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R6062/plot.py
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plot.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Plot functions to create figures.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import torch.nn as nn
from paths import FIGURES_DIR, DEBUG_DIR_MNIST_012
def plot_loss_and_acc(run_time, run_name, history):
"""Generate a plot with training loss and validation accuracy for a specific model."""
num_epochs = list(range(len(history)))
loss = [t[0] for t in history]
acc = [t[1] for t in history]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
r1 = ax1.plot(num_epochs, loss, color='red', label='training loss')
ax1.set_xlabel('epoch')
ax1.set_ylabel('loss')
r2 = ax2.plot(num_epochs, acc, color='blue', label='validation accuracy')
ax2.set_ylabel('accuracy')
lns = r2 + r1
labs = [l.get_label() for l in lns]
leg = plt.legend(lns, labs, loc='center right', shadow=True)
leg.draw_frame(False)
plt.savefig(FIGURES_DIR + run_time + '_' + run_name + '_loss_and_acc_results.png')
plt.gcf().clear()
def plot_loss(run_time, run_name, history):
"""Generate a plot with training and validation loss for the given history."""
num_epochs = list(range(len(history)))
train_loss = [t[0] for t in history]
valid_loss = [t[1] for t in history]
fig, ax1 = plt.subplots()
train_curve = ax1.plot(num_epochs, train_loss, color='red', label='training')
valid_curve = ax1.plot(num_epochs, valid_loss, color='blue', label='validation')
ax1.set_xlabel('epoch')
ax1.set_ylabel('loss')
leg = plt.legend(loc='upper right', shadow=True)
leg.draw_frame(False)
plt.savefig(FIGURES_DIR + run_time + '_' + run_name + '_losses.png')
plt.gcf().clear()
def plot_error(run_time, run_name, history):
"""Generates a plot with training validation errors for the given history."""
num_epochs = list(range(len(history)))
train_errors = [t[0] for t in history]
valid_errors = [t[1] for t in history]
fig, ax1 = plt.subplots()
train_curve = ax1.plot(num_epochs, train_errors, color='red', label='training')
valid_curve = ax1.plot(num_epochs, valid_errors, color='blue', label='validation')
ax1.set_xlabel('epoch')
ax1.set_ylabel('error %')
leg = plt.legend(loc='upper right', shadow=True)
leg.draw_frame(False)
plt.savefig(FIGURES_DIR + run_time + '_' + run_name + '_errors.png')
plt.gcf().clear()
def colorbar(mappable):
"""Create a colorbar that matches properly the size of the image."""
ax = mappable.axes
fig = ax.figure
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
return fig.colorbar(mappable, cax=cax)
def show_tensor(image, filter_name, dim, num_nodes):
"""Show the image given by the tensor."""
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
im = ax.imshow(image.view(dim,dim), cmap='jet')
# add suptitle and display the plot
plt.suptitle(filter_name)
plt.tight_layout(h_pad=1)
plt.show()
def show_cheb_poly_tensor(cheb_poly, filter_name, num_filters, dim, num_nodes):
"""Show chebyshev polynomial of the intermediary layers given by the tensor."""
# create the figure containing all the filters
fig = plt.figure(figsize=(15,10))
for i in range(num_filters):
for j in range(10):
ax = fig.add_subplot(num_filters, 10, i*10 + j+1) # this line adds sub-axes
im = ax.imshow(cheb_poly.detach()[1, i, :, j].contiguous().view(dim,dim), cmap='jet')
# add suptitle and display the plot
plt.suptitle(filter_name)
plt.tight_layout(h_pad=1)
plt.show()
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