def plot_sequence_filters(dnn):
fig = plt.figure(figsize=(15, 8))
fig.subplots_adjust(hspace=0.1, wspace=0.1)
conv_filters = dnn.get_sequence_filters()
num_plots_per_axis = int(len(conv_filters)**0.5) + 1
for i, conv_filter in enumerate(conv_filters):
ax = fig.add_subplot(num_plots_per_axis, num_plots_per_axis, i + 1)
add_letters_to_axis(ax, conv_filter.T)
ax.axis("off")
ax.set_title("Filter %s" % (str(i + 1)))
def plot_sequence_filters(dnn):
fig = plt.figure(figsize=(15, 8))
fig.subplots_adjust(hspace=0.1, wspace=0.1)
conv_filters = dnn.get_sequence_filters()
num_plots_per_axis = int(len(conv_filters)**0.5) + 1
for i, conv_filter in enumerate(conv_filters):
ax = fig.add_subplot(num_plots_per_axis, num_plots_per_axis, i+1)
add_letters_to_axis(ax, conv_filter.T)
ax.axis("off")
ax.set_title("Filter %s" % (str(i+1)))
def plot_sequence_filters(model):
fig = plt.figure(figsize=(15, 8))
fig.subplots_adjust(hspace=0.1, wspace=0.1)
conv_filters = model.layers[0].get_weights()[0]
#transpose for plotting
conv_filters = np.transpose(conv_filters, (3, 1, 2, 0)).squeeze(axis=-1)
num_plots_per_axis = int(len(conv_filters)**0.5) + 1
for i, conv_filter in enumerate(conv_filters):
ax = fig.add_subplot(num_plots_per_axis, num_plots_per_axis, i + 1)
add_letters_to_axis(ax, conv_filter)
ax.axis("off")
ax.set_title("Filter %s" % (str(i + 1)))
def plot_sequence_filters(model):
fig = plt.figure(figsize=(15, 8))
fig.subplots_adjust(hspace=0.1, wspace=0.1)
conv_filters=model.layers[0].get_weights()[0]
#transpose for plotting
conv_filters=np.transpose(conv_filters,(3,1,2,0)).squeeze(axis=-1)
num_plots_per_axis = int(len(conv_filters)**0.5) + 1
for i, conv_filter in enumerate(conv_filters):
ax = fig.add_subplot(num_plots_per_axis, num_plots_per_axis, i+1)
add_letters_to_axis(ax, conv_filter)
ax.axis("off")
ax.set_title("Filter %s" % (str(i+1)))
def plot_sequence_filters(model, prefix):
'''
model - a trained SequenceDNN
prefix - the name of the loaded model
'''
fig = plt.figure(figsize=(15, 8))
fig.subplots_adjust(hspace=0.1, wspace=0.1)
conv_filters = model.get_sequence_filters()
num_plots_per_axis = int(len(conv_filters)**0.5) + 1
for i, conv_filter in enumerate(conv_filters):
ax = fig.add_subplot(num_plots_per_axis, num_plots_per_axis, i + 1)
add_letters_to_axis(ax, conv_filter.T)
ax.axis('off')
ax.set_title('Filter %s' % (str(i + 1)))
fig.savefig('{}_convolutional_filters.png'.format(prefix),
bbox_inches='tight')