【python实现卷积神经网络】卷积层Conv2D实现（带stride、padding）
关于卷积操作是如何进行的就不必多说了，结合代码一步一步来看卷积层是怎么实现的。
代码来源：https://github.com/eriklindernoren/ML-From-Scratch
先看一下其基本的组件函数，首先是determine_padding(filter_shape, output_shape="same")：
def determine_padding(filter_shape, output_shape="same"):
# No padding
if output_shape == "valid":
return (0, 0), (0, 0)
# Pad so that the output shape is the same as input shape (given that stride=1)
elif output_shape == "same":
filter_height, filter_width = filter_shape
# Derived from:
# output_height = (height + pad_h - filter_height) / stride + 1
# In this case output_height = height and stride = 1. This gives the
# expression for the padding below.
pad_h1 = int(math.floor((filter_height - 1)/2))
pad_h2 = int(math.ceil((filter_height - 1)/2))
pad_w1 = int(math.floor((filter_width - 1)/2))
pad_w2 = int(math.ceil((filter_width - 1)/2))
return (pad_h1, pad_h2), (pad_w1, pad_w2)
说明：根据卷积核的形状以及padding的方式来计算出padding的值，包括上、下、左、右，其中out_shape=valid表示不填充。
补充：
math.floor(x)表示返回小于或等于x的最大整数。
math.ceil(x)表示返回大于或等于x的最大整数。
带入实际的参数来看下输出：
pad_h,pad_w=determine_padding((3,3), output_shape="same")
输出：(1,1),(1,1)
然后是image_to_column(images, filter_shape, stride, output_shape='same')函数
def image_to_column(images, filter_shape, stride, output_shape='same'):
filter_height, filter_width = filter_shape
pad_h, pad_w = determine_padding(filter_shape, output_shape)# Add padding to the image
images_padded = np.pad(images, ((0, 0), (0, 0), pad_h, pad_w), mode='constant')# Calculate the indices where the dot products are to be applied between weights
# and the image
k, i, j = get_im2col_indices(images.shape, filter_shape, (pad_h, pad_w), stride)
# Get content from image at those indices
cols = images_padded[:, k, i, j]
channels = images.shape[1]
# Reshape content into column shape
cols = cols.transpose(1, 2, 0).reshape(filter_height * filter_width * channels, -1)
return cols
说明：输入的images的形状是[batchsize,channel,height,width]，类似于pytorch的图像格式的输入。也就是说images_padded是在height和width上进行padding的。在其中调用了get_im2col_indices()函数，那我们接下来看看它是个什么样子的：
def get_im2col_indices(images_shape, filter_shape, padding, stride=1):
# First figure out what the size of the output should be
batch_size, channels, height, width = images_shape
filter_height, filter_width = filter_shape
pad_h, pad_w = padding
out_height = int((height + np.sum(pad_h) - filter_height) / stride + 1)
out_width = int((width + np.sum(pad_w) - filter_width) / stride + 1)
i0 = np.repeat(np.arange(filter_height), filter_width)
i0 = np.tile(i0, channels)
i1 = stride * np.repeat(np.arange(out_height), out_width)
j0 = np.tile(np.arange(filter_width), filter_height * channels)
j1 = stride * np.tile(np.arange(out_width), out_height)
i = i0.reshape(-1, 1) + i1.reshape(1, -1)
j = j0.reshape(-1, 1) + j1.reshape(1, -1)
k = np.repeat(np.arange(channels), filter_height * filter_width).reshape(-1, 1)return (k, i, j)
说明：单独看很难理解，我们还是带着带着实际的参数一步步来看。
get_im2col_indices((1,3,32,32), (3,3), ((1,1),(1,1)), stride=1)
说明：看一下每一个变量的变化情况，out_width和out_height就不多说，是卷积之后的输出的特征图的宽和高维度。
i0：np.repeat(np.arange(3),3)：[0 ,0,0,1,1,1,2,2,2]
i0：np.tile([0,0,0,1,1,1,2,2,2],3)：[0,0,0,1,1,1,2,2,2,0,0,0,1,1,1,2,2,2,0,0,0,1,1,1,2,2,2]，大小为：(27,)
i1：1*np.repeat(np.arange(32),32)：[0,0,0......,31,31,31]，大小为：(1024,)
j0：np.tile(np.arange(3),3*3)：[0,1,2,0,1,2,......]，大小为：(27,)
j1：1*np.tile(np.arange(32),32)：[0,1,2,3,......,0,1,2,......,29,30,31]，大小为(1024,)
i：i0.reshape(-1,1)+i1.reshape(1,-1)：大小(27,1024)
j：j0.reshape(-1,1)+j1.reshape(1,-1)：大小(27,1024)
k：np.repeat(np.arange(3),3*3).reshape(-1,1)：大小(27,1)
补充：
numpy.pad(array, pad_width, mode, **kwargs)：array是要要被填充的数据，第二个参数指定填充的长度，mod用于指定填充的数据，默认是0，如果是constant，则需要指定填充的值。
numpy.arange(start, stop, step, dtype = None)：举例numpy.arange(3)，输出[0,1,2]
numpy.repeat(array,repeats,axis=None)：举例numpy.repeat([0,1,2],3)，输出：[0,0,0,1,1,1,2,2,2]
numpy.tile(array,reps)：举例numpy.tile([0,1,2],3)，输出：[0,1,2,0,1,2,0,1,2]
具体的更复杂的用法还是得去查相关资料。这里只列举出与本代码相关的。
有了这些大小还是挺难理解的呀。那么我们继续，需要明确的是k是对通道进行操作，i是对特征图的高，j是对特征图的宽。使用3×3的卷积核在一个通道上进行卷积，每次执行3×3=9个像素操作，共3个通道，所以共对9×3=27个像素点进行操作。而图像大小是32×32，共1024个像素。再回去看这三行代码：
cols = images_padded[:, k, i, j]
channels = images.shape[1]
# Reshape content into column shape
cols = cols.transpose(1, 2, 0).reshape(filter_height * filter_width * channels, -1)images_padded的大小是(1,3,34,34)，则cols=images_padded的大小是(1,27,1024)
channels的大小是3
最终cols=cols.transpose(1,2,0).reshape(333,-1)的大小是(27,1024)。
当batchsize的大小不是1，假设是64时，那么最终输出的cols的大小就是：(27,1024×64)=(27,65536)。
最后就是卷积层的实现了：
首先有一个Layer通用基类，通过继承该基类可以实现不同的层，例如卷积层、池化层、批量归一化层等等：
class Layer(object):
def set_input_shape(self, shape):
""" Sets the shape that the layer expects of the input in the forward
pass method """
self.input_shape = shape
def layer_name(self):
""" The name of the layer. Used in model summary. """
return self.__class__.__name__
def parameters(self):
""" The number of trainable parameters used by the layer """
return 0
def forward_pass(self, X, training):
""" Propogates the signal forward in the network """
raise NotImplementedError()
def backward_pass(self, accum_grad):
""" Propogates the accumulated gradient backwards in the network.
If the has trainable weights then these weights are also tuned in this method.
As input (accum_grad) it receives the gradient with respect to the output of the layer and
returns the gradient with respect to the output of the previous layer. """
raise NotImplementedError()
def output_shape(self):
""" The shape of the output produced by forward_pass """
raise NotImplementedError()
对于子类继承该基类必须要实现的方法，如果没有实现使用raise NotImplementedError()抛出异常。
接着就可以基于该基类实现Conv2D了：
class Conv2D(Layer):
"""A 2D Convolution Layer.
Parameters:
-----------
n_filters: int
The number of filters that will convolve over the input matrix. The number of channels
of the output shape.
filter_shape: tuple
A tuple (filter_height, filter_width).
input_shape: tuple
The shape of the expected input of the layer. (batch_size, channels, height, width)
Only needs to be specified for first layer in the network.
padding: string
Either 'same' or 'valid'. 'same' results in padding being added so that the output height and width
matches the input height and width. For 'valid' no padding is added.
stride: int
The stride length of the filters during the convolution over the input.
"""
def __init__(self, n_filters, filter_shape, input_shape=None, padding='same', stride=1):
self.n_filters = n_filters
self.filter_shape = filter_shape
self.padding = padding
self.stride = stride
self.input_shape = input_shape
self.trainable = True
def initialize(self, optimizer):
# Initialize the weights
filter_height, filter_width = self.filter_shape
channels = self.input_shape[0]
limit = 1 / math.sqrt(np.prod(self.filter_shape))
self.W = np.random.uniform(-limit, limit, size=(self.n_filters, channels, filter_height, filter_width))
self.w0 = np.zeros((self.n_filters, 1))
# Weight optimizers
self.W_opt = copy.copy(optimizer)
self.w0_opt = copy.copy(optimizer)
def parameters(self):
return np.prod(self.W.shape) + np.prod(self.w0.shape)
def forward_pass(self, X, training=True):
batch_size, channels, height, width = X.shape
self.layer_input = X
# Turn image shape into column shape
# (enables dot product between input and weights)
self.X_col = image_to_column(X, self.filter_shape, stride=self.stride, output_shape=self.padding)
# Turn weights into column shape
self.W_col = self.W.reshape((self.n_filters, -1))
# Calculate output
output = self.W_col.dot(self.X_col) + self.w0
# Reshape into (n_filters, out_height, out_width, batch_size)
output = output.reshape(self.output_shape() + (batch_size, ))
# Redistribute axises so that batch size comes first
return output.transpose(3,0,1,2)
def backward_pass(self, accum_grad):
# Reshape accumulated gradient into column shape
accum_grad = accum_grad.transpose(1, 2, 3, 0).reshape(self.n_filters, -1)
if self.trainable:
# Take dot product between column shaped accum. gradient and column shape
# layer input to determine the gradient at the layer with respect to layer weights
grad_w = accum_grad.dot(self.X_col.T).reshape(self.W.shape)
# The gradient with respect to bias terms is the sum similarly to in Dense layer
grad_w0 = np.sum(accum_grad, axis=1, keepdims=True)
# Update the layers weights
self.W = self.W_opt.update(self.W, grad_w)
self.w0 = self.w0_opt.update(self.w0, grad_w0)
# Recalculate the gradient which will be propogated back to prev. layer
accum_grad = self.W_col.T.dot(accum_grad)
# Reshape from column shape to image shape
accum_grad = column_to_image(accum_grad,
self.layer_input.shape,
self.filter_shape,
stride=self.stride,
output_shape=self.padding)
return accum_grad
def output_shape(self):
channels, height, width = self.input_shape
pad_h, pad_w = determine_padding(self.filter_shape, output_shape=self.padding)
output_height = (height + np.sum(pad_h) - self.filter_shape[0]) / self.stride + 1
output_width = (width + np.sum(pad_w) - self.filter_shape[1]) / self.stride + 1
return self.n_filters, int(output_height), int(output_width)
假设输入还是(1,3,32,32)的维度，使用16个3×3的卷积核进行卷积，那么self.W的大小就是(16,3,3,3)，self.w0的大小就是(16,1)。
self.X_col的大小就是(27,1024)，self.W_col的大小是(16,27)，那么output = self.W_col.dot(self.X_col) + self.w0的大小就是(16,1024)
最后是这么使用的：
image = np.random.randint(0,255,size=(1,3,32,32)).astype(np.uint8)
input_shape=image.squeeze().shape
conv2d = Conv2D(16, (3,3), input_shape=input_shape, padding='same', stride=1)
conv2d.initialize(None)
output=conv2d.forward_pass(image,training=True)
print(output.shape)
输出结果：(1,16,32,32)
计算下参数：
print(conv2d.parameters())
输出结果：448
也就是448=3×3×3×16+16
再是一个padding=valid的：
image = np.random.randint(0,255,size=(1,3,32,32)).astype(np.uint8)
input_shape=image.squeeze().shape
conv2d = Conv2D(16, (3,3), input_shape=input_shape, padding='valid', stride=1)
conv2d.initialize(None)
output=conv2d.forward_pass(image,training=True)
print(output.shape)
print(conv2d.parameters())
需要注意的是cols的大小变化了，因为我们卷积之后的输出是(1,16,30,30)
输出：
cols的大小：(27,900)
(1,16,30,30)
448
最后是带步长的：
image = np.random.randint(0,255,size=(1,3,32,32)).astype(np.uint8)
input_shape=image.squeeze().shape
conv2d = Conv2D(16, (3,3), input_shape=input_shape, padding='valid', stride=2)
conv2d.initialize(None)
output=conv2d.forward_pass(image,training=True)
print(output.shape)
print(conv2d.parameters())
cols的大小：(27,225)
(1,16,15,15)
448
最后补充下：
卷积层参数计算公式 ：params=卷积核高×卷积核宽×通道数目×卷积核数目+偏置项（卷积核数目）
卷积之后图像大小计算公式：
输出图像的高=(输入图像的高+padding（高）×2-卷积核高)/步长+1
输出图像的宽=(输入图像的宽+padding（宽）×2-卷积核宽)/步长+1
get_im2col_indices()函数中的变换操作是清楚了，至于为什么这么变换的原因还需要好好去琢磨。至于反向传播和优化optimizer等研究好了之后再更新了。
