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, 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):
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