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tune.py
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import numpy as np
import cudamat as cm
import time
from cudamat import CUDAMatrix as M
import cPickle
n_epoch = 10
n_hidden = 100
init_scale = 0.01
batch_size = 1000
learning_rate = 0.01
momentum = 0.9
def load_activations(filename):
with open(filename) as h:
X = np.load(h)
return X
def load_targets(filename):
with open(filename, 'rb') as h:
Y = np.load(h)
return Y
def load_model(filename):
with open(filename) as handler:
H, bh = cPickle.load(handler)
return zip(H, bh)
def save_model(model, filename):
with open(filename, 'wb') as handle:
H, bh = model
_H, _bh = [], []
for p0,p1 in zip(H, bh):
_H.append(p0.asarray())
_bh.append(p1.asarray())
cPickle.dump((_H,_bh), handle)
def grad(X, Y, act, params, grads, aux):
H, bh = params
_H, _bh = grads
a, eh, loss = aux
# forward pass
a[0].assign(X)
n_layers = len(eh)
for i in range(n_layers):
# a = sigmoid( ap*H + bh )
a[i].dot(H[i], target = a[i+1])
a[i+1].add_row_vec(bh[i])
if i < n_layers-1:
cm.sigmoid(a[i+1])
else:
# last layer
if act == 'logistic':
cm.sigmoid(a[i+1])
elif act == 'softmax':
a_t = a[i+1].transpose()
cm.softmax(a_t)
a_t.transpose(target=a[i+1])
a_t.free_device_memory()
else:
pass
# backward pass
# compute error term of the last layer
a[-1].subtract(Y, target=eh[-1])
# check the following
for i in range(n_layers-1, -1, -1):
# compute derivatives
_H[i].assign(0.0)
_H[i].add_dot(a[i].T, eh[i])
eh[i].sum(axis=0, target=_bh[i])
# compute error term for the previous layer
if i > 0:
# eh = sigmoid'(a) x ( ehp*H' )
eh[i].dot(H[i].T, target=eh[i-1])
eh[i-1].apply_logistic_deriv(a[i])
if act == 'logistic':
cm.cross_entropy_bernoulli(Y, a[n_layers], target=loss)
elif act == 'softmax':
loss = cm.cross_entropy(Y, a[n_layers], target=loss)
elif act == 'linear':
a[-1].mult(a[-1], target=loss)
return loss.sum()
def train(x, y, model, prev, args):
n_items = x.shape[0]
n_batches = n_items/batch_size
H, bh, a, eh = [], [], [], []
_H, _bh = [], []
n_in = x.shape[1]
n_out = y.shape[1]
if not prev:
lh, _ = model[-1]
l_H = np.random.normal( scale=0.1, size=(lh.shape[1], n_out))
l_bh = np.zeros((1,n_out))
model.append( (l_H, l_bh) )
for p_H,p_bh in model:
H.append(M(p_H))
bh.append(M(p_bh))
_H.append(cm.empty(p_H.shape))
_bh.append(cm.empty(p_bh.shape))
# allocate space for the activation vectors
a.append(cm.empty((batch_size, p_H.shape[0])))
# allocate space for the error vectors
eh.append(cm.empty((batch_size, p_H.shape[1])))
# last layer
a.append(cm.empty((batch_size, n_out)))
# loss
loss = M(np.zeros((batch_size, n_out)))
# each parameter and gradient is a list
params = [H, bh]
grads = [_H, _bh]
aux = [a, eh, loss]
X = cm.empty((batch_size, n_in))
Y = cm.empty((batch_size, n_out))
x_val = M(x[0:batch_size])
y_val = M(y[0:batch_size])
for epoch in range(n_epoch):
err = []
t0 = time.clock()
v_err = grad(x_val, y_val, args.act_out,
params, grads, aux)
for i in range(1,n_batches):
s = slice(i*batch_size, (i+1)*batch_size)
X.overwrite(x[s])
Y.overwrite(y[s])
# apply momentum
for layer in grads:
for g in layer:
g.mult(momentum)
cost = grad(X, Y, args.act_out,
params, grads, aux)
# update parameters
for _p,_g in zip(params, grads):
for p,g in zip(_p,_g):
p.subtract_mult(g, mult=learning_rate/(batch_size))
err.append(cost/batch_size)
print "Epoch: %d, Loss: %.8f, VLoss: %.8f, Time: %.4fs" % (
epoch,
np.mean(err),
v_err/batch_size,
time.clock()-t0 )
if args.out_params:
save_model(params, args.out_params)
return params
def load_params(filename):
params = []
with open(filename) as h:
while True:
try:
H = np.load(h)
O = np.load(h)
bh = np.load(h)
bo = np.load(h)
params.append((H, bh))
except IOError:
break
return params
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-a', '--activations', required=True, help='data file')
parser.add_argument('-t', '--targets', required=True, help='data file')
parser.add_argument('-o', '--out_params', default='', help='continue with the model')
parser.add_argument('-m', '--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('-l', '--learning_rate', type=float, default=0.01, help='learning rate')
parser.add_argument('-b', '--batch_size', type=int, default=100, help='batch size')
parser.add_argument('-e', '--epoch', type=int, default=10, help='number of epochs')
parser.add_argument('-p', '--params', default='', help='continue with the model')
parser.add_argument('-ao', '--act_out', default='linear', choices=['linear', 'logistic', 'softmax'], help='')
parser.add_argument('-c', '--continue', dest='cont',
action='store_true', default=False, help='continue with the model')
args = parser.parse_args()
prev = False # marks that we need to create an additional layer
if args.cont:
model = load_model(args.out_params)
prev = True
else:
model = load_params(args.params)
momentum = args.momentum
learning_rate = args.learning_rate
batch_size = args.batch_size
n_epoch = args.epoch
X = load_activations(args.activations)
Y = load_targets(args.targets)
cm.cublas_init()
model = train(X, Y, model, prev, args)