Add 'layers.py'

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