def run_softmax_gradient_check(X,Y, numargs):
f = prepare_for_gradient_check(ms.loss_softmax, X, Y)
df = prepare_for_gradient_check(ms.gradient_softmax_batch,X,Y)
#w = np.asarray([random.random() for j in xrange(0,numargs)])
w = np.zeros(X.shape[1])
#print f(w)
print gradient_check(f,df,numargs, x=[w])
###TESTING########################################################################
def sumSquare(x):
'''x1^2 + x2^2'''
return (x[0])*x[0] + (x[1])*x[1]
def dSumSquare(x):
'''derivative of sum square'''
return (2*x[0], 2*x[1])
if __name__=='__main__':
#gradient_check(1,1,5)
#gradient_check(1,2,3, False)
#value_difference([2,3], .01, sumSquare, dSumSquare)
full_trainarray = np.load('data/numpy/trainarray.npy')
full_trainlabel = np.load('data/numpy/trainlabel.npy')
full_testarray = np.load('data/numpy/testarray.npy' )
full_testlabel = np.load('data/numpy/testlabel.npy' )
X_test, Y_test = fn.preprocess_data(full_testarray, full_testlabel, True)
run_logistic_gradient_check(X_test, Y_test, len(np.zeros(X_test.shape[1])))
self.reset()
w = np.random.rand(X_train.shape[1])
pass
#plot convergence results
def plot_convergence(self):
pass
#reset before initiating new method
def reset(self):
self.log_lkhd=np.array([0],dtype=np.float_)
self.stepsz=np.array([0],dtype=np.float_)
self.params=np.array([0],dtype=np.float_)
self.w=self.X_train[:, np.random.randint(1, self.X_train.shape[2], 1)]
self.gradient=[]
if __name__=='__main__':
# Load dataset from MNIST
full_trainarray = np.load(os.path.join('data','numpy','trainarray.npy'))
full_trainlabel = np.load(os.path.join('data','numpy','trainlabel.npy'))
full_testarray = np.load(os.path.join('data','numpy','testarray.npy' ))
full_testlabel = np.load(os.path.join('data','numpy','testlabel.npy' ))
X_train, Y_train = fn.preprocess_data(full_trainarray, full_trainlabel)
X_test, Y_test = fn.preprocess_data(full_testarray, full_testlabel)
gdo=GradientDescent(X_train, Y_train, X_test, Y_test)
gdo.batch_gd()
gdo.plot_convergence()
pass
#plot convergence results
def plot_convergence(self):
pass
#reset before initiating new method
def reset(self):
self.log_lkhd = np.array([0], dtype=np.float_)
self.stepsz = np.array([0], dtype=np.float_)
self.params = np.array([0], dtype=np.float_)
self.w = self.X_train[:,
np.random.randint(1, self.X_train.shape[2], 1)]
self.gradient = []
if __name__ == '__main__':
# Load dataset from MNIST
full_trainarray = np.load(os.path.join('data', 'numpy', 'trainarray.npy'))
full_trainlabel = np.load(os.path.join('data', 'numpy', 'trainlabel.npy'))
full_testarray = np.load(os.path.join('data', 'numpy', 'testarray.npy'))
full_testlabel = np.load(os.path.join('data', 'numpy', 'testlabel.npy'))
X_train, Y_train = fn.preprocess_data(full_trainarray, full_trainlabel)
X_test, Y_test = fn.preprocess_data(full_testarray, full_testlabel)
gdo = GradientDescent(X_train, Y_train, X_test, Y_test)
gdo.batch_gd()
gdo.plot_convergence()