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# -*- coding: utf-8 -*-
"""
Classes for controling machine learning processes
"""
import numpy as np
import math
import matplotlib.pyplot as plt
import csv
class TrainingPlot:
"""
Creating live plot during training
REUIRES notebook backend: %matplotlib notebook
@TODO Migrate to Tensorboard
"""
train_loss = []
train_acc = []
valid_acc = []
test_iter = 0
loss_iter = 0
interval = 0
ax1 = None
ax2 = None
fig = None
def __init__(self, steps, test_itr, loss_itr):
self.test_iter = test_itr
self.loss_iter = loss_itr
self.interval = steps
self.fig, self.ax1 = plt.subplots()
self.ax2 = self.ax1.twinx()
self.ax1.set_autoscaley_on(True)
plt.ion()
self._update_plot()
# Description
self.ax1.set_xlabel('Iteration')
self.ax1.set_ylabel('Train Loss')
self.ax2.set_ylabel('Valid. Accuracy')
# Axes limits
self.ax1.set_ylim([0,10])
def _update_plot(self):
self.fig.canvas.draw()
def update_loss(self, loss_train, index):
self.trainLoss.append(loss_train)
if len(self.train_loss) == 1:
self.ax1.set_ylim([0, min(10, math.ceil(loss_train))])
self.ax1.plot(self.lossInterval * np.arange(len(self.train_loss)),
self.train_loss, 'b', linewidth=1.0)
self.updatePlot()
def update_acc(self, acc_val, acc_train, index):
self.validAcc.append(acc_val)
self.trainAcc.append(acc_train)
self.ax2.plot(self.test_iter * np.arange(len(self.valid_acc)),
self.valid_acc, 'r', linewidth=1.0)
self.ax2.plot(self.test_iter * np.arange(len(self.train_acc)),
self.train_acc, 'g',linewidth=1.0)
self.ax2.set_title('Valid. Accuracy: {:.4f}'.format(self.valid_acc[-1]))
self.updatePlot()
class DataSet:
"""Class for training data and feeding train function."""
images = None
labels = None
length = 0
index = 0
def __init__(self, img, lbl):
self.images = img
self.labels = lbl
self.length = len(img)
self.index = 0
def next_batch(self, batch_size):
"""Return the next batch from the data set."""
start = self.index
self.index += batch_size
if self.index > self.length:
# Shuffle the data
perm = np.arange(self.length)
np.random.shuffle(perm)
self.images = self.images[perm]
self.labels = self.labels[perm]
# Start next epoch
start = 0
self.index = batch_size
end = self.index
return self.images[start:end], self.labels[start:end]
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