def make_train_plots(log=None, log_path=None, keys=None, save_loc=None):
if log is None and log_path is None:
print("Need to provide either the log or path to a log file")
if log is None:
logger = DataLog()
logger.read_log(log_path)
log = logger.log
# make plots for specified keys
for key in keys:
if key in log.keys():
plt.figure(figsize=(10, 6))
plt.plot(log['steps'], log[key])
plt.title(key)
plt.savefig(save_loc + '/' + key + '.png', dpi=100)
plt.close()
def make_train_plots(log=None, log_path=None, keys=None, save_loc=None):
if log is None and log_path is None:
print("Need to provide either the log or path to a log file")
if log is None:
logger = DataLog()
logger.read_log(log_path)
log = logger.log
# make plots for specified keys
for key in keys:
if key in log.keys():
plt.figure(figsize=(10, 6))
if 'samples' in keys:
cum_samples = [
np.sum(log['num_samples'][:i])
for i in range(len(log['num_samples']))
]
plt.plot(cum_samples, log[key])
plt.xlabel('samples')
else:
plt.plot(log[key])
plt.xlabel('iterations')
plt.title(key)
plt.savefig(save_loc + '/' + key + '.png', dpi=100)
plt.close()
def make_train_plots(log=None,
log_path=None,
keys=None,
save_loc=None,
sample_key='num_samples',
x_scale=1.0,
y_scale=1.0):
if log is None and log_path is None:
print("Need to provide either the log or path to a log file")
if log is None:
logger = DataLog()
logger.read_log(log_path)
log = logger.log
# make plots for specified keys
for key in keys:
if key in log.keys():
fig = plt.figure(figsize=(10, 6))
ax1 = fig.add_subplot(111)
try:
cum_samples = [
np.sum(log[sample_key][:i]) * x_scale
for i in range(len(log[sample_key]))
]
ax1.plot(cum_samples, [elem * y_scale for elem in log[key]])
ax1.set_xlabel('samples')
# mark iteration on the top axis
ax2 = ax1.twiny()
ax2.set_xlabel('iterations', color=(.7, .7, .7))
ax2.tick_params(axis='x', labelcolor=(.7, .7, .7))
ax2.set_xlim([0, len(log[key])])
except:
ax1.plot(log[key])
ax1.set_xlabel('iterations')
ax1.set_title(key)
plt.savefig(save_loc + '/' + key + '.png', dpi=100)
plt.close()
