如何在 keras 中使用 Conv2D.set_weights？答案

【问题标题】：How to use Conv2D.set_weights in keras?如何在 keras 中使用 Conv2D.set_weights？
【发布时间】：2020-02-24 07:13:24
【问题描述】：

我遵循Keras:Applications 中的“使用 VGG19 从任意中间层提取特征”部分。我可以得到输入：input_1和第一层卷积：block1_conv1的权重和偏差，以及该层的输出。

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input, decode_predictions
from keras.models import Model
import numpy as np

base_model = VGG16(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('block1_conv1').output)
model.summary()

# Get weight and bias of block1_conv1
block1_conv1 = model.get_layer("block1_conv1")
weight, bias = block1_conv1.get_weights()
print("weight dtype:{}, shape:{}".format(weight.dtype, weight.shape))
print("bias length:{}".format(len(bias)))

# Get the image input
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# Get the output of block1_conv1
block1_conv1_features = model.predict(x)
print(block1_conv1_features.size, block1_conv1_features.shape)
print(block1_conv1_features[0][0][0][:5])

现在，我想创建一个 keras.layers 的 Conv2D(keras.layers import Conv2D)，设置相同的 input:x 和 weight/bias，并期望它具有相同的输出。但是我不知道该怎么做，有人可以给我一些建议吗？非常感谢。

【问题讨论】：

标签： python keras

【解决方案1】：

# get the configuration the this layer or set by yourself
config = model.get_layer('block1_conv1').get_config()

conv_model = Sequential()
conv_model.add(Conv2D(config['filters'], kernel_size=config['kernel_size'], \
    strides=config['strides'], input_shape=(224, 224, 3), \
    padding=config['padding'], data_format=config['data_format'], \
    activation=config['activation'], use_bias=config['use_bias']))
model.set_weights([w, b])
# calculate the layer
out = model.predict(x)
# compare the result
np.array_equal(block1_conv1_features, out)

【讨论】：