在Pytorch和Tensorflow中有没有工具来分析DNN的每一层？

import torch
from torch.profiler import profile, record_function, ProfilerActivity

model = ...  # Your DNN model
input_data = ...  # Input data for your model

with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
    with record_function("model_inference"):
        model(input_data)

print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))

python -m torch.utils.bott督办 -m your_script.py

import tensorflow as tf

model = ...  # Your DNN model
input_data = ...  # Input data for your model

# 创建一个FileWriter来保存摘要数据
log_dir = "logs/"
writer = tf.summary.create_file_writer(log_dir)

# 在训练循环中记录摘要数据
with writer.as_default():
    for epoch in range(epochs):
        # ... 训练代码 ...
        tf.summary.histogram("layer_name/weights", model.get_layer("layer_name").kernel, step=epoch)
        tf.summary.histogram("layer_name/activations", layer_output, step=epoch)

tensorboard --logdir logs/

import tensorflow as tf
from tensorflow.python.profiler import profiler_v2 as profiler

model = ...  # Your DNN model
input_data = ...  # Input治疗for your model

# 启动性能分析器
profiler.start('logdir')

# 运行模型
model(input_data)

# 停止性能分析器并保存结果
profiler.stop()