def main():
var = theano.shared(T.zeros(shape=(88, 100), dtype=theano.config.floatX).eval(), name='W')
updates = [(var, add_uniform(input=var, noise_level=.02))]
stats = get_stats(var)
l1 = stats.pop('l1')
l2 = stats.pop('l2')
min = stats.pop('min')
max = stats.pop('max')
var = stats.pop('var')
std = stats.pop('std')
mean = stats.pop('mean')
mean_monitor = Monitor('mean', mean, train=True, valid=True, out_service=FileService('outs/mean.txt'))
var_monitor = Monitor('var', var, out_service=FileService('outs/var.txt'))
w_channel = MonitorsChannel('W', monitors=mean_monitor)
stat_channel = MonitorsChannel('stats', monitors=[var_monitor])
monitors = [w_channel, stat_channel]
train_collapsed_raw = collapse_channels(monitors, train=True)
train_collapsed = OrderedDict([(item[0], item[1]) for item in train_collapsed_raw])
train_services = OrderedDict([(item[0], item[2]) for item in train_collapsed_raw])
valid_collapsed_raw = collapse_channels(monitors, valid=True)
valid_collapsed = OrderedDict([(item[0], item[1]) for item in valid_collapsed_raw])
valid_services = OrderedDict([(item[0], item[2]) for item in valid_collapsed_raw])
log.debug('compiling...')
f = theano.function(inputs=[], outputs=list(train_collapsed.values()), updates=updates)
f2 = theano.function(inputs=[], outputs=list(valid_collapsed.values()), updates=updates)
log.debug('done')
t1=time.time()
for epoch in range(10):
t=time.time()
log.debug(epoch)
vals = f()
m = OrderedDict(zip(train_collapsed.keys(), vals))
for name, service in train_services.items():
if name in m:
service.write(m[name], "train")
log.debug('----- '+make_time_units_string(time.time()-t))
for epoch in range(10):
t = time.time()
log.debug(epoch)
vals = f2()
m = OrderedDict(zip(valid_collapsed.keys(), vals))
for name, service in valid_services.items():
if name in m:
service.write(m[name], "valid")
log.debug('----- ' + make_time_units_string(time.time() - t))
log.debug("TOTAL TIME "+make_time_units_string(time.time()-t1))
def main():
var = theano.shared(T.zeros(shape=(88, 100), dtype=theano.config.floatX).eval(), name='W')
updates = [(var, add_uniform(input=var, noise_level=.02))]
stats = get_stats(var)
l1 = stats.pop('l1')
l2 = stats.pop('l2')
min = stats.pop('min')
max = stats.pop('max')
var = stats.pop('var')
std = stats.pop('std')
mean = stats.pop('mean')
mean_monitor = Monitor('mean', mean, train=True, valid=True, out_service=FileService('outs/mean.txt'))
var_monitor = Monitor('var', var, out_service=FileService('outs/var.txt'))
w_channel = MonitorsChannel('W', monitors=mean_monitor)
stat_channel = MonitorsChannel('stats', monitors=[var_monitor])
monitors = [w_channel, stat_channel]
train_collapsed_raw = collapse_channels(monitors, train=True)
train_collapsed = OrderedDict([(item[0], item[1]) for item in train_collapsed_raw])
train_services = OrderedDict([(item[0], item[2]) for item in train_collapsed_raw])
valid_collapsed_raw = collapse_channels(monitors, valid=True)
valid_collapsed = OrderedDict([(item[0], item[1]) for item in valid_collapsed_raw])
valid_services = OrderedDict([(item[0], item[2]) for item in valid_collapsed_raw])
log.debug('compiling...')
f = theano.function(inputs=[], outputs=train_collapsed.values(), updates=updates)
f2 = theano.function(inputs=[], outputs=valid_collapsed.values(), updates=updates)
log.debug('done')
t1=time.time()
for epoch in range(10):
t=time.time()
log.debug(epoch)
vals = f()
m = OrderedDict(zip(train_collapsed.keys(), vals))
for name, service in train_services.items():
if name in m:
service.write(m[name], TRAIN)
log.debug('----- '+make_time_units_string(time.time()-t))
for epoch in range(10):
t = time.time()
log.debug(epoch)
vals = f2()
m = OrderedDict(zip(valid_collapsed.keys(), vals))
for name, service in valid_services.items():
if name in m:
service.write(m[name], VALID)
log.debug('----- ' + make_time_units_string(time.time() - t))
log.debug("TOTAL TIME "+make_time_units_string(time.time()-t1))
def main():
w = theano.shared(T.zeros(shape=(88, 100), dtype=theano.config.floatX).eval(), name='W')
updates = [(w, add_uniform(input=w, noise_level=.02))]
stats = get_stats(w)
l1 = stats.pop('l1')
l2 = stats.pop('l2')
min = stats.pop('min')
max = stats.pop('max')
var = stats.pop('var')
std = stats.pop('std')
mean = stats.pop('mean')
mean_monitor = Monitor('mean', mean, train=True, valid=True)
stat_monitor = Monitor('max', max)
w_channel = MonitorsChannel('W', monitors=mean_monitor)
stat_channel = MonitorsChannel('stats', monitors=[stat_monitor])
monitors = [w_channel, stat_channel]
train_collapsed = collapse_channels(monitors, train=True)
train_collapsed = OrderedDict([(name, expression) for name, expression, _ in train_collapsed])
valid_collapsed = collapse_channels(monitors, valid=True)
valid_collapsed = OrderedDict([(name, expression) for name, expression, _ in valid_collapsed])
plot = Plot(bokeh_doc_name='test_plots', monitor_channels=monitors, open_browser=True)
log.debug('compiling...')
f = theano.function(inputs=[], outputs=list(train_collapsed.values()), updates=updates)
f2 = theano.function(inputs=[], outputs=list(valid_collapsed.values()), updates=updates)
log.debug('done')
t1=time.time()
for epoch in range(100):
t=time.time()
log.debug(epoch)
vals = f()
m = OrderedDict(zip(train_collapsed.keys(), vals))
plot.update_plots(epoch, m)
time.sleep(0.02)
log.debug('----- '+make_time_units_string(time.time()-t))
for epoch in range(100):
t = time.time()
log.debug(epoch)
vals = f2()
m = OrderedDict(zip(valid_collapsed.keys(), vals))
plot.update_plots(epoch, m)
time.sleep(0.02)
log.debug('----- ' + make_time_units_string(time.time() - t))
log.debug("TOTAL TIME "+make_time_units_string(time.time()-t1))
def main():
var = theano.shared(T.zeros(shape=(88, 100),
dtype=theano.config.floatX).eval(),
name='W')
updates = [(var, add_uniform(input=var, noise_level=.02))]
stats = get_stats(var)
l1 = stats.pop('l1')
l2 = stats.pop('l2')
min = stats.pop('min')
max = stats.pop('max')
var = stats.pop('var')
std = stats.pop('std')
mean = stats.pop('mean')
mean_monitor = Monitor('mean', mean, train=True, valid=True)
var_monitor = Monitor('var', var)
w_channel = MonitorsChannel('W', monitors=mean_monitor)
stat_channel = MonitorsChannel('stats', monitors=[var_monitor])
monitors = [w_channel, stat_channel]
train_collapsed = collapse_channels(monitors, train=True)
train_collapsed = OrderedDict([(name, expression)
for name, expression, _ in train_collapsed])
valid_collapsed = collapse_channels(monitors, valid=True)
valid_collapsed = OrderedDict([(name, expression)
for name, expression, _ in valid_collapsed])
plot = Plot(bokeh_doc_name='test_plots',
monitor_channels=monitors,
open_browser=True)
log.debug('compiling...')
f = theano.function(inputs=[],
outputs=list(train_collapsed.values()),
updates=updates)
f2 = theano.function(inputs=[],
outputs=list(valid_collapsed.values()),
updates=updates)
log.debug('done')
t1 = time.time()
for epoch in range(100):
t = time.time()
log.debug(epoch)
vals = f()
m = OrderedDict(zip(train_collapsed.keys(), vals))
plot.update_plots(epoch, m)
log.debug('----- ' + make_time_units_string(time.time() - t))
for epoch in range(100):
t = time.time()
log.debug(epoch)
vals = f2()
m = OrderedDict(zip(valid_collapsed.keys(), vals))
plot.update_plots(epoch, m)
log.debug('----- ' + make_time_units_string(time.time() - t))
log.debug("TOTAL TIME " + make_time_units_string(time.time() - t1))