channel = m.monitor.channels["valid_y_nll"]
hl.Curve(zip(channel.epoch_record, channel.val_record),label="valid_y_nll")

channel = m.monitor.channels["valid_y_nll"]
plt.plot(channel.epoch_record, channel.val_record)

"""
Explanation: The train_y_nll, valid_y_nll and valid_objective are in synchrony, suggesting we are not overfitting.
End of explanation
"""


ch1 = m.monitor.channels["valid_y_nll"]
ch2 = m.monitor.channels["train_y_nll"]
hl.Curve(zip(ch1.epoch_record[-40:], ch1.val_record[-40:]),label="valid_y_nll")

hl.Curve(zip(ch2.epoch_record[-40:], ch2.val_record[-40:]),label="train_y_nll")

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/continue_hopeful_recent.pkl")

make_curves(m,"valid_objective","valid_y_nll","train_y_nll")

"""
Explanation: Hard to see whether it is still learning...
End of explanation
"""


