def show_mpl():
cli = tw.WatcherClient()
p = tw.mpl.LinePlot(title='Demo')
s1 = cli.create_stream(expr='lambda v:(v.i, v.sum)')
p.subscribe(s1, xtitle='Index', ytitle='sqrt(ev_i)')
p.show()
tw.plt_loop()
def mpl_line_plot():
cli = tw.WatcherClient()
p = tw.LinePlot(title='Demo')
s1 = cli.create_stream(event_name='ev_i',
expr='map(lambda v:math.sqrt(v.val)*2, l)')
p.subscribe(s1, xtitle='Index', ytitle='sqrt(ev_i)')
p.show()
tw.plt_loop()
def show_mpl():
cli = tw.WatcherClient(r'c:\temp\sum.log')
s1 = cli.open_stream('sum')
p = tw.LinePlot(title='Demo')
p.subscribe(s1, xtitle='Index', ytitle='sqrt(ev_i)')
s1.load()
p.show()
tw.plt_loop()
def dynamic_hist():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='histogram', bins=6, clear_after_each=True)
v.show()
for _ in range(100):
s.write([random.random() * 10 for _ in range(100)])
tw.plt_loop(count=3)
def dynamic_bar():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='bar', clear_after_each=True)
v.show()
for i in range(100):
s.write([('a' + str(i), random.random() * 10) for i in range(10)])
tw.plt_loop(count=3)
def static_hist():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='histogram', bins=6)
v.show()
for _ in range(100):
s.write(random.random() * 10)
tw.plt_loop()
def dynamic_pie():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='pie', bins=6, clear_after_each=True)
v.show()
for _ in range(100):
s.write([('label' + str(i), random.random() * 10, None, i * 0.01)
for i in range(12)])
tw.plt_loop(count=3)
def static_bar():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='bar')
v.show()
for i in range(10):
s.write(int(random.random() * 10))
tw.plt_loop()
def show_mpl():
cli = tw.WatcherClient()
st_isum = cli.open_stream('isums')
st_rsum = cli.open_stream('rsums')
line_plot = tw.Visualizer(st_isum, vis_type='line', xtitle='i', ytitle='isum')
line_plot.show()
line_plot2 = tw.Visualizer(st_rsum, vis_type='line', host=line_plot, ytitle='rsum')
tw.plt_loop()
def dynamic_line3d():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='line3d', clear_after_each=True)
v.show()
for i in range(100):
s.write([(i, random.random() * 10, z) for i in range(10)
for z in range(10)])
tw.plt_loop(count=3)
def static_pie():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='pie', bins=6)
v.show()
for i in range(6):
s.write(('label' + str(i), random.random() * 10, None,
0.5 if i == 3 else 0))
tw.plt_loop()
def mpl_history_plot():
cli = tw.WatcherClient()
p2 = tw.LinePlot(title='History Demo')
p2s1 = cli.create_stream(
event_name='ev_j', expr='map(lambda v:(v.val, math.sqrt(v.val)*2), l)')
p2.subscribe(p2s1,
xtitle='Index',
ytitle='sqrt(ev_j)',
clear_after_end=True,
history_len=15)
p2.show()
tw.plt_loop()
def reader3():
print('---------------------------reader3---------------------------')
watcher = tw.Watcher(filename=r'c:\temp\test.log', port=None)
stream1 = watcher.open_stream('metric1')
stream2 = watcher.open_stream('metric2')
vis1 = tw.Visualizer(stream1, vis_type='line')
vis2 = tw.Visualizer(stream2, vis_type='line', host=vis1)
vis1.show()
tw.plt_loop()
def plot_grads():
train_cli = tw.WatcherClient()
grads = train_cli.create_stream(event_name='batch',
expr='lambda d:grads_abs_mean(d.model)',
throttle=1)
grad_plot = tw.LinePlot()
grad_plot.subscribe(grads,
xtitle='Layer',
ytitle='Gradients',
clear_after_each=1,
history_len=40,
dim_history=True)
grad_plot.show()
tw.plt_loop()
def plot_weight():
train_cli = tw.WatcherClient()
params = train_cli.create_stream(event_name='batch',
expr='lambda d:weights_abs_mean(d.model)',
throttle=1)
params_plot = tw.LinePlot()
params_plot.subscribe(params,
xtitle='Layer',
ytitle='avg |params|',
clear_after_each=1,
history_len=40,
dim_history=True)
params_plot.show()
tw.plt_loop()
def plot_weight():
train_cli = tw.WatcherClient()
params = train_cli.create_stream(
event_name='batch',
expr='lambda d:agg_params(d.model, lambda p: p.abs().mean().item())',
throttle=1)
params_plot = tw.mpl.LinePlot()
params_plot.subscribe(params,
xtitle='Epoch',
ytitle='avg |params|',
clear_after_each=1,
history_len=40,
dim_history=True)
params_plot.show()
tw.plt_loop()
def batch_stats():
train_cli = tw.WatcherClient()
stream = train_cli.create_stream(
event_name="batch",
expr='lambda v:(v.metrics.epochf, v.metrics.batch_loss)',
throttle=0.75)
train_loss = tw.Visualizer(stream,
clear_after_end=False,
vis_type='mpl-line',
xtitle='Epoch',
ytitle='Train Loss')
#train_acc = tw.Visualizer('lambda v:(v.metrics.epochf, v.metrics.epoch_loss)', event_name="batch",
# xtitle='Epoch', ytitle='Train Accuracy', clear_after_end=False, yrange=(0,1),
# vis=train_loss, vis_type='mpl-line')
train_loss.show()
tw.plt_loop()
def epoch_stats():
train_cli = tw.WatcherClient(port=0)
test_cli = tw.WatcherClient(port=1)
plot = tw.mpl.LinePlot()
train_loss = train_cli.create_stream(
event_name="epoch",
expr='lambda v:(v.metrics.epoch_index, v.metrics.epoch_loss)')
plot.subscribe(train_loss, xtitle='Epoch', ytitle='Train Loss')
test_acc = test_cli.create_stream(
event_name="epoch",
expr='lambda v:(v.metrics.epoch_index, v.metrics.epoch_accuracy)')
plot.subscribe(test_acc,
xtitle='Epoch',
ytitle='Test Accuracy',
ylim=(0, 1))
plot.show()
tw.plt_loop()
import tensorwatch as tw
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='line')
v.show()
for i in range(10):
i = float(i)
s.write(tw.PointData(i, i * i, low=i * i - i, high=i * i + i))
tw.plt_loop()
