def model(self, latent_dim: int = 1):
'''Keras style layers + optimizers'''
self.layers = []
self.layers.append(Dense(32, activation='tanh'))
self.layers.append(Dropout(keep_prob=0.9))
self.layers.append(Dense(16, activation='tanh'))
self.layers.append(Dropout(keep_prob=0.9))
self.layers.append(Dense(8, activation='tanh'))
self.layers.append(Dropout(keep_prob=0.9))
self.layers.append(Dense(latent_dim))
self.layers.append(Scale(fixed=True, init_vals=10)) # why need scaling
self.layers.append(CovMat(kernel='rbf', alpha_fixed=False))
# self.layers.append(CovMat(kernel='rbf', alpha_fixed=False, alpha=self.noise)) # noise free
# optimizer
self.opt = Adam(1e-3)
def Deep_Kernel_Gaussian_Process(x_train, y_train, x_test, y_test):
layers = []
layers.append(Dense(64, activation='tanh'))
layers.append(Dense(64, activation='tanh'))
layers.append(Dense(20))
layers.append(CovMat(alpha=0.3, var=1.0, kernel='rbf'))
opt = SciPyMin('l-bfgs-b')
opt = Adam(1e-3)
batch_size = 500
gp = NNRegressor(layers,
opt=opt,
batch_size=batch_size,
maxiter=500,
gp=True,
verbose=True)
gp.fit(x_train, y_train)
#Can extract mapping z(x) and hyperparams for use in other learning algorithm
alph = gp.layers[-1].s_alpha
var = gp.layers[-1].var
A_full = gp.fast_forward(x_train)
kernel = ConstantKernel(var) * RBF(np.ones(1)) + WhiteKernel(alph)
A_test = gp.fast_forward(x_test)
gp1 = GaussianProcessRegressor(kernel, optimizer=None)
if A_full.shape[0] > 1000:
data_index = np.arange(0, A_full.shape[0])
np.random.shuffle(data_index)
ind = data_index[0:1000]
gp1.fit(A_full[ind, :], y_train[ind, :])
else:
#gp1.fit(A_full[500,:],y_train[500,:])
gp1.fit(A_full, y_train)
mu, stdt = gp1.predict(A_test, return_std=True)
labels = np.rint(mu)
return labels
np.random.seed(0)
x_train=np.random.random(size=(70,1))-0.5
y_train=f(x_train)+np.random.normal(0.0,0.01,size=x_train.shape)
layers=[]
#layers.append(Dense(64,activation='tanh'))
#layers.append(Dropout(0.99))
layers.append(Dense(6,activation='tanh'))
layers.append(Dropout(0.99))
layers.append(Dense(1))
layers.append(Scale(fixed=True,init_vals=64.0))
layers.append(CovMat(kernel='rbf',alpha_fixed=False))
opt=Adam(1e-3)
#opt=SciPyMin('l-bfgs-b')
gp=NNRegressor(layers,opt=opt,batch_size=x_train.shape[0],maxiter=10000,gp=True,verbose=True)
gp.fit(x_train,y_train)
#print(gp.grad_check(x_train[0:10],y_train[0:10]))
x_test=np.linspace(-0.7,0.7,1000).reshape(-1,1)
y_pred,std=gp.predict(x_test)
plt.plot(x_test,gp.layers[-2].out)
plt.xlabel('X')
plt.ylabel('Z')
x_train = train_data[:, which_qs]
y_train = train_data[:, [-1]]
x_test = test_data[:, which_qs]
y_test = test_data[:, [-1]]
layers = []
n_out = 2
layers.append(Dense(100, activation='lrelu'))
#layers.append(Dropout(0.8))
layers.append(Dense(100, activation='lrelu'))
#layers.append(Dropout(0.8))
#layers.append(Dense(50,activation='lrelu'))
layers.append(Dense(n_out))
layers.append(CovMat(kernel='rbf', alpha_fixed=False))
opt = Adam(1e-4)
gp = NNRegressor(layers,
opt=opt,
batch_size=50,
maxiter=4000,
gp=True,
verbose=False)
gp.fit(x_train, y_train)
if len(which_qs) > 2 or n_out > 2 or True:
ytr_pred, std = gp.predict(x_train)
ytestpred, std = gp.predict(x_test)
ydumb = np.mean(y_train)
mse_train = np.mean((ytr_pred - y_train)**2)