def plot_conv_output(conv_img, name, filters_all=True, filters=[0]):
"""
Makes plots of results of performing convolution
:param conv_img: numpy array of rank 4
:param name: string, name of convolutional layer
:return: nothing, plots are saved on the disk
"""
# make path to output folder
plot_dir = os.path.join(PLOT_DIR, 'conv_output')
plot_dir = os.path.join(plot_dir, name)
# create directory if does not exist, otherwise empty it
utils.prepare_dir(plot_dir, empty=True)
w_min = np.min(conv_img)
w_max = np.max(conv_img)
# get number of convolutional filters
if filters_all:
num_filters = conv_img.shape[3]
filters = range(conv_img.shape[3])
else:
num_filters = len(filters)
# get number of grid rows and columns
grid_r, grid_c = utils.get_grid_dim(num_filters)
# create figure and axes
fig, axes = plt.subplots(min([grid_r, grid_c]), max([grid_r, grid_c]))
if num_filters == 1:
img = conv_img[0, :, :, filters[0]]
axes.imshow(img,
vmin=w_min,
vmax=w_max,
interpolation='bicubic',
cmap='Greys')
# remove any labels from the axes
axes.set_xticks([])
axes.set_yticks([])
# iterate filters
else:
for l, ax in enumerate(axes.flat):
# get a single image
img = conv_img[0, :, :, filters[l]]
# put it on the grid
ax.imshow(img,
vmin=w_min,
vmax=w_max,
interpolation='bicubic',
cmap='Greys')
# remove any labels from the axes
ax.set_xticks([])
ax.set_yticks([])
# save figure
plt.savefig(os.path.join(plot_dir, '{}.png'.format(name)),
bbox_inches='tight')
def plot_conv_weights(weights, name, channels_all=True):
"""
Plots convolutional filters
:param weights: numpy array of rank 4
:param name: string, name of convolutional layer
:param channels_all: boolean, optional
:return: nothing, plots are saved on the disk
"""
# make path to output folder
plot_dir = os.path.join(PLOT_DIR, 'conv_weights')
plot_dir = os.path.join(plot_dir, name)
# create directory if does not exist, otherwise empty it
utils.prepare_dir(plot_dir, empty=True)
w_min = np.min(weights)
w_max = np.max(weights)
channels = [0]
# make a list of channels if all are plotted
if channels_all:
channels = range(weights.shape[2])
# get number of convolutional filters
num_filters = weights.shape[3]
# get number of grid rows and columns
grid_r, grid_c = utils.get_grid_dim(num_filters)
# create figure and axes
fig, axes = plt.subplots(min([grid_r, grid_c]), max([grid_r, grid_c]))
# iterate channels
for channel in channels:
# iterate filters inside every channel
for l, ax in enumerate(axes.flat):
# get a single filter
img = weights[:, :, channel, l]
# put it on the grid
ax.imshow(img,
vmin=w_min,
vmax=w_max,
interpolation='nearest',
cmap='seismic')
# remove any labels from the axes
ax.set_xticks([])
ax.set_yticks([])
# save figure
plt.savefig(os.path.join(plot_dir, '{}-{}.png'.format(name, channel)),
bbox_inches='tight')
def plot_conv_weights(weights, name, channels_all=True):
"""
Plots convolutional filters
:param weights: numpy array of rank 4
:param name: string, name of convolutional layer
:param channels_all: boolean, optional
:return: nothing, plots are saved on the disk
"""
# make path to output folder
plot_dir = os.path.join(PLOT_DIR, 'conv_weights')
plot_dir = os.path.join(plot_dir, name)
# create directory if does not exist, otherwise empty it
utils.prepare_dir(plot_dir, empty=True)
w_min = np.min(weights)
w_max = np.max(weights)
channels = [0]
# make a list of channels if all are plotted
if channels_all:
channels = range(weights.shape[2])
# get number of convolutional filters
num_filters = weights.shape[3]
# get number of grid rows and columns
grid_r, grid_c = utils.get_grid_dim(num_filters)
# create figure and axes
fig, axes = plt.subplots(min([grid_r, grid_c]),
max([grid_r, grid_c]))
# iterate channels
for channel in channels:
# iterate filters inside every channel
for l, ax in enumerate(axes.flat):
# get a single filter
img = weights[:, :, channel, l]
# put it on the grid
ax.imshow(img, vmin=w_min, vmax=w_max, interpolation='nearest', cmap='seismic')
# remove any labels from the axes
ax.set_xticks([])
ax.set_yticks([])
# save figure
plt.savefig(os.path.join(plot_dir, '{}-{}.png'.format(name, channel)), bbox_inches='tight')
def plot_conv_output(conv_img, plot_dir, name, filters_all=True, filters=[0]):
w_min = np.min(conv_img)
w_max = np.max(conv_img)
# get number of convolutional filters
if filters_all:
num_filters = conv_img.shape[3]
filters = range(conv_img.shape[3])
else:
num_filters = len(filters)
# get number of grid rows and columns
grid_r, grid_c = utils.get_grid_dim(num_filters)
# create figure and axes
fig, axes = plt.subplots(min([grid_r, grid_c]), max([grid_r, grid_c]))
# iterate filters
if num_filters == 1:
img = conv_img[0, :, :, filters[0]]
axes.imshow(img,
vmin=w_min,
vmax=w_max,
interpolation='bicubic',
cmap=cm.hot)
# remove any labels from the axes
axes.set_xticks([])
axes.set_yticks([])
else:
for l, ax in enumerate(axes.flat):
# get a single image
img = conv_img[0, :, :, filters[l]]
# put it on the grid
ax.imshow(img,
vmin=w_min,
vmax=w_max,
interpolation='bicubic',
cmap=cm.hot)
# remove any labels from the axes
ax.set_xticks([])
ax.set_yticks([])
# save figure
print(os.path.join(plot_dir, '{}.png'.format(name)))
plt.savefig(os.path.join(plot_dir, '{}.png'.format(name)),
bbox_inches='tight')
def plot_weights(weights, name, path):
channels = weights.shape[2]
grid_r, grid_c = utils.get_grid_dim(int(channels))
fig, axes = plt.subplots(min([grid_r, grid_c]), max([grid_r, grid_c]))
w_min = np.min(weights)
w_max = np.max(weights)
for i, ax in enumerate(axes.flat):
img = weights[:, :, i]
ax.imshow(img,
vmin=w_min,
vmax=w_max,
interpolation='nearest',
cmap='Greys')
ax.set_xticks([])
ax.set_yticks([])
plt.savefig(path + name, bbox_inches='tight')
def plot_conv_output(conv_img, name):
"""
Makes plots of results of performing convolution
:param conv_img: numpy array of rank 4
:param name: string, name of convolutional layer
:return: nothing, plots are saved on the disk
"""
# make path to output folder
plot_dir = os.path.join(PLOT_DIR, 'feature_maps')
plot_dir = os.path.join(plot_dir, name)
plt.figure(num=None, figsize=(16, 12), dpi=80)
# create directory if does not exist, otherwise empty it
utils.prepare_dir(plot_dir, empty=True)
# w_min = np.min(conv_img)
# w_max = np.max(conv_img)
# get number of convolutional filters
num_filters = conv_img.shape[1]
# get number of grid rows and columns
grid_r, grid_c = utils.get_grid_dim(num_filters)
# create figure and axes
fig, axes = plt.subplots(min([grid_r, grid_c]),
max([grid_r, grid_c]),
figsize=(12, 8))
# iterate filters
for l, ax in enumerate(axes.flat):
# get a single image
img = conv_img[0, l, :, :]
# put it on the grid
ax.imshow(img, interpolation='bicubic', cmap='Greys')
# remove any labels from the axes
ax.set_xticks([])
ax.set_yticks([])
# save figure
plt.savefig(os.path.join(plot_dir, '{}.png'.format(name)),
bbox_inches='tight')
def plot_conv_output(conv_img, name):
"""
Makes plots of results of performing convolution
:param conv_img: numpy array of rank 4
:param name: string, name of convolutional layer
:return: nothing, plots are saved on the disk
"""
# make path to output folder
plot_dir = os.path.join(PLOT_DIR, 'conv_output')
plot_dir = os.path.join(plot_dir, name)
# create directory if does not exist, otherwise empty it
utils.prepare_dir(plot_dir, empty=True)
w_min = np.min(conv_img)
w_max = np.max(conv_img)
# get number of convolutional filters
num_filters = conv_img.shape[3]
# get number of grid rows and columns
grid_r, grid_c = utils.get_grid_dim(num_filters)
# create figure and axes
fig, axes = plt.subplots(min([grid_r, grid_c]),
max([grid_r, grid_c]))
# iterate filters
for l, ax in enumerate(axes.flat):
# get a single image
img = conv_img[0, :, :, l]
# put it on the grid
ax.imshow(img, vmin=w_min, vmax=w_max, interpolation='bicubic', cmap='Greys')
# remove any labels from the axes
ax.set_xticks([])
ax.set_yticks([])
# save figure
plt.savefig(os.path.join(plot_dir, '{}.png'.format(name)), bbox_inches='tight')
