02.course-convolution-and-relu【卷积和ReLU】
import numpy as np from itertools import product def show_kernel(kernel, label=True, digits=None, text_size=28): # Format kernel kernel = np.array(kernel) if digits is not None: kernel = kernel.round(digits) # Plot kernel cmap = plt.get_cmap('Blues_r') plt.imshow(kernel, cmap=cmap) rows, cols = kernel.shape thresh = (kernel.max()+kernel.min())/2 # Optionally, add value labels if label: for i, j in product(range(rows), range(cols)): […]