def draw_error_vs_validation(error_vals, validation_vals, colors=("r", "b")):
fig = tfmpl.create_figure(figsize=(4, 4))
ax = fig.add_subplot(111)
ax.plot(*zip(*error_vals), c=colors[0])
ax.plot(*zip(*validation_vals), c=colors[1])
return fig
def init_fig(*args, **kwargs):
'''Initialize figures.'''
fig = tfmpl.create_figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d', elev=50, azim=-30)
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.set_title('Gradient descent on Beale surface')
ax.set_xlabel('$x$')
ax.set_ylabel('$y$')
ax.set_zlabel('beale($x$,$y$)')
xx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 40),
np.linspace(-4.5, 4.5, 40))
zz = beale(xx, yy)
ax.plot_surface(xx,
yy,
zz,
norm=LogNorm(),
rstride=1,
cstride=1,
edgecolor='none',
alpha=.8,
cmap=cm.jet)
ax.plot([3], [.5], [beale(3, .5)], 'k*', markersize=5)
for o in optimizers:
path, = ax.plot([], [], [], label=o[1])
paths.append(path)
ax.legend(loc='upper left')
fig.tight_layout()
return fig, paths
def draw_scatter(vals, color):
fig = tfmpl.create_figure(figsize=(4, 4))
ax = fig.add_subplot(111)
ax.scatter(*zip(*vals), c=color)
fig.tight_layout()
return fig
def draw(a, b, c, d=None, e=None):
debug['a'] = a
debug['b'] = b
debug['c'] = c
debug['d'] = d
debug['e'] = e
return tfmpl.create_figure()
def init(t):
nonlocal rect
fig = tfmpl.create_figure(figsize=(4, 4), dpi=100)
ax = fig.add_axes([0, 0, 1, 1])
ax.invert_yaxis()
rect = ax.add_patch(
patches.Rectangle((0, 0), 0.1, 0.1, facecolor=(0, 1, 0)))
return fig, rect
def draw_hinton_plot(matrices,
trans=True,
max_val=None,
max_weight=None,
scale=True,
colors=("red", "blue"),
titles=(),
step=0):
headline = "Hinton plot, step %d" % step
if len(titles) == 0:
titles = ["Plot %d" % i for i in range(len(matrices))]
fig = tfmpl.create_figure()
fig.suptitle(headline, fontsize=18)
axes = fig.subplots(1, len(matrices))
for matrix, title, ax in zip(matrices, titles, axes):
ax.clear()
if trans:
matrix = matrix.transpose()
if max_val is None: # maxval
max_val = np.abs(matrix).max()
if max_weight is None: # maxsize
max_weight = 2**np.ceil(np.log(max_val) / np.log(2))
ax.set_title(title)
ax.patch.set_facecolor('gray')
ax.set_aspect('auto', 'box')
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
y_max = (matrix.shape[1] - 1) * max_weight
for (x, y), w in np.ndenumerate(matrix):
color = colors[0] if w > 0 else colors[1]
if scale:
size = max(
0.01,
np.sqrt(min(max_weight,
max_weight * np.abs(w) / max_val)))
else:
size = np.sqrt(min(np.abs(w), max_weight))
bottom_left = [x - size / 2, (y_max - y) - size / 2]
rect = plt.Rectangle(bottom_left,
size,
size,
facecolor=color,
edgecolor=color)
ax.add_patch(rect)
ax.autoscale_view()
ax.invert_yaxis()
buf = io.BytesIO()
fig.savefig(buf, format="png")
buf.seek(0)
return buf.getvalue()
def draw_scatter(self, data):
'''Draw scatter plots. One for each color.'''
fig = tfmpl.create_figure()
# plt.imshow(data[:, :, 0])
# plt.show()
ax = fig.add_subplot(111)
ax.imshow(data[:, :, 0])
fig.tight_layout()
return fig
def draw_matrix_tb(matrix, figsize=(4, 4)):
""" Matrix plotting for tensorboard """
fig = tfmpl.create_figure(figsize=figsize)
ax = fig.add_subplot(111)
im = ax.pcolor(matrix)
ax.invert_yaxis()
fig.colorbar(im)
fig.tight_layout()
return fig
def draw_confusion_matrix(matrix, axis_labels, figsize):
'''Draw confusion matrix for MNIST.'''
fig = tfmpl.create_figure(figsize=figsize)
ax = fig.add_subplot(111)
ax.set_title('Confusion matrix')
tfmpl.plots.confusion_matrix.draw(ax,
matrix,
axis_labels=axis_labels,
normalize=True)
return fig
def draw_confusion_matrix(matrix):
'''Draw confusion matrix for MNIST.'''
fig = tfmpl.create_figure(figsize=(7, 7))
ax = fig.add_subplot(111)
ax.set_title('Confusion matrix for big5 classification')
tfmpl.plots.confusion_matrix.draw(ax,
matrix,
axis_labels=['0', '1'],
normalize=True)
return fig
def draw_scatter_subplot(self, data1, data2):
'''Draw scatter plots. One for each color.'''
fig = tfmpl.create_figure()
ax = fig.add_subplot(122)
ax.imshow(data1[:, :, :])
ax = fig.add_subplot(121)
try:
ax.imshow(data2[:, :, :])
except:
ax.imshow(data2[:, :, 0])
fig.tight_layout()
return fig
def _create_tfmpl_figure(l, r, pair_label, left_label, right_label) -> Figure:
fig: Figure = tfmpl.create_figure()
a = fig.add_subplot(1, 2, 1)
a.imshow(l)
a.axis('off')
b = fig.add_subplot(1, 2, 2)
b.imshow(r)
b.axis('off')
fig.tight_layout()
if str(pair_label) == '1':
figure_title = "same ({}-{})".format(left_label, right_label)
else:
figure_title = "different ({}-{})".format(left_label, right_label)
fig.suptitle(figure_title, fontsize=32)
return fig
def draw_dendrogram(features,
labels,
metric='euclidean',
mode='average',
title='Dendrogram',
orient='top',
lrot=90.0):
fig = tfmpl.create_figure(figsize=(6, 10))
ax = fig.add_subplot(111)
print(features)
cluster_history = sch.linkage(features, method=mode, metric=metric)
sch.dendrogram(cluster_history,
labels=labels,
orientation=orient,
leaf_rotation=lrot,
ax=ax)
plt.tight_layout()
ax.set_title(title)
ax.set_ylabel(metric + ' distance')
buf = io.BytesIO()
fig.savefig(buf, format="png")
buf.seek(0)
return buf.getvalue()
def draw(a):
debug['called'] += 1
debug['a'].append(a)
return tfmpl.create_figure()
def init(a, b, c, d=None, e=None):
debug['init_args'] = [a, b, c, d, e]
return tfmpl.create_figure(), None
def init(a):
debug['init_called'] += 1
debug['a_init'] = a
return tfmpl.create_figure(), None
def draw_tf_clusters_plot(feat, labels):
fig: Figure = tfmpl.create_figure()
return _draw_scatter(fig, feat, labels)
