def fully_connected(X, n_in, n_out, activation=None, name=None):
with scope('fully_connected', X.graph): # Not working yet
W = Parameter(np.random.randn(n_in, n_out), 'Weights')
b = Parameter(np.random.randn(n_out), 'Bias')
temp = Matmul(X, W) + b
if activation == 'softmax':
return Softmax(temp)
elif activation == 'sigmoid':
return Sigmoid(temp)
elif activation == 'linear':
return temp
else:
return Leaky_relu(temp)
def cross_entropy(y_estimated, y):
with scope('Loss : Cross_entropy', y_estimated.graph):
return -Reduce_sum(y * Log(y_estimated))
def l1(y_estimated, y):
with scope('Loss : L1', y_estimated.graph):
return Reduce_sum(Absolute(y_estimated - y))
def l2(y_estimated, y):
with scope('Loss : L2', y_estimated.graph):
return Reduce_sum((y_estimated - y)**2)