v + e_h
# zero the class labels for next iteration
y[:, :] = 0.0
# since these numbers may get pretty small, use the sum-exp trick for converting
# these to probabilities
pred_y = (
gnp.exp(free_energy - gnp.max(free_energy, axis=1)[:, gnp.newaxis])
/ gnp.sum(gnp.exp(free_energy - gnp.max(free_energy, axis=1)[:, gnp.newaxis]), axis=1)[:, gnp.newaxis]
)
return pred_y
def exact_moments(rbm, batch_units=10, show_progress=False):
expect_vis = gnp.zeros(rbm.nvis)
expect_hid = gnp.zeros(rbm.nhid)
expect_prod = gnp.zeros((rbm.nvis, rbm.nhid))
for hid, p in iter_configurations(rbm, batch_units=batch_units, show_progress=show_progress):
cond_vis = gnp.logistic(rbm.vis_inputs(hid))
expect_vis += gnp.dot(p, cond_vis)
expect_hid += gnp.dot(p, hid)
expect_prod += gnp.dot(cond_vis.T * p, hid)
return binary_rbms.Moments(expect_vis, expect_hid, expect_prod)
def fprop(self, inputBatch, weightsToStopBefore = None ):
inputBatch = inputBatch if isinstance(inputBatch, gnp.garray) else gnp.garray(inputBatch)
if weightsToStopBefore == None:
weightsToStopBefore = len(self.weights)
self.state = [inputBatch]
for i in range(min(len(self.weights) - 1, weightsToStopBefore)):
curActs = self.hidActFuncts[i].activation(gnp.dot(self.state[-1], self.weights[i]) + self.biases[i])
self.state.append(curActs)
if weightsToStopBefore >= len(self.weights):
self.state.append(gnp.dot(self.state[-1], self.weights[-1]) + self.biases[-1])
self.acts = self.outputActFunct.activation(self.state[-1])
return self.acts
return self.state[weightsToStopBefore]
def CD1(vis, visToHid, visBias, hidBias, visUnit = Binary(), hidUnit = Binary()):
posHid = hidUnit.activate(gnp.dot(vis, visToHid) + hidBias)
posHidStates = hidUnit.sampleStates(posHid)
negVis = visUnit.activate(gnp.dot(posHidStates, visToHid.T) + visBias)
negHid = hidUnit.activate(gnp.dot(negVis, visToHid) + hidBias)
visHidStats = gnp.dot(vis.T, posHid) - gnp.dot(negVis.T, negHid)
visBiasStats = vis.sum(axis=0).reshape(*visBias.shape) - negVis.sum(axis=0).reshape(*visBias.shape)
hidBiasStats = posHid.sum(axis=0).reshape(*hidBias.shape) - negHid.sum(axis=0).reshape(*hidBias.shape)
return visHidStats, hidBiasStats, visBiasStats, negVis
def costfunc_gpu(x, *args):
num_input, num_hidden, num_output, inputs, noNoiseData, lambda_val, sparsityParam, beta = args
num_weights1 = (num_input + 1) * num_hidden
x = gpu.garray(x)
# randomNoise = random.random_sample(shape(inputs))
# criteriaTable = randomNoise > 0.32
# inputs = inputs * criteriaTable
inputs = gpu.garray(inputs)
noNoiseData = gpu.garray(noNoiseData)
#weights1 = gpu.garray(reshape(x[0:num_weights1],(num_hidden,num_input+1)))
weights1 = x[0:num_weights1].reshape((num_hidden, num_input + 1))
#weights2 = gpu.garray(reshape(x[num_weights1:shape(x)[0]], (num_output,num_hidden+1)))
weights2 = x[num_weights1:shape(x)[0]].reshape(
(num_output, num_hidden + 1))
nData = shape(inputs)[1]
data = gpu.concatenate((gpu.ones((1, nData)), inputs), axis=0)
hidden_sum = gpu.dot(weights1, data)
hidden_activation = hidden_sum.logistic()
p_avg = gpu.sum(hidden_activation, axis=1) / nData
hidden_activation = gpu.concatenate((gpu.ones(
(1, nData)), hidden_activation),
axis=0)
output = gpu.dot(weights2, hidden_activation)
regularized_penalty1 = weights1[:, 1:shape(weights1)[1]]
regularized_penalty2 = weights2[:, 1:shape(weights2)[1]]
regularized_penalty1 = regularized_penalty1 * regularized_penalty1
regularized_penalty2 = regularized_penalty2 * regularized_penalty2
output_target_diff = (output - noNoiseData) * (output - noNoiseData)
KL = gpu.sum(sparsityParam * gpu.log(sparsityParam / p_avg) +
(1 - sparsityParam) * gpu.log((1 - sparsityParam) /
(1 - p_avg)))
cost = gpu.sum(output_target_diff) / (2 * nData) + 0.5 * lambda_val * (
gpu.sum(regularized_penalty1) +
gpu.sum(regularized_penalty2)) + beta * KL
print 'GPU Linear Denoising Decoder Cost: ', cost
del x
del inputs
del noNoiseData
del data
del hidden_sum
del hidden_activation
del p_avg
del output
del regularized_penalty1
del regularized_penalty2
del weights1
del weights2
del output_target_diff
gpu.free_reuse_cache()
return cost
def grad_costfunc_gpu(x, *args):
num_input,num_hidden,num_output,inputs,lambda_val,sparsityParam,beta = args
num_weights1 = (num_input+1)*num_hidden
num_weights2 = (num_hidden+1)*num_output
x = gpu.garray(x)
inputs = gpu.garray(inputs)
weights1 = x[0:num_weights1].reshape((num_hidden,num_input+1))
weights2 = x[num_weights1:shape(x)[0]].reshape((num_output,num_hidden+1))
nData = shape(inputs)[1]
data = gpu.concatenate((gpu.ones((1,nData)), inputs), axis = 0)
hidden_sum = gpu.dot(weights1, data)
hidden_activation = hidden_sum.logistic()
p_avg = gpu.sum(hidden_activation,axis=1)/nData
grad_sparse = -1*sparsityParam/p_avg.as_numpy_array() + (1-sparsityParam)/(1-p_avg.as_numpy_array())
grad_sparse = append(0,grad_sparse)
grad_sparse = tile(grad_sparse, (nData, 1))
grad_sparse = gpu.garray(transpose(grad_sparse))
hidden_activation = gpu.concatenate((gpu.ones((1,nData)), hidden_activation), axis = 0)
outputs = gpu.dot(weights2, hidden_activation)
weights1_grad = gpu.zeros(shape(weights1))
weights2_grad = gpu.zeros(shape(weights2))
p = outputs-inputs
weights2_grad += gpu.dot(p, gpu.garray(transpose(hidden_activation.as_numpy_array())))
q_temp = gpu.dot(gpu.garray(transpose(weights2.as_numpy_array())),p) + beta*grad_sparse
#q = multiply(multiply(q_temp,hidden_activation),(1-hidden_activation))
q = (q_temp*hidden_activation)*(1-hidden_activation)
delta2 = gpu.dot(q, gpu.garray(transpose(data.as_numpy_array())))
weights1_grad += delta2[1:shape(delta2)[0], :]
weights1_grad = weights1_grad/nData
weights2_grad = weights2_grad/nData
weights1_grad[:,1:shape(weights1_grad)[1]] = weights1_grad[:,1:shape(weights1_grad)[1]] + weights1[:,1:shape(weights1)[1]] * lambda_val
weights2_grad[:,1:shape(weights2_grad)[1]] = weights2_grad[:,1:shape(weights2_grad)[1]] + weights2[:,1:shape(weights2)[1]] * lambda_val
#weights1_grad = reshape(weights1_grad, num_weights1)
weights1_grad = weights1_grad.reshape(num_weights1)
#weights2_grad = reshape(weights2_grad, num_weights2)
weights2_grad = weights2_grad.reshape(num_weights2)
del x
del inputs
del data
del grad_sparse
del p
del q_temp
del q
del delta2
del hidden_sum
del hidden_activation
del weights1
del weights2
gpu.free_reuse_cache()
return hstack((weights1_grad.as_numpy_array(),weights2_grad.as_numpy_array()))
def input_to_hidden(self, set_name='train'):
self.timer_logger('input_to_hidden {0}'.format(type), time.time())
self.results['activations'] = []
if set_name == 'train':
self.results['activations'].append(
[self.batch, self.w[0], self.b[0]])
dropped_out = self.batch * (gpu.rand(
self.current_batch_size, self.X.shape[1]) > self.dropout[0])
self.results['current'] = gpu.dot(dropped_out,
self.w[0]) + self.b[0]
else:
self.results['current'] = gpu.dot(self.batch,
self.w[0]) + self.b[0]
self.timer_logger('input_to_hidden {0}'.format(type), time.time())
def fpropDropout(self,
inputBatch,
useDropout=False,
weightsToStopBefore=None):
"""
Perform a (possibly partial) forward pass through the
network. Updates self.state which, on a full forward pass,
holds the input followed by each hidden layer's activation and
finally the net input incident on the output layer. For a full
forward pass, we return the actual output unit activations. In
a partial forward pass we return None.
If useDropout == True, ranomly drop units for each layer.
"""
inputBatch = inputBatch if isinstance(
inputBatch, gnp.garray) else gnp.garray(inputBatch)
if weightsToStopBefore == None:
weightsToStopBefore = len(self.weights)
self.keptMask = [gnp.rand(*inputBatch.shape) > self.dropouts[0]]
#self.state holds everything before the output nonlinearity, including the net input to the output units
self.state = [inputBatch * self.keptMask[0]]
for i in range(min(len(self.weights) - 1, weightsToStopBefore)):
if useDropout:
dropoutMultiplier = 1.0 / (1.0 - self.dropouts[i])
curActs = self.hidActFuncts[i].activation(
gnp.dot(dropoutMultiplier *
self.state[-1], self.weights[i]) + self.biases[i])
self.keptMask.append(
gnp.rand(*curActs.shape) > self.dropouts[i + 1])
self.state.append(curActs * self.keptMask[-1])
else:
curActs = self.hidActFuncts[i].activation(
gnp.dot(self.state[-1], self.weights[i]) + self.biases[i])
self.state.append(curActs)
if weightsToStopBefore >= len(self.weights):
if useDropout:
dropoutMultiplier = 1.0 / (1.0 - self.dropouts[-1])
self.state.append(
gnp.dot(dropoutMultiplier *
self.state[-1], self.weights[-1]) +
self.biases[-1])
else:
self.state.append(
gnp.dot(self.state[-1], self.weights[-1]) +
self.biases[-1])
self.acts = self.outputActFunct.activation(self.state[-1])
return self.acts
# If we didn't reach the output units
# To return the first set of hidden activations, we would set
# weightsToStopBefore to 1.
return self.state[weightsToStopBefore]
def fpropDropout(self, inputBatch, weightsToStopBefore = None ):
inputBatch = inputBatch if isinstance(inputBatch, gnp.garray) else gnp.garray(inputBatch)
if weightsToStopBefore == None:
weightsToStopBefore = len(self.weights)
self.state = [inputBatch * (gnp.rand(*inputBatch.shape) > self.dropouts[0])]
for i in range(min(len(self.weights) - 1, weightsToStopBefore)):
dropoutMultiplier = 1.0/(1.0-self.dropouts[i])
curActs = self.hidActFuncts[i].activation(gnp.dot(dropoutMultiplier*self.state[-1], self.weights[i]) + self.biases[i])
self.state.append(curActs * (gnp.rand(*curActs.shape) > self.dropouts[i+1]) )
if weightsToStopBefore >= len(self.weights):
dropoutMultiplier = 1.0/(1.0-self.dropouts[-1])
self.state.append(gnp.dot(dropoutMultiplier*self.state[-1], self.weights[-1]) + self.biases[-1])
self.acts = self.outputActFunct.activation(self.state[-1])
return self.acts
return self.state[weightsToStopBefore]
def hidden_to_output(self, set_name = 'train'):
self.timer_logger('hidden_to_output {0}'.format(type), time.time())
i = 0
for weight, bias in zip(self.w, self.b):
if i > 0: #ignore the first weight that goes from inputs to first hidden layer
if set_name == 'train':
self.results['activations'].insert(0, [self.activation(self.results['current']) , weight])
self.results['current'] = gpu.dot(self.results['activations'][0][0] *
(gpu.rand(self.results['activations'][0][0].shape[0],self.results['activations'][0][0].shape[1]) > self.dropout[1]), #dropout
weight) + bias
else:
self.results['current'] = gpu.dot(self.activation(self.results['current'])* (1 - self.dropout[1]), weight) + bias
i += 1
self.timer_logger('hidden_to_output {0}'.format(type), time.time())
def R_forward_pass(self, state, R):
self.R_state_X = R_state_X = [None] * len(self.sizes)
R_state_X[0] = state[0]*0
R_state_i = R_state_X[0]
for i in range(len(self.sizes) - 1):
R_state_X[i+1] = g.dot(state[i], R.W[i]) + \
g.dot(R_state_i, self.W[i]) + R.b[i]
R_state_i = self.nonlins[i].grad_y(state[i+1]) * R_state_X[i+1]
return R_state_X[-1]
def test_gnumpy(dat, num_epochs):
import gnumpy as gpu
import numpy
import time
# load data. <dat> is 2 dimensional: 60000 X 784
#dat = gpu.garray(load('mnist_cudaTest').T/255.)
# training parameters
epsilon = 0.1
momentum = 0.9
batch_size = 128
num_batches = dat.shape[0] / batch_size
# model parameters
num_vis = dat.shape[1]
num_hid = 4096
# initialize weights
w_vh = 0.1 * gpu.randn(num_vis, num_hid)
w_v = gpu.zeros(num_vis)
w_h = -4. * gpu.ones(num_hid)
# initialize weight updates
wu_vh = gpu.zeros((num_vis, num_hid))
wu_v = gpu.zeros(num_vis)
wu_h = gpu.zeros(num_hid)
for epoch in range(num_epochs):
err = []
tic = time.clock()
for batch in range(num_batches):
# positive phase
v1 = dat[batch * batch_size:(batch + 1) * batch_size]
h1 = (gpu.dot(v1, w_vh) + w_h).logistic()
# sample hiddens
hSampled = h1.rand() < h1
# negative phase
v2 = (gpu.dot(hSampled, w_vh.T) + w_v).logistic()
h2 = (gpu.dot(v2, w_vh) + w_h).logistic()
# update weights
wu_vh = wu_vh * momentum + gpu.dot(v1.T, h1) - gpu.dot(v2.T, h2)
wu_v = wu_v * momentum + v1.sum(0) - v2.sum(0)
wu_h = wu_h * momentum + h1.sum(0) - h2.sum(0)
w_vh += wu_vh * (epsilon / batch_size)
w_v += wu_v * (epsilon / batch_size)
w_h += wu_h * (epsilon / batch_size)
# calculate reconstruction error
err.append((v2 - v1).euclid_norm()**2 / (num_vis * batch_size))
toc = time.clock()
print "Mean squared error: %.4f, takes time: %d" % (numpy.mean(err),
toc - tic)
return w_vh, w_v, w_h
def forward(self, X):
self.X = X
# Num of examples
N = X.shape[0]
# Timespan
T = X.shape[1]
# Windows size
S = self.windowSize
# Channels
D = self.numChannels
# Num filters
F = self.numFilters
Z = np.zeros((N, T - S + 1, S, D), X.dtype)
for i in range(T - S + 1):
Z[:, i, :, :] = X[:, i:i + S, :]
Z = Z.reshape(N * (T - S + 1), S * D)
if self.gpu:
Z = gpu.as_garray(Z.astype('float32'))
Y = gpu.dot(Z, self.W)
Y = gpu.as_numpy_array(Y)
else:
Y = np.dot(Z, self.W)
Y = Y.reshape(N, T - S + 1, F)
self.Z = Z
return Y
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
m, _ = inpts.shape
hddn = logistic(
gpu.dot(inpts, params[: self.m_end].reshape(self.shape)) + params[self.m_end : self.m_end + self.shape[1]]
)
Z = gdot(hddn, params[: self.m_end].reshape(self.shape).T) + params[-self.shape[0] :]
w = params[: self.m_end].reshape(self.shape)
cae = gpu.sum(gpu.mean(Dsigmoid(hddn) ** 2, axis=0) * gpu.sum(w ** 2, axis=0))
cae *= self.cae
_, delta = self.score(Z, inpts, error=True, addon=cae)
g[: self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0] :] = delta.sum(axis=0)
cae_grad = gpu.mean(Dsigmoid(hddn) ** 2, axis=0) * w
cae_grad += gdot(inpts.T, (Dsigmoid(hddn) ** 2 * (1 - 2 * hddn))) / m * gpu.sum(w ** 2, axis=0)
g[: self.m_end] += self.cae * 2 * cae_grad.ravel()
dsc_dha = Dsigmoid(hddn) * gdot(delta, params[: self.m_end].reshape(self.shape))
g[: self.m_end] += gdot(inpts.T, dsc_dha).ravel()
g[self.m_end : -self.shape[0]] = dsc_dha.sum(axis=0)
# clean up
del delta, hddn, Z
return g
def cov(x):
y = gpu.mean(x, axis=1)[:, None]
x = x.as_numpy_array().__sub__(y.as_numpy_array())
x_T = x.T.conj()
result = gpu.dot(x, x_T)
result = result.__div__(x.shape[1] - 1)
return result
def check_rank(self, query_id, same_ids):
# same_ids is the list of the ids in the same class
v = self.index[query_id] # normalized
if self._metric == 'angular':
# argmax_a cossim(a, b) = argmax_a dot(a, b) / |a||b| = argmin_a -dot(a, b)
dists = -gnumpy.dot(self.index, v)
elif self._metric == 'euclidean':
# argmin_a (a - b)^2 = argmin_a a^2 - 2ab + b^2 = argmin_a a^2 - 2ab
dists = self.lengths - 2 * gnumpy.dot(self.index, v)
else:
assert False, "invalid metric" # shouldn't get past the constructor!
# this rank should start from 1, because of the self-retrieval
neighbor_dists = [ dists[i] for i in same_ids]
closest_positive_dist = min(neighbor_dists)
rank = gnumpy.sum( dists < closest_positive_dist )
return int(rank)
def apply_update(self, pos_moments, neg_moments, rbm, weight_decay, lrate):
assert np.allclose(lrate.vbias, lrate.hbias)
if self.count < self.params.start_after:
rbm.sgd_update(pos_moments, neg_moments, lrate)
return
# base rates
ds = gnp.concatenate([pos_moments.expect_vis - neg_moments.expect_vis,
pos_moments.expect_hid - neg_moments.expect_hid])
dbias = lrate.vbias * gnp.dot(self.Lambda, ds.as_numpy_array())
da, db = dbias[:rbm.nvis], dbias[rbm.nvis:]
residuals = pos_moments.expect_prod - neg_moments.expect_prod + \
-weight_decay * rbm.weights + \
-self.beta[:, :, 0] * (pos_moments.expect_vis - neg_moments.expect_vis)[:, nax] + \
-self.beta[:, :, 1] * (pos_moments.expect_hid - neg_moments.expect_hid)[nax, :]
lam = 1. / self.sigma_sq
dw = lrate.weights * lam * residuals
da -= lrate.weights * (lam * residuals * self.beta[:, :, 0]).sum(1)
db -= lrate.weights * (lam * residuals * self.beta[:, :, 1]).sum(0)
update = binary_rbms.Update(da, db, dw)
rbm += update
def glog_l_new(self, Wmat):
ll = 0
n_correct = 0
gWmat = gnp.garray(np.array(Wmat))
for n in xrange(self.nsamples):
#print self.Xi[n][0].shape, self.Xi[n][1].shape, self.Xi[n][2].shape, gWmat.shape
internals = (gnp.dot(gnp.garray(self.Xi[n][0]),gnp.dot(gWmat, gnp.garray(self.Xi[n][2].T))) - gnp.dot(gnp.garray(self.Xi[n][1]),gnp.dot(gWmat, gnp.garray(self.Xi[n][2].T)))).as_numpy_array()[0]
if logistic(internals) > 0.5 and self.Y[n] == 1:
n_correct += 1
elif logistic(internals) < 0.5 and self.Y[n] == -1:
n_correct += 1
ll += np.log(logistic(self.Y[n] * internals))
return ll, n_correct / self.nsamples
