def gradients(self,params=None):
# args contains the training data
x,y=self.args;
self.update_params(params);
sigmoid_activation = pyvision.softmax(self.W,self.b,x);
e = [ self.compute_gradients(a,c,b) for a, c,b in izip(sigmoid_activation,y,x)]
mean1=np.mean(e,axis=0);
mean1=mean1.T.flatten();
return mean1;
def gradients(self,params=None):
# args contains the training data
x,y=self.args;
self.update_params(params);
sigmoid_activation = pyvision.softmax(self.W,self.b,x);
e = [ self.compute_gradients(a,c,b) for a, c,b in izip(sigmoid_activation,y,x)]
mean1=np.mean(e,axis=0);
mean1=mean1.T.flatten();
return mean1;
def negative_log_likelihood(self,params):
# args contains the training data
x,y=self.args;
self.update_params(params);
sigmoid_activation = pyvision.softmax(self.W,self.b,x);
index=[range(0,np.shape(sigmoid_activation)[0]),y];
p=sigmoid_activation[index]
l=-np.mean(np.log(p));
return l;
def negative_log_likelihood(self,params=None):
# args contains the training data
x,y=self.args;
self.update_params(params);
sigmoid_activation = pyvision.softmax(self.W,self.b,x);
index=[range(0,np.shape(sigmoid_activation)[0]),y];
p=sigmoid_activation[index]
l=-np.mean(np.log(p));
if self.Regularization==2:
l=l+0.5*self.eta*np.sum(self.W**2);
if self.Regularization==1:
l=l+self.eta*np.sum(np.abs(self.W));
return l;
def negative_log_likelihood(self,params=None):
# args contains the training data
x,y=self.args;
self.update_params(params);
sigmoid_activation = pyvision.softmax(self.W,self.b,x);
index=[range(0,np.shape(sigmoid_activation)[0]),y];
p=sigmoid_activation[index]
l=-np.mean(np.log(p));
if self.Regularization==2:
l=l+0.5*self.eta*np.sum(self.W**2);
if self.Regularization==1:
l=l+self.eta*np.sum(np.abs(self.W));
return l;
def predict(self,x):
y=pyvision.softmax(self.W,self.b,x);
self.output=y;
return y;
def predict(self,x):
y=pyvision.softmax(self.W,self.b,x);
self.output=y;
return y;
