feature map 可视化

一个简单的栗子实现特征图可视化

# coding: utf-8

from keras.models import Model
import cv2
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers import Activation
from pylab import *
import keras


def get_row_col(num_pic):
    squr = num_pic ** 0.5
    row = round(squr)
    col = row + 1 if squr - row > 0 else row
    return row, col


def visualize_feature_map(img_batch):
    feature_map = np.squeeze(img_batch, axis=0)
    print(feature_map.shape)

    feature_map_combination = []
    plt.figure()

    num_pic = feature_map.shape[2]
    row, col = get_row_col(num_pic)

    for i in range(0, num_pic):
        feature_map_split = feature_map[:, :, i]
        feature_map_combination.append(feature_map_split)
        plt.subplot(row, col, i + 1)
        plt.imshow(feature_map_split)
        axis('off')
        title('feature_map_{}'.format(i))
    plt.savefig('feature_map.png')
    plt.show()
    feature_map_sum = sum(ele for ele in feature_map_combination)
    plt.imshow(feature_map_sum)
    plt.savefig("feature_map_sum.png")
def create_model():
    model = Sequential()
    model.add(Convolution2D(9, 5, 5, input_shape=img.shape))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(4, 4)))
    model.add(Convolution2D(9, 5, 5, input_shape=img.shape))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(3, 3)))
    model.add(Convolution2D(9, 5, 5, input_shape=img.shape))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(9, 3, 3, input_shape=img.shape))
    model.add(Activation('relu'))


    return model


if __name__ == "__main__":
    img = cv2.imread('001.jpg')

    model = create_model()

    img_batch = np.expand_dims(img, axis=0)
    conv_img = model.predict(img_batch)

    visualize_feature_map(conv_img)

原始图像每一层的feature map
feature map 求和