From 41285de9cd6f19d59c29395aaabdb865f936e255 Mon Sep 17 00:00:00 2001 From: p4w2aybsf <2363061197@qq.com> Date: Thu, 29 Apr 2021 17:16:25 +0800 Subject: [PATCH] Add 'layers.py' --- layers.py | 284 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 284 insertions(+) create mode 100644 layers.py diff --git a/layers.py b/layers.py new file mode 100644 index 0000000..ae50d80 --- /dev/null +++ b/layers.py @@ -0,0 +1,284 @@ +# coding: utf-8 +import numpy as np +from common.functions import * +from common.util import im2col, col2im + + +class Relu: + def __init__(self): + self.mask = None + + def forward(self, x): + self.mask = (x <= 0) + out = x.copy() + out[self.mask] = 0 + + return out + + def backward(self, dout): + dout[self.mask] = 0 + dx = dout + + return dx + + +class Sigmoid: + def __init__(self): + self.out = None + + def forward(self, x): + out = sigmoid(x) + self.out = out + return out + + def backward(self, dout): + dx = dout * (1.0 - self.out) * self.out + + return dx + + +class Affine: + def __init__(self, W, b): + self.W =W + self.b = b + + self.x = None + self.original_x_shape = None + # 权重和偏置参数的导数 + self.dW = None + self.db = None + + def forward(self, x): + # 对应张量 + self.original_x_shape = x.shape + x = x.reshape(x.shape[0], -1) + self.x = x + + out = np.dot(self.x, self.W) + self.b + + return out + + def backward(self, dout): + dx = np.dot(dout, self.W.T) + self.dW = np.dot(self.x.T, dout) + self.db = np.sum(dout, axis=0) + + dx = dx.reshape(*self.original_x_shape) # 还原输入数据的形状（对应张量） + return dx + + +class SoftmaxWithLoss: + def __init__(self): + self.loss = None + self.y = None # softmax的输出 + self.t = None # 监督数据 + + def forward(self, x, t): + self.t = t + self.y = softmax(x) + self.loss = cross_entropy_error(self.y, self.t) + + return self.loss + + def backward(self, dout=1): + batch_size = self.t.shape[0] + if self.t.size == self.y.size: # 监督数据是one-hot-vector的情况 + dx = (self.y - self.t) / batch_size + else: + dx = self.y.copy() + dx[np.arange(batch_size), self.t] -= 1 + dx = dx / batch_size + + return dx + + +class Dropout: + """ + http://arxiv.org/abs/1207.0580 + """ + def __init__(self, dropout_ratio=0.5): + self.dropout_ratio = dropout_ratio + self.mask = None + + def forward(self, x, train_flg=True): + if train_flg: + self.mask = np.random.rand(*x.shape) > self.dropout_ratio + return x * self.mask + else: + return x * (1.0 - self.dropout_ratio) + + def backward(self, dout): + return dout * self.mask + + +class BatchNormalization: + """ + http://arxiv.org/abs/1502.03167 + """ + def __init__(self, gamma, beta, momentum=0.9, running_mean=None, running_var=None): + self.gamma = gamma + self.beta = beta + self.momentum = momentum + self.input_shape = None # Conv层的情况下为4维，全连接层的情况下为2维 + + # 测试时使用的平均值和方差 + self.running_mean = running_mean + self.running_var = running_var + + # backward时使用的中间数据 + self.batch_size = None + self.xc = None + self.std = None + self.dgamma = None + self.dbeta = None + + def forward(self, x, train_flg=True): + self.input_shape = x.shape + if x.ndim != 2: + N, C, H, W = x.shape + x = x.reshape(N, -1) + + out = self.__forward(x, train_flg) + + return out.reshape(*self.input_shape) + + def __forward(self, x, train_flg): + if self.running_mean is None: + N, D = x.shape + self.running_mean = np.zeros(D) + self.running_var = np.zeros(D) + + if train_flg: + mu = x.mean(axis=0) + xc = x - mu + var = np.mean(xc**2, axis=0) + std = np.sqrt(var + 10e-7) + xn = xc / std + + self.batch_size = x.shape[0] + self.xc = xc + self.xn = xn + self.std = std + self.running_mean = self.momentum * self.running_mean + (1-self.momentum) * mu + self.running_var = self.momentum * self.running_var + (1-self.momentum) * var + else: + xc = x - self.running_mean + xn = xc / ((np.sqrt(self.running_var + 10e-7))) + + out = self.gamma * xn + self.beta + return out + + def backward(self, dout): + if dout.ndim != 2: + N, C, H, W = dout.shape + dout = dout.reshape(N, -1) + + dx = self.__backward(dout) + + dx = dx.reshape(*self.input_shape) + return dx + + def __backward(self, dout): + dbeta = dout.sum(axis=0) + dgamma = np.sum(self.xn * dout, axis=0) + dxn = self.gamma * dout + dxc = dxn / self.std + dstd = -np.sum((dxn * self.xc) / (self.std * self.std), axis=0) + dvar = 0.5 * dstd / self.std + dxc += (2.0 / self.batch_size) * self.xc * dvar + dmu = np.sum(dxc, axis=0) + dx = dxc - dmu / self.batch_size + + self.dgamma = dgamma + self.dbeta = dbeta + + return dx + + +class Convolution: + def __init__(self, W, b, stride=1, pad=0): + self.W = W + self.b = b + self.stride = stride + self.pad = pad + + # 中间数据（backward时使用） + self.x = None + self.col = None + self.col_W = None + + # 权重和偏置参数的梯度 + self.dW = None + self.db = None + + def forward(self, x): + FN, C, FH, FW = self.W.shape + N, C, H, W = x.shape + out_h = 1 + int((H + 2*self.pad - FH) / self.stride) + out_w = 1 + int((W + 2*self.pad - FW) / self.stride) + + col = im2col(x, FH, FW, self.stride, self.pad) + col_W = self.W.reshape(FN, -1).T + + out = np.dot(col, col_W) + self.b + out = out.reshape(N, out_h, out_w, -1).transpose(0, 3, 1, 2) + + self.x = x + self.col = col + self.col_W = col_W + + return out + + def backward(self, dout): + FN, C, FH, FW = self.W.shape + dout = dout.transpose(0,2,3,1).reshape(-1, FN) + + self.db = np.sum(dout, axis=0) + self.dW = np.dot(self.col.T, dout) + self.dW = self.dW.transpose(1, 0).reshape(FN, C, FH, FW) + + dcol = np.dot(dout, self.col_W.T) + dx = col2im(dcol, self.x.shape, FH, FW, self.stride, self.pad) + + return dx + + +class Pooling: + def __init__(self, pool_h, pool_w, stride=1, pad=0): + self.pool_h = pool_h + self.pool_w = pool_w + self.stride = stride + self.pad = pad + + self.x = None + self.arg_max = None + + def forward(self, x): + N, C, H, W = x.shape + out_h = int(1 + (H - self.pool_h) / self.stride) + out_w = int(1 + (W - self.pool_w) / self.stride) + + col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad) + col = col.reshape(-1, self.pool_h*self.pool_w) + + arg_max = np.argmax(col, axis=1) + out = np.max(col, axis=1) + out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2) + + self.x = x + self.arg_max = arg_max + + return out + + def backward(self, dout): + dout = dout.transpose(0, 2, 3, 1) + + pool_size = self.pool_h * self.pool_w + dmax = np.zeros((dout.size, pool_size)) + dmax[np.arange(self.arg_max.size), self.arg_max.flatten()] = dout.flatten() + dmax = dmax.reshape(dout.shape + (pool_size,)) + + dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1) + dx = col2im(dcol, self.x.shape, self.pool_h, self.pool_w, self.stride, self.pad) + + return dx