parser.add_argument('--exp', nargs='+', default=[], help="Nombre de los experimentos")
args = parser.parse_args()
lexperiments = args.exp
if not args.batch:
# 'e150514''e120503''e110616''e150707''e151126''e120511'
lexperiments = ['e150514']
for exp in lexperiments:
datainfo = experiments[exp]
for sensor in datainfo.sensors:
print(sensor)
for dfile in [datainfo.datafiles[0]]:
print(dfile)
f = datainfo.open_experiment_data(mode='r')
data = datainfo.get_peaks_resample_PCA(f, dfile, sensor)
for d in data:
mn = np.mean(d[0:20])
print d[0:20]
plotListSignals([d])
datainfo.close_experiment_data(f)
datamat = datainfo.get_raw_data(f, dfile)
for i in range(len(vecsync)):
saverage = np.zeros((len(lsensors), (wlen*2)))
scounts = np.zeros(len(lsensors))
for syn in vecsync[i]:
stime = time_sync_sensor(syn, nsensor)
for j in range(len(lsensors)):
saverage[j] += datamat[stime-wlen:stime+wlen,j]
# for s in syn:
# saverage[s[0]] += datamat[stime-wlen:stime+wlen,s[0]]
# scounts[s[0]] += 1
for i in range(len(lsensors)):
saverage[i] /= len(vecsync[i])
# for i in range(len(lsensors)):
# saverage[i] /= scounts[i]
plotListSignals(saverage, ncols=2)
datainfo.close_experiment_data(f)
# for s in lsynchs_pruned:
# print s
# construct the stacked denoising autoencoder class
sda = SdA(
numpy_rng=numpy_rng,
n_ins=data.shape[1],
hidden_layers_sizes=[1000, 500, 20, data.shape[1]],
n_outs=10
)
# end-snippet-3 start-snippet-4
pretraining_fns = sda.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size)
print('... pre-training the model')
start_time = timeit.default_timer()
## Pre-train layer-wise
corruption_levels = [.1, .1, .1, .1]
for i in range(sda.n_layers):
# go through pretraining epochs
for epoch in range(pretraining_epochs):
# go through the training set
c = []
for batch_index in range(n_train_batches):
c.append(pretraining_fns[i](index=batch_index,
corruption=corruption_levels[i],
lr=pretrain_lr))
print('Pre-training layer %i, epoch %d, cost %f' % (i, epoch, numpy.mean(c)))
end_time = timeit.default_timer()
print(sda.dA_layers[-1].W.get_value(borrow=True).shape)
plotListSignals(sda.dA_layers[-1].W.get_value(borrow=True), ncols=5)
parser.add_argument('--exp',
nargs='+',
default=[],
help="Nombre de los experimentos")
args = parser.parse_args()
lexperiments = args.exp
if not args.batch:
# 'e150514''e120503''e110616''e150707''e151126''e120511'
lexperiments = ['e150514']
for exp in lexperiments:
datainfo = experiments[exp]
for sensor in datainfo.sensors:
print(sensor)
for dfile in [datainfo.datafiles[0]]:
print(dfile)
f = datainfo.open_experiment_data(mode='r')
data = datainfo.get_peaks_resample_PCA(f, dfile, sensor)
for d in data:
mn = np.mean(d[0:20])
print d[0:20]
plotListSignals([d])
datainfo.close_experiment_data(f)
cost, updates = da.get_cost_updates(
corruption_level=0.1,
learning_rate=learning_rate
)
train_da = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size]
}
)
start_time = timeit.default_timer()
# go through training epochs
for epoch in range(training_epochs):
# go through trainng set
c = []
for batch_index in range(n_train_batches):
c.append(train_da(batch_index))
print('Training epoch %d, cost ' % epoch, numpy.mean(c))
end_time = timeit.default_timer()
print(da.W.get_value(borrow=True).shape)
plotListSignals(da.W.get_value(borrow=True).T, ncols=5)
da = dA(numpy_rng=rng,
theano_rng=theano_rng,
input=x,
n_visible=data.shape[1],
n_hidden=20)
cost, updates = da.get_cost_updates(corruption_level=0.1,
learning_rate=learning_rate)
train_da = theano.function(
[index],
cost,
updates=updates,
givens={x: train_set_x[index * batch_size:(index + 1) * batch_size]})
start_time = timeit.default_timer()
# go through training epochs
for epoch in range(training_epochs):
# go through trainng set
c = []
for batch_index in range(n_train_batches):
c.append(train_da(batch_index))
print('Training epoch %d, cost ' % epoch, numpy.mean(c))
end_time = timeit.default_timer()
print(da.W.get_value(borrow=True).shape)
plotListSignals(da.W.get_value(borrow=True).T, ncols=5)
