def get_data(): X, Y = get_clouds() # Split the data into train and test sets # This lets us simulate how our model will perform in the future Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) return Xtrain, Xtest, Ytrain, Ytest
def random_search():
X, Y = get_clouds()
X, Y = shuffle(X, Y)
Ntrain = int(0.7 * len(X))
Xtrain, Xtest = X[:Ntrain], X[Ntrain:]
Ytrain, Ytest = Y[:Ntrain], Y[Ntrain:]
M = 20
nHidden = 2
log_lr = -4
log_l2 = -2
max_tries = 30
best_nHidden = None
best_validation_rate = 0
best_M = None
best_lr = None
best_l2 = None
for _ in range(max_tries):
model = ANN([M] * nHidden)
model.fit(Xtrain,
Ytrain,
learning_rate=10**log_lr,
reg=10**log_l2,
mu=0.99,
epochs=3000,
show_fig=False)
validation_accuracy = model.score(Xtest, Ytest)
train_accuracy = model.score(Xtrain, Ytrain)
print(
f'validation_accuracy: {validation_accuracy}, train_accuracy: {train_accuracy}, setting M: {M}, nHidden: {nHidden}, lr: {log_lr}, l2: {log_l2}'
)
if validation_accuracy > best_validation_rate:
best_validation_rate = validation_accuracy
best_M = M
best_nHidden = nHidden
best_l2 = log_l2
best_lr = log_lr
# select new hyperparams
nHidden = best_nHidden + np.random.randint(-1, 2)
nHidden = max(1, nHidden)
M = best_M + np.random.randint(-1, 2) * 10
M = max(10, M)
log_lr = best_lr + np.random.randint(-1, 2)
log_l2 = best_l2 + np.random.randint(-1, 2)
print(f'Best validation_accuracy: {best_validation_rate}')
print('Best Setting:')
print(f'Best hidden layer number: {best_nHidden}')
print(f"Best hidden_layer_size, {best_M}")
print(f'Best learning rate: {best_lr}')
print(f'Best regularizations : {best_l2}')
def main():
X = get_clouds()
X = shuffle(X)
# show data
plt.scatter(X[:, 0], X[:, 1])
plt.show()
model = soft_kmeans(4)
model.fit(X, beta=1, eps=1e-3)
def get_data(): ### medical data # load the data # data = load_breast_cancer() # X, Y = data.data, data.target # X, Y = get_xor() # X, Y = get_donut() # X, Y = get_spiral() X, Y = get_clouds() # split the data into train and test sets # this lets us simulate how our model will perform in the future Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) return Xtrain, Xtest, Ytrain, Ytest
def get_data(): ### medical data # load the data # data = load_breast_cancer() # X, Y = data.data, data.target # X, Y = get_xor() # X, Y = get_donut() # X, Y = get_spiral() X, Y = get_clouds() # split the data into train and test sets # this lets us simulate how our model will perform in the future Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) return Xtrain, Xtest, Ytrain, Ytest
def clouds():
X, Y = get_clouds()
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.33)
return X_train, X_test, Y_train, Y_test, 1e-3, 200
def clouds(): X, Y = get_clouds() Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-5, 400
def clouds(): X, Y = get_clouds() Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) return Xtrain, Xtest, Ytrain, Ytest, 1e-3, 200
def clouds(): X, Y = get_clouds() Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-5, 500
