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):
# 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)
