def plot(predictions, label):
xs = np.arange(len(predictions)) * test_every
ys = [[100.0 - preds[i, i] / preds[i].sum() * 100 for preds in predictions] for i in range(model.output_size)]
mpl.rc('savefig', format='svg')
mpl.rc('lines', linewidth=0.5)
mpl.style.use('seaborn')
for i in range(model.output_size):
plt.plot(xs, ys[i], label=str(i))
plt.legend()
plt.xlabel('Epoch')
plt.ylabel('Error (%)')
plt.savefig(f"predictions_{label}_error")
plt.close()
np.save(fname + '_hist', history)
np.save(fname + '_model', model)
np.save(fname + '_ngram', ngram)
with open(fname + '_doc', "w+") as doc:
doc.write("primal_lr: {}\ndual_lr: {}\nn: {}\n{}".format(
primal_lr, dual_lr, ngram.n, comment))
# %% PLOTTING TEST
xs = np.arange(len(history['predictions'])) * test_every
ys = [[
100.0 - preds[i, i] / preds[i].sum() * 100
for preds in history['predictions']
] for i in range(model.output_size)]
mpl.rc('savefig', format='svg')
mpl.rc('lines', linewidth=0.5)
mpl.style.use('seaborn')
for i in range(model.output_size):
plt.plot(xs, ys[i], label=str(i))
plt.legend()
plt.xlabel('Epoch')
plt.ylabel('Error (%)')
plt.savefig("predictions_test_error")
plt.close()
# %% PLOTTING DATA
ys = [[
100.0 - preds[i, i] / preds[i].sum() * 100
for preds in history['predictions_data']
