def forward(self, X, train=True): gamma1, gamma2, gamma3, gamma4, gamma5 = \ self.model['gamma1'],self.model['gamma2'], \ self.model['gamma3'],self.model['gamma4'], \ self.model['gamma5'] beta1, beta2, beta3, beta4, beta5 = \ self.model['beta1'], self.model['beta2'],\ self.model['beta3'], self.model['beta4'],\ self.model['beta5'] u1, u2, u3, u4, u5, u6 = None, None, None,None,None, None bn1_cache, bn2_cache, bn3_cache, bn4_cache, bn5_cache = None, None, None,None,None '''Convolutional layer - 1''' h1, h1_cache = l.conv_forward(X, self.model['W1'], self.model['b1']) h1, nl_cache1 = l.relu_forward(h1) '''Pool -1''' hpool1, hpool1_cache = l.maxpool_forward(h1) '''Conv -2''' h2, h2_cache = l.conv_forward(hpool1, self.model['W2'], self.model['b2']) h2, nl_cache2 = l.relu_forward(h2) '''Pool- 2''' hpool2, hpool2_cache = l.maxpool_forward(h2) '''reshape to Fully-connected layer''' hpool2_ = hpool2.ravel().reshape(X.shape[0],-1) '''FC -1''' h4, h4_cache = l.fc_forward(hpool2_, self.model['W4'], self.model['b4']) bn4_cache = (self.bn_caches['bn4_mean'], self.bn_caches['bn4_var']) h4, bn4_cache, run_mean, run_var = l.bn_forward(h4, gamma4, beta4, bn4_cache, train=train) h4, nl_cache4 = l.relu_forward(h4) self.bn_caches['bn4_mean'], self.bn_caches['bn4_var'] = run_mean,run_var '''FC -2''' h5, h5_cache = l.fc_forward(h4, self.model['W5'], self.model['b5']) bn5_cache = (self.bn_caches['bn5_mean'], self.bn_caches['bn5_var']) h5, bn5_cache, run_mean, run_var = l.bn_forward(h5, gamma5, beta5, bn5_cache,train=train) h5, nl_cache5 = l.relu_forward(h5) self.bn_caches['bn5_mean'], self.bn_caches['bn5_var'] = run_mean, run_var '''Output layer''' score, score_cache = l.fc_forward(h5, self.model['W6'], self.model['b6']) return score, (X, h1_cache, h2_cache, h4_cache, h5_cache, score_cache, hpool1_cache, hpool1, hpool2_cache, hpool2, nl_cache1, nl_cache2, nl_cache4, nl_cache5, bn4_cache,bn5_cache )
def forward(self, X, train=False): self.model['W1'] = np.floor(self.model['W1'] * (2 ** 8)) / (2 ** 8) self.model['W2'] = np.floor(self.model['W2'] * (2**8)) / (2 ** 8) self.model['b3'] = np.floor(self.model['b3'] * (2 ** 8)) / (2 ** 8) self.model['b2'] = np.floor(self.model['b2'] * (2**8)) / (2 ** 8) # Conv-1 h1, h1_cache = l.conv_forward(X, self.model['W1'], self.model['b1']) h1, nl_cache1 = l.relu_forward(h1) print("h1 finish") # Pool-1 hpool, hpool_cache = l.maxpool_forward(h1) print("hpool finish") hpool = np.transpose(hpool, [0, 2, 3, 1]) h2 = hpool.ravel().reshape(X.shape[0], -1) # 先攤平後分成64組 print("h2 finish") # FC-7 h3, h3_cache = l.fc_forward(h2, self.model['W2'], self.model['b2']) h3, nl_cache3 = l.relu_forward(h3) print("h3 finish") # Softmax score, score_cache = l.fc_forward(h3, self.model['W3'], self.model['b3']) print("score finish") return score, (X, h1_cache, h3_cache, score_cache, hpool_cache, hpool, nl_cache1, nl_cache3)
def forward(self, X, train=True): gamma1, gamma2, gamma3 = self.model['gamma1'], self.model[ 'gamma2'], self.model['gamma3'] beta1, beta2, beta3 = self.model['beta1'], self.model[ 'beta2'], self.model['beta3'] u1, u2, u3 = None, None, None bn1_cache, pool_cache, bn3_cache = None, None, None # Conv-1 #print(X.shape) #print(self.model['W1'].shape) #print(self.model['b1'].shape) h1, h1_cache = l.conv_forward(X, self.model['W1'], self.model['b1']) #bn1_cache = (self.bn_caches['bn1_mean'],self.bn_caches['bn1_var']) #h1,bn1_cache,run_mean,run_var = l.conv_bn_forward(h1,gamma1,beta1,bn1_cache,train=train) h1, nl_cache1 = l.relu_forward(h1) #print('h1 shape',h1.shape) #self.bn_caches['bn1_mean'],self.bn_caches['bn1_var']=run_mean,run_var #if train: # h1 ,u1 = l.dropout_forward(h1,self.p_dropout) # Pool-1 hpool, hpool_cache = l.maxpool_forward(h1) #print('hpool shape',hpool.shape) #bn2_cache = (self.bn_caches['bn2_mean'],self.bn_caches['bn2_var']) #hpool,pool_cache,run_mean,run_var = l.conv_bn_forward(hpool,gamma2,beta2,bn2_cache,train= train) h2 = hpool.ravel().reshape(X.shape[0], -1) #print('h2 shape',h2.shape) #self.bn_caches['bn2_mean'],self.bn_caches['bn2_var'] = run_mean,run_var #if train: # h2,u2 = l.dropout_forward(h2,self.p_dropout) # FC-7 h3, h3_cache = l.fc_forward(h2, self.model['W2'], self.model['b2']) #bn3_cache = (self.bn_caches['bn3_mean'],self.bn_caches['bn3_var']) #h3, bn3_cache, run_mean, run_var = l.bn_forward(h3, gamma3, beta3, bn3_cache, train=train) h3, nl_cache3 = l.relu_forward(h3) #print('h3 shape',h3.shape) #self.bn_caches['bn3_mean'],self.bn_caches['bn3_var']= run_mean,run_var #if train: # h3,u3 = l.dropout_forward(h3,self.p_dropout) # Softmax # forth layer score, score_cache = l.fc_forward(h3, self.model['W3'], self.model['b3']) #print('score shape',score.shape) #print('score',score) #return score, (X, h1_cache,h3_cache, score_cache, hpool_cache, hpool, nl_cache1, nl_cache3,u1,u2,u3,bn1_cache,pool_cache,bn3_cache) return score, (X, h1_cache, h3_cache, score_cache, hpool_cache, hpool, nl_cache1, nl_cache3)
def forward(self, X, train=False): # Conv-1 h1, h1_cache = l.conv_forward(X, self.model['W1'], self.model['b1']) h1, nl_cache1 = l.relu_forward(h1) # print(h1.shape) # Pool-1 hpool, hpool_cache = l.maxpool_forward(h1) # print(hpool.shape) # print(hpool[0]) # np.savetxt('hpool.txt', hpool[0]) # input() hpool = np.transpose(hpool, [0, 2, 3, 1]) h2 = hpool.ravel().reshape(X.shape[0], -1) # 先攤平後分成64組 # print(h2.shape) # print(h2[0]) # np.savetxt('h2.txt', h2[0]) # input() # print(hpool.shape) # print(hpool[0,0,:,:]) # print(h1.shape) # print(hpool.shape) # print(h2.shape) # print(len(self.model['W1'])) # print(len(h1), len(h1_cache)) # print(len(h1), len(nl_cache1)) # print(len(hpool), len(hpool_cache)) # "X.shape[0] = 64" # FC-7 h3, h3_cache = l.fc_forward(h2, self.model['W2'], self.model['b2']) h3, nl_cache3 = l.relu_forward(h3) # print(h3.shape) # print(h3[0]) # input() # Softmax score, score_cache = l.fc_forward(h3, self.model['W3'], self.model['b3']) # print('h1' + str(h1.shape)) # print('W1' + str(self.model['W1'].shape)) # print('b1' + str(self.model['b1'].shape)) # print('hpool' + str(hpool.shape)) # print('h2' + str(h2.shape)) # print('W2' + str(self.model['W2'].shape)) # print('b2' + str(self.model['b2'].shape)) # print('h3' + str(h3.shape)) # print('W2' + str(self.model['W3'].shape)) # print('b3' + str(self.model['b3'].shape)) # print('score' + str(score.shape)) # input() return score, (X, h1_cache, h3_cache, score_cache, hpool_cache, hpool, nl_cache1, nl_cache3)
def forward(self, X, train=False): # Conv-1 h1, h1_cache = l.conv_forward(X, self.model['W1'], self.model['b1']) h1, nl_cache1 = l.relu_forward(h1) # Pool-1 hpool, hpool_cache = l.maxpool_forward(h1) h2 = hpool.ravel().reshape(X.shape[0], -1) # FC-7 h3, h3_cache = l.fc_forward(h2, self.model['W2'], self.model['b2']) h3, nl_cache3 = l.relu_forward(h3) # Softmax score, score_cache = l.fc_forward(h3, self.model['W3'], self.model['b3']) return score, (X, h1_cache, h3_cache, score_cache, hpool_cache, hpool, nl_cache1, nl_cache3)
def forward(self, X, train=False): u3 = None # Conv-1 h1, h1_cache = l.conv_forward(X, self.model['W1'], self.model['b1']) h1, nl_cache1 = l.relu_forward(h1) # Pool-1 hpool, hpool_cache = l.maxpool_forward(h1) h2 = hpool.ravel().reshape(X.shape[0], -1) # FC-7 h3, h3_cache = l.fc_forward(h2, self.model['W2'], self.model['b2']) h3, nl_cache3 = l.relu_forward(h3) #add dropout in fully connected layer if train: h3, u3 = l.dropout_forward(h3, self.p_dropout[0]) # Softmax score, score_cache = l.fc_forward(h3, self.model['W3'], self.model['b3']) return score, (X, h1_cache, h3_cache, score_cache, hpool_cache, hpool, nl_cache1, nl_cache3, u3)