def main(argv):
args = parser.parse_args(argv[1:])
width = 45
train_x, train_y = mnist.train()
test_x, test_y = mnist.test()
if args.central:
print("Use centralize now")
width = 32
train_x = mnist.train_32()
test_x = mnist.test_32()
train_x = train_x / 256
test_x = test_x / 256
train_y = train_y.astype(np.int32)
test_y = test_y.astype(np.int32)
mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
params={"width": width})
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": train_x},
y=train_y,
batch_size=100,
num_epochs=None,
shuffle=True)
mnist_classifier.train(input_fn=train_input_fn, steps=10000)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": test_x},
y=test_y,
num_epochs=1,
shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
def go_svm(proc, pca_enabled, central):
print("SVM")
print("0-1", proc) # only 0 and 1
print("Central", central) # 32x32
print("PCA", pca_enabled) # PCA to 50 dims
train_x, train_y = mnist.train()
test_x, test_y = mnist.test()
if central:
train_x = mnist.train_32()
test_x = mnist.test_32()
if proc:
with Timer("process"):
train_x = process(train_x)
test_x = process(test_x)
train_x = train_x.reshape((train_x.shape[0], -1))
test_x = test_x.reshape((test_x.shape[0], -1))
if pca_enabled:
with Timer("PCA"):
pca = PCA(n_components=50, whiten=True)
train_x = pca.fit_transform(train_x)
test_x = pca.transform(test_x)
with Timer("train"):
clf = svm.SVC(cache_size=7000)
clf.fit(train_x, train_y)
print("Accuracy:", clf.score(test_x, test_y))
def go(proc, central):
print("kNN")
print("0-1", proc)
print("central", central)
train_x, train_y = mnist.train()
test_x, test_y = mnist.test()
if central:
train_x = mnist.train_32()
test_x = mnist.test_32()
if proc:
with Timer("process"):
train_x = process(train_x)
test_x = process(test_x)
train_x = train_x.reshape((train_x.shape[0], -1))
test_x = test_x.reshape((test_x.shape[0], -1))
with Timer("kNN fit"):
neigh = KNeighborsClassifier(n_neighbors=5, n_jobs=-1)
neigh.fit(train_x, train_y)
with Timer("kNN test"):
print("Accuracy:", neigh.score(test_x, test_y))
def go(pca_enabled=False, centralize=False):
print("PCA:", pca_enabled)
print("Centralize:", centralize)
train_x, train_y = mnist.train()
test_x, test_y = mnist.test()
if centralize:
train_x = mnist.train_32()
test_x = mnist.test_32()
train_x = train_x.reshape((train_x.shape[0], -1))
test_x = test_x.reshape((test_x.shape[0], -1))
if pca_enabled:
with Timer("PCA"):
pca = PCA(n_components=50, whiten=True)
train_x = pca.fit_transform(train_x)
test_x = pca.transform(test_x)
with Timer("train"):
max_iter = 1000 if centralize or pca_enabled else 200
clf = MLPClassifier(max_iter=max_iter, verbose=True)
clf.fit(train_x, train_y)
print("Accuracy:", clf.score(test_x, test_y))
def run():
m = mnist.My_VAE_V2(10)
mnist.train(m, 10)
mnist.test(m) # will output dd.png
return m
teacher_out = teacher.forward_pass(x)
student.train(x, teacher_out)
if __name__ == "__main__":
teacher_layers = [
Layer(784, 16, LeakyReLU()),
Layer(16, 16, LeakyReLU()),
Layer(16, 10, LeakyReLU()),
]
teacher_net = NeuralNetwork(teacher_layers, CrossEntropyLoss(), 0.001)
train_data = load_data("mnistdata/mnist_train.csv",
delimiter=",",
dtype=int)
train(teacher_net, train_data)
test_data = load_data("mnistdata/mnist_test.csv", delimiter=",", dtype=int)
accuracy = test(teacher_net, test_data)
print(f"Accuracy of the teacher net is {100*accuracy:.2f}")
student_layers = [
Layer(784, 10, Sigmoid()),
]
student_net = NeuralNetwork(student_layers, MSELoss(), 0.005)
train_student(student_net, teacher_net, train_data)
student_accuracy = test(student_net, test_data)
print(f"Accuracy of the student net is {100*accuracy:.2f}")