在Keras Tensorflow中提取特定预测的权重和偏差

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# 创建一个简单的模型
model = Sequential([
    Dense(64, activation='relu', input_shape=(784,)),
    Dense(10, activation='softmax')
])

# 编译模型
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 打印模型摘要
model.summary()

# 提取第一个全连接层的权重和偏差
layer_name = 'dense'
weights, biases = model.get_layer(layer_name).get_weights()

print(f"权重矩阵形状: {weights.shape}")
print(f"偏差向量形状: {biases.shape}")

# 打印权重和偏差的前几项以供查看
print("权重矩阵前几项:")
print(weights[:5, :5])
print("偏差向量前几项:")
print(biases[:5])