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scripts.py
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scripts.py
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import numpy as np
import keras
import matplotlib.pyplot as plt
fig_font_size = 30
def visualize_kernel(kernel, fig):
"""
Displays kernel weight as a plot.
returns: plot of weights
"""
im = fig.imshow(np.abs(kernel),
interpolation='none',
cmap='inferno',
clim=0)
plt.colorbar(im, ax=fig)
return fig
def quad_plot(new, old, output):
fig = plt.figure(constrained_layout=True, figsize=(24, 12))
gs = plt.GridSpec(2, 2, figure=fig)
ax2 = fig.add_subplot(gs[0, 0])
ax3 = fig.add_subplot(gs[0, 1])
ax4 = fig.add_subplot(gs[1, 0])
ax5 = fig.add_subplot(gs[1, 1])
ax4.hist(np.abs(old.flatten()), density=False, width=.1, bins='auto')
ax4.set_title("Original Weight Distribution", fontsize=fig_font_size)
ax5.hist(np.abs(new.flatten()), density=False, width=.1, bins='auto')
ax5.set_title("Pruned Weight Distribution", fontsize=fig_font_size)
visualize_kernel(old, ax2)
ax2.set_title("Original Weight Plot", fontsize=fig_font_size)
visualize_kernel(new, ax3)
ax3.set_title("Pruned Weight Plot", fontsize=fig_font_size)
plt.savefig(output)
return output
def calculate_layer_sparsity(layer):
"""
Calculates the fraction of values in a tensor that are 0.
args:
- layer: the desired layer to be evaluated
returns: fraction of zero values in ndarray
"""
return 1 - np.count_nonzero(layer) / np.product(layer.shape)
def calculate_model_sparsity(model):
"""
Calculates the fraction of values in a model that are 0.
args:
- model: the desired model to be evaluated
returns: fraction of zero values in model
"""
return np.average([calculate_layer_sparsity(layer) for layer in model],
weights=[np.product(layer.shape) for layer in model])