def forward(self, X, train=False):
gamma1, gamma2 = self.model['gamma1'], self.model['gamma2']
beta1, beta2 = self.model['beta1'], self.model['beta2']
u1, u2 = None, None
bn1_cache, bn2_cache = None, None
# First layer
h1, h1_cache = l.fc_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.bn_forward(h1, gamma1, beta1, bn1_cache, train=train)
h1, nl_cache1 = self.forward_nonlin(h1)
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)
# Second layer
h2, h2_cache = l.fc_forward(h1, self.model['W2'], self.model['b2'])
bn2_cache = (self.bn_caches['bn2_mean'], self.bn_caches['bn2_var'])
h2, bn2_cache, run_mean, run_var = l.bn_forward(h2, gamma2, beta2, bn2_cache, train=train)
h2, nl_cache2 = self.forward_nonlin(h2)
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)
# Third layer
score, score_cache = l.fc_forward(h2, self.model['W3'], self.model['b3'])
cache = (X, h1_cache, h2_cache, score_cache, nl_cache1, nl_cache2, u1, u2, bn1_cache, bn2_cache)
return score, cache
def forward(self, X, train=False):
gamma1, gamma2 = self.model['gamma1'], self.model['gamma2']
beta1, beta2 = self.model['beta1'], self.model['beta2']
u1, u2 = None, None
bn1_cache, bn2_cache = None, None
# First layer
h1, h1_cache = l.fc_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.bn_forward(h1, gamma1, beta1, bn1_cache, train=train)
h1, nl_cache1 = self.forward_nonlin(h1)
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)
# Second layer
h2, h2_cache = l.fc_forward(h1, self.model['W2'], self.model['b2'])
bn2_cache = (self.bn_caches['bn2_mean'], self.bn_caches['bn2_var'])
h2, bn2_cache, run_mean, run_var = l.bn_forward(h2, gamma2, beta2, bn2_cache, train=train)
h2, nl_cache2 = self.forward_nonlin(h2)
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)
# Third layer
score, score_cache = l.fc_forward(h2, self.model['W3'], self.model['b3'])
cache = (X, h1_cache, h2_cache, score_cache, nl_cache1, nl_cache2, u1, u2, bn1_cache, bn2_cache)
return score, cache
def forward(self, X, iter, train=False):
if self.multilevel:
if iter != 0 and iter % self.multi_step == 0 and iter < self.multi_step * self.multi_times + 1:
self.doubleLayers()
cache = dict(X=X)
h = X
prev = 0
#h, cache['c_cache'] = l.conv_forward(h, self.model['Wc'], self.model['bc'])
h, cache['h_caches'], cache['nl_caches'] = \
l.fcrelu_forward(h, self.model['Ws'], self.model['bs'], hypo=self.hypo)
if train and self.doDropout:
h, cache['u1'] = l.dropout_forward(h, self.p_dropout)
for i in range(1, self.num_layers + 1):
temp = h
#print(np.max(h))
if self.leapfrog:
h, cache['h_cache'+str(i)], cache['nl_cache'+str(i)] = \
l.leap_forward(h, prev, self.model['W'+str(i)], self.model['b'+str(i)], self.hypo, i == 1)
else:
h, cache['h_cache'+str(i)], cache['nl_cache'+str(i)] = \
l.fcrelu_forward(h, self.model['W'+str(i)], self.model['b'+str(i)], hypo=self.hypo)
prev = temp
cache['finalh'] = h
score, cache['score_cache'] = l.fc_forward(h, self.model['Wf'],
self.model['bf'])
return score, cache
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)
def forward(self, X, train=False):
if self.nlayer == 2:
gamma1 = self.model['gamma1']
beta1 = self.model['beta1']
u1, bn1_cache = None, None
# First layer
h1, h1_cache = l.fc_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.bn_forward(h1, gamma1, beta1, bn1_cache, train=train)
h1, nl_cache1 = self.forward_nonlin(h1)
if train:
h1, u1 = l.dropout_forward(h1, self.p_dropout[0])
# Last layer
score, score_cache = l.fc_forward(h1, self.model['W4'], self.model['b4'])
cache = (X, h1_cache, score_cache, nl_cache1, u1, bn1_cache)
if self.nlayer == 3:
gamma1, gamma2 = self.model['gamma1'], self.model['gamma2']
beta1, beta2 = self.model['beta1'], self.model['beta2']
u1, u2 = None, None
bn1_cache, bn2_cache = None, None
# First layer
h1, h1_cache = l.fc_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.bn_forward(h1, gamma1, beta1, bn1_cache, train=train)
h1, nl_cache1 = self.forward_nonlin(h1)
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[0])
# Second layer
h2, h2_cache = l.fc_forward(h1, self.model['W2'], self.model['b2'])
bn2_cache = (self.bn_caches['bn2_mean'], self.bn_caches['bn2_var'])
h2, bn2_cache, run_mean, run_var = l.bn_forward(h2, gamma2, beta2, bn2_cache, train=train)
h2, nl_cache2 = self.forward_nonlin(h2)
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[1])
# Third layer
score, score_cache = l.fc_forward(h2, self.model['W4'], self.model['b4'])
cache = (X, h1_cache, h2_cache, score_cache, nl_cache1, nl_cache2, u1, u2, bn1_cache, bn2_cache)
if self.nlayer == 4:
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, bn2_cache, bn3_cache = None, None, None
# First layer
h1, h1_cache = l.fc_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.bn_forward(h1, gamma1, beta1, bn1_cache, train=train)
h1, nl_cache1 = self.forward_nonlin(h1)
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[0])
# Second layer
h2, h2_cache = l.fc_forward(h1, self.model['W2'], self.model['b2'])
bn2_cache = (self.bn_caches['bn2_mean'], self.bn_caches['bn2_var'])
h2, bn2_cache, run_mean, run_var = l.bn_forward(h2, gamma2, beta2, bn2_cache, train=train)
h2, nl_cache2 = self.forward_nonlin(h2)
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[1])
# Third layer
h3, h3_cache = l.fc_forward(h1, self.model['W3'], self.model['b3'])
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 = self.forward_nonlin(h3)
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[2])
# Third layer
score, score_cache = l.fc_forward(h3, self.model['W4'], self.model['b4'])
cache = (X, h1_cache, h2_cache, h3_cache, score_cache, nl_cache1, nl_cache2, nl_cache3, u1, u2, u3, bn1_cache, bn2_cache, bn3_cache)
return score, cache