from keras.callbacks import Callback
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn.metrics import confusion_matrix
import itertools
import numpy as np
class AccLossPlotter(Callback):
"""Plot training Accuracy and Loss values on a Matplotlib graph.
The graph is updated by the 'on_epoch_end' event of the Keras Callback class
# Arguments
graphs: list with some or all of ('acc', 'loss')
save_graph: Save graph as an image on Keras Callback 'on_train_end' event
"""
def __init__(self, graphs=['acc', 'loss'], save_graph=False):
self.graphs = graphs
self.num_subplots = len(graphs)
self.save_graph = save_graph
def on_train_begin(self, logs={}):
self.acc = []
self.val_acc = []
self.loss = []
self.val_loss = []
self.epoch_count = 0
plt.ion()
plt.show()
def on_epoch_end(self, epoch, logs={}):
self.epoch_count += 1
self.val_acc.append(logs.get('val_acc'))
self.acc.append(logs.get('acc'))
self.loss.append(logs.get('loss'))
self.val_loss.append(logs.get('val_loss'))
epochs = [x for x in range(self.epoch_count)]
count_subplots = 0
if 'acc' in self.graphs:
count_subplots += 1
plt.subplot(self.num_subplots, 1, count_subplots)
plt.title('Accuracy')
#plt.axis([0,100,0,1])
plt.plot(epochs, self.val_acc, color='r')
plt.plot(epochs, self.acc, color='b')
plt.ylabel('accuracy')
red_patch = mpatches.Patch(color='red', label='Test')
blue_patch = mpatches.Patch(color='blue', label='Train')
plt.legend(handles=[red_patch, blue_patch], loc=4)
if 'loss' in self.graphs:
count_subplots += 1
plt.subplot(self.num_subplots, 1, count_subplots)
plt.title('Loss')
#plt.axis([0,100,0,5])
plt.plot(epochs, self.val_loss, color='r')
plt.plot(epochs, self.loss, color='b')
plt.ylabel('loss')
red_patch = mpatches.Patch(color='red', label='Test')
blue_patch = mpatches.Patch(color='blue', label='Train')
plt.legend(handles=[red_patch, blue_patch], loc=4)
plt.draw()
plt.pause(0.001)
def on_train_end(self, logs={}):
if self.save_graph:
plt.savefig('training_acc_loss.png')
class ConfusionMatrixPlotter(Callback):
"""Plot the confusion matrix on a graph and update after each epoch
# Arguments
X_val: The input values
Y_val: The expected output values
classes: The categories as a list of string names
normalize: True - normalize to [0,1], False - keep as is
cmap: Specify matplotlib colour map
title: Graph Title
"""
def __init__(self, X_val, Y_val, classes, normalize=False, cmap=plt.cm.Blues, title='Confusion Matrix'):
self.X_val = X_val
self.Y_val = Y_val
self.title = title
self.classes = classes
self.normalize = normalize
self.cmap = cmap
plt.ion()
#plt.show()
plt.figure()
plt.title(self.title)
def on_train_begin(self, logs={}):
pass
def on_epoch_end(self, epoch, logs={}):
plt.clf()
pred = self.model.predict(self.X_val)
max_pred = np.argmax(pred, axis=1)
max_y = np.argmax(self.Y_val, axis=1)
cnf_mat = confusion_matrix(max_y, max_pred)
if self.normalize:
cnf_mat = cnf_mat.astype('float') / cnf_mat.sum(axis=1)[:, np.newaxis]
thresh = cnf_mat.max() / 2.
for i, j in itertools.product(range(cnf_mat.shape[0]), range(cnf_mat.shape[1])):
plt.text(j, i, cnf_mat[i, j],
horizontalalignment="center",
color="white" if cnf_mat[i, j] > thresh else "black")
plt.imshow(cnf_mat, interpolation='nearest', cmap=self.cmap)
# Labels
tick_marks = np.arange(len(self.classes))
plt.xticks(tick_marks, self.classes, rotation=45)
plt.yticks(tick_marks, self.classes)
plt.colorbar()
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
#plt.draw()
plt.show()
plt.pause(0.001)
