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