目录
1.应用场景
2.卷积神经网络结构
2.1 卷积（convelution）
2.2 Relu激活函数
2.3 池化（pool）
2.4 全连接（full connection）
2.5 损失函数（softmax_loss）
2.6 前向传播（forward propagation）
2.7 反向传播（backford propagation）
2.8 随机梯度下降（sgd_momentum）
3.代码实现流程图以及介绍
4.代码实现（python3.6）
5.运行结果以及分析
6.参考文献
# -*- coding: utf-8 -*-
try:
from . import layers
except Exception:
import layers
def affine_relu_forward(x, w, b):
"""
Convenience layer that perorms an affine transform followed by a ReLU
Inputs:
-x: Input to the affine layer
-w, b: Weights for the affine layer
Returns a tuple of:
-out: Output from the ReLU
-cache: Object to give to the backward pass
"""
a,fc_cache = layers.affine_forward(x, w, b)
out, relu_cache = layers.relu_forward(a)
cache = (fc_cache, relu_cache)
return out, cache
def affine_relu_backward(dout, cache):
"""
Backward pass for the affine-relu convenience layer
"""
fc_cache, relu_cache = cache
da= layers.relu_backward(dout, relu_cache)
dx,dw, db = layers.affine_backward(da, fc_cache)
return dx, dw, db
pass
def conv_relu_forward(x, w, b, conv_param):
"""
Aconvenience layer that performs a convolution followed by a ReLU.
Inputs:
-x: Input to the convolutional layer
-w, b, conv_param: Weights and parameters for the convolutional layer
Returns a tuple of:
-out: Output from the ReLU
-cache: Object to give to the backward pass
"""
a,conv_cache = layers.conv_forward_fast(x, w, b, conv_param)
out, relu_cache = layers.relu_forward(a)
cache = (conv_cache, relu_cache)
return out, cache
def conv_relu_backward(dout, cache):
"""
Backward pass for the conv-relu convenience layer.
"""
conv_cache, relu_cache = cache
da= layers.relu_backward(dout, relu_cache)
dx,dw, db = layers.conv_backward_fast(da, conv_cache)
return dx, dw, db
def conv_relu_pool_forward(x, w, b,conv_param, pool_param):
"""
Convenience layer that performs a convolution, a ReLU, and a pool.
Inputs:
-x: Input to the convolutional layer
-w, b, conv_param: Weights and parameters for the convolutional layer
-pool_param: Parameters for the pooling layer
Returns a tuple of:
-out: Output from the pooling layer
-cache: Object to give to the backward pass
"""
a,conv_cache = layers.conv_forward_naive(x, w, b, conv_param)
s,relu_cache = layers.relu_forward(a)
out, pool_cache = layers.max_pool_forward_naive(s, pool_param)
cache = (conv_cache, relu_cache, pool_cache)
return out, cache
def conv_relu_pool_backward(dout, cache):
"""
Backward pass for the conv-relu-pool convenience layer
"""
conv_cache, relu_cache, pool_cache = cache
ds= layers.max_pool_backward_naive(dout, pool_cache)
da= layers.relu_backward(ds, relu_cache)
dx,dw, db = layers.conv_backward_naive(da, conv_cache)
return dx, dw, db
layers.py
import numpy as np
'''
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