def __init__(self,
out_shape: int,
input_shape: tuple = None,
eps=1e-5,
axis=0,
momentum=.9,
beta_opt: Optimizer = None,
gamma_opt: Optimizer = None,
alpha: float = 1e-7) -> None:
super().__init__(out_shape, input_shape, False)
self.axis: int = axis
self.gamma: np.ndarray = np.ones(self.out_shape, dtype=np.float64)
self.beta: np.ndarray = np.zeros(self.out_shape, dtype=np.float64)
self.running_mu: np.ndarray = np.zeros(self.out_shape,
dtype=np.float64)
self.running_var: np.ndarray = np.zeros(self.out_shape,
dtype=np.float64)
if axis == 1:
self.gamma = self.gamma.reshape((-1, 1))
self.beta = self.beta.reshape((-1, 1))
self.running_mu = self.running_mu.reshape((-1, 1))
self.running_var = self.running_var.reshape((-1, 1))
self.mu = 0
self.var = 0
self.std = 0
self.z = 0
self.eps = eps
self.alpha = alpha
self.momentum = momentum
self.beta_opt: Optimizer() = beta_opt if beta_opt else Adam()
self.gamma_opt: Optimizer() = gamma_opt if gamma_opt else Adam()
def __init__(self):
self.params = dict()
self.grads = dict()
self.params['W1'] = args.weight_init_std * np.random.randn(784, 256)
self.params['B1'] = np.zeros(256)
self.params['W2'] = args.weight_init_std * np.random.randn(256, 256)
self.params['B2'] = np.zeros(256)
self.params['W3'] = args.weight_init_std * np.random.randn(256, 10)
self.params['B3'] = np.zeros(10)
self.optimizer = Adam()
def main() -> None:
(x_train, t_train), (x_test, t_test) = sequence.load_data('addition.txt')
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
char_to_id, id_to_char = sequence.get_vocab()
vocab_size = len(char_to_id)
wordvec_size = 16
hidden_size = 128
batch_size = 128
max_epoch = 25
max_grad = 5.0
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(1, max_epoch+1):
trainer.fit(x_train, t_train, max_epoch=1, batch_size=batch_size, max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model, question, correct, id_to_char, verbose)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print(f'val acc {acc*100}%')
print('DONE')
def primal_step(self, x, y, learning_rate, input_dim):
mlp, cost, probs = self.create_model(x, y, input_dim)
cg = ComputationGraph([cost])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
updates = Adam(cost, weights)
return mlp, updates, cost, probs
def primal_step(self, x, y, learning_rate, alpha, beta, input_dim, p):
mlp, cost = self.create_model(x, y, input_dim, p)
cg = ComputationGraph([cost])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
updates = Adam(cost, weights, y, alpha, beta)
return mlp, updates, -1 * cost
def primal_step(self, x, y, learning_rate, input_dim, p):
self.model = self.model(x, y, input_dim, p)
score, probs = self.model.create_model()
criterion = self.alpha * p - self.beta * np.float32(1 - p)
r = theano.shared(np.float32(0.0), name='tp+fp')
q = theano.shared(np.float32(0.0), name='tn+fn')
pos_criterion = T.lt(probs, 0.5) * -criterion * score
neg_criterion = T.gt(probs, 0.5) * criterion * score
cost_weighed = T.mean(pos_criterion * T.gt(criterion, 0) +
neg_criterion * T.lt(criterion, 0))
cg = ComputationGraph([cost_weighed])
# Reward version
r_temp = (self.t * r + T.mean(score * T.gt(probs, 0.5))) / (self.t + 1)
q_temp = (self.t * q + T.mean(score * T.lt(probs, 0.5))) / (self.t + 1)
# True Count version
# r_temp = (self.t*r + T.mean(1.0 * T.gt(probs, 0.5)))/(self.t + 1)
# q_temp = (self.t*q + T.mean(1.0 * T.lt(probs, 0.5)))/(self.t + 1)
primal_updates = [(r, r_temp), (q, q_temp), (self.t, self.t + 1)]
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
updates = Adam(cost_weighed, weights) + primal_updates
# r = tp + fp
# q = fp + fn
primal_var = [r, q]
return updates, cost_weighed, score, primal_var
def train(data_path, adaptor, classifier, summ):
input_ = Input(FLAGS.train_batch_size, FLAGS.num_points)
waves, labels = input_(data_path)
# Calculate the loss of the model.
if FLAGS.adp:
logits = tf.stop_gradient(adaptor(waves))
# logits = adaptor(waves)
logits = classifier(logits)
else:
logits = classifier(waves, expand_dims=True)
loss = LossClassification(FLAGS.num_classes)(logits, labels)
opt = Adam(FLAGS.learning_rate,
lr_decay=True,
lr_decay_steps=FLAGS.lr_decay_steps,
lr_decay_factor=FLAGS.lr_decay_factor)
graph_regularizers = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_regularization_loss = tf.reduce_sum(graph_regularizers)
train_op = opt(loss + total_regularization_loss)
summ.register('train', 'train_loss', loss)
train_summ_op = summ('train')
return loss, train_op, train_summ_op
def main():
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
contexts, target = create_context_target(corpus, window_size)
one_hot_target = convert_one_hot(target, vocab_size)
one_hot_contexts = convert_one_hot(contexts, vocab_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(one_hot_contexts, one_hot_target, max_epoch, batch_size)
# trainer.plot()
word_vecs = model.word_vecs
for word_id, word in id_to_word.items():
print(word, word_vecs[word_id])
print('DONE')
def train(data_path, image_upsampler, wm_upsampler, blender, downsampler, extrator, summ):
data_file = os.path.join(data_path, 'train_images.tfr')
wm_file = os.path.join(data_path, 'watermark.mat')
assert os.path.isfile(data_file), "Invalid file name"
assert os.path.isfile(wm_file), "Invalid file name"
input_ = Input(FLAGS.batch_size, [FLAGS.img_height, FLAGS.img_width, FLAGS.num_chans])
images = input_(data_file)
wm = Watermark(wm_file)()
image_upsampled = image_upsampler(images)
wm_upsampled = wm_upsampler(wm)
image_blended = blender(image_upsampled, wm_upsampled)
image_downsampled = downsampler(image_blended, training = True)
wm_extracted = extrator(image_downsampled, training = True)
image_loss = LossRegression()(image_downsampled, images)
wm_loss = LossRegression()(wm_extracted, wm)
opt = Adam(FLAGS.learning_rate, lr_decay = FLAGS.lr_decay, lr_decay_steps = FLAGS.lr_decay_steps,
lr_decay_factor = FLAGS.lr_decay_factor)
train_op = opt(image_loss + wm_loss)
summ.register('train', 'image_loss', image_loss)
summ.register('train', 'wm_loss', wm_loss)
train_summ_op = summ('train')
return image_loss + wm_loss, train_op, train_summ_op
def main() -> None:
window_size = 5
hidden_size = 100
batch_size = 100
max_epoch = 10
corpus, word_to_id, id_to_word = ptb.load_data('train')
vocab_size = len(word_to_id)
contexts, target = create_context_target(corpus, window_size)
model = CBOW(vocab_size, hidden_size, window_size, corpus)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts, target, max_epoch, batch_size)
# trainer.plot()
word_vecs = model.word_vecs
params = {
'word_vecs': word_vecs.astype(np.float16),
'word_to_id': word_to_id,
'id_to_word': id_to_word
}
with open('cbow_params.pkl', 'wb') as f:
pickle.dump(params, f, -1)
def __init__(self,
out_shape: int,
reg: Regularization = L2,
ns: bool = False,
opt: Optimizer = None,
opt_bias_: Optimizer = None,
eps=1e-3,
alpha=1e-5,
input_shape: tuple = None,
lambda_=0,
bias=True,
reg_bias=False,
opt_bias=True,
init_W=stdScale,
seed=-1) -> None:
super().__init__(out_shape, input_shape, ns)
self.seed = seed
# general params
self.eps: float = eps
self.bias: bool = bias
self.reg_bias: bool = reg_bias
self.opt_bias: bool = opt_bias
# layer params
self.init_W = init_W
self.W = None
# TODO remove
# np.random.seed(6)
self.b = init_W((out_shape, ), eps) if bias else None
self.__input_shape = input_shape
if input_shape:
assert len(input_shape) == 1
# TODO remove
if seed >= 0:
np.random.seed(seed)
self.W = init_W((input_shape[0], out_shape), eps)
# hyper params
self.alpha, self.lambda_ = alpha, lambda_
# engine param
self.reg: Regularization = reg
self.opt: Optimizer() = opt if opt else Adam()
self.opt_bias_: Optimizer(
) = opt_bias_ if opt_bias_ or not bias else Adam()
def primal_step(self, x, y, learning_rate, alpha, beta, input_dim, p):
mlp, cost = self.create_model(x, y, input_dim, p)
flag = T.eq(y, 1) * alpha + T.eq(y, 0) * beta
cost_weighed = T.mean(cost * flag.dimshuffle(0, 'x'))
cg = ComputationGraph([cost_weighed])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
updates = Adam(cost_weighed, weights)
return mlp, updates, cost_weighed, cost
def __init__(self, inputDim=1, outputDim=1, optimizer=Adam()):
self.inputDim = inputDim
self.outputDim = outputDim
self.mean = Dense(self.inputDim,
self.outputDim,
activation=Identity(),
optimizer=copy.copy(optimizer))
self.logVar = Dense(self.inputDim,
self.outputDim,
activation=Identity(),
optimizer=copy.copy(optimizer))
def __init__(self, load_data_function, hidden_unit=16, learning_rate=0.01, weight_decay=5e-4):
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data_function()
# 邻接矩阵再正则化
self.adj = preprocess_adj(adj)
# 特征归一化
self.features = preprocess_features(features) # preprocess
self.y_train, self.train_mask = y_train, train_mask
self.y_val, self.val_mask = y_val, val_mask
self.y_test, self.test_mask = y_test, test_mask
# init
# 节点个数
self.n = adj.shape[0]
# 特征个数，1433维
self.f = features.shape[1]
# 类别个数，7维
self.c = y_train.shape[1]
# 隐藏单元个数
self.h = hidden_unit
# init weight
# 第一层权重参数
self.weight_hidden = init_weight((self.f, self.h))
# 第二层权重参数
self.weight_outputs = init_weight((self.h, self.c))
self.adam_weight_hidden = Adam(weights=self.weight_hidden, learning_rate=learning_rate)
self.adam_weight_outputs = Adam(weights=self.weight_outputs, learning_rate=learning_rate)
# 第二层输入，反向传播使用
self.hidden = np.zeros((self.n, self.h))
# 第二层输出，反向传播使用
self.outputs = np.zeros((self.n, self.c))
self.weight_decay = weight_decay
# test
self.grad_loss = None
self.grad_weight_outputs = None
self.grad_hidden = None
self.grad_weight_hidden = None
def primal_step(self, x, y, learning_rate, input_dim, p, mask=None):
if mask is None:
self.model = self.model(x, y, input_dim, p)
else:
self.model = self.model(x, y, input_dim, p, mask=mask)
probs = self.model.create_model()
cost = T.sum((probs - y.dimshuffle(0, 'x'))**2)
cg = ComputationGraph([cost])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
updates = Adam(cost, weights)
return updates, cost
def main():
# optimizer = SGD(lr, weight_decay, mu=mu)
optimizer = Adam(lr, weight_decay)
model = ListModel(net=[
Linear(784, 400),
ReLU(),
Linear(400, 100),
ReLU(),
Linear(100, 10),
Softmax()
],
loss=CrossEntropyLoss())
for epoch in range(num_epochs):
print('epoch number: {}'.format(epoch))
train(model, optimizer)
valid(model)
def get_opt():
opt = parameter.optimization.lower()
if (opt == "adam"):
return Adam()
elif (opt == "adadelta"):
return Adadelta()
elif (opt == "adagrad"):
return AdaGrad()
elif (opt == "rmsprop"):
return RMSProp()
elif (opt == "nesterov"):
return Nesterov()
elif (opt == "momentum"):
return Momentum()
else:
return None
def primal_step(self, x, y, learning_rate, input_dim, p, mask=None):
if mask is None:
self.model = self.model(x, y, input_dim, p)
else:
self.model = self.model(x, y, input_dim, p, mask=mask)
cost = self.model.create_model()
flag = T.eq(y, 1) * (self.gamma[0] * self.alpha[0] +
self.gamma[1] * self.beta[0]) +\
T.eq(y, 0) * (self.gamma[0] * self.alpha[1] +
self.gamma[1] * self.beta[0])
q0 = theano.shared(np.float32(0), name='q0')
q1 = theano.shared(np.float32(0), name='q1')
r0 = theano.shared(np.float32(0), name='r0')
r1 = theano.shared(np.float32(0), name='r1')
q0_temp = q0 * self.t + T.mean(
(T.eq(y, 1) * self.alpha[0] +
T.eq(y, 0) * self.alpha[1]).dimshuffle(0, 'x') * cost)
q1_temp = q1 * self.t + T.mean(
(T.eq(y, 1) * self.beta[0] + T.eq(y, 0) * self.beta[1]).dimshuffle(
0, 'x') * cost)
# Update r
r0_next = (r0 * self.t + T.mean(
T.eq(y, 1).dimshuffle(0, 'x') * cost)) * 1.0 / (self.t + 1)
r1_next = (r1 * self.t + T.mean(
T.eq(y, 0).dimshuffle(0, 'x') * cost)) * 1.0 / (self.t + 1)
# Update q
q0_next = (q0_temp - self.dual_class.dual1_fn(self.alpha)) / (self.t +
1)
q1_next = (q1_temp - self.dual_class.dual2_fn(self.beta)) / (self.t +
1)
primal_updates = [(q0, q0_next), (q1, q1_next), (r0, r0_next),
(r1, r1_next), (self.t, self.t + 1)]
cost_weighed = T.mean(cost * flag.dimshuffle(0, 'x'))
cg = ComputationGraph([cost_weighed])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
updates = Adam(cost_weighed, weights) + primal_updates
primal_var = [[r0, r1], [q0, q1]]
return updates, cost_weighed, cost, primal_var
def t1():
# layer_param = [(8, 3, 1, 1, 'conv'), (8, 'pool'), (12, 3, 1, 1, 'conv'), (12, 3, 1, 1, 'conv'),
# (12, 3, 1, 1, 'conv'), (12, 'pool'), (36, 3, 1, 1, 'conv'), (36, 3, 1, 1, 'conv'),
# (36, 3, 1, 1, 'conv'), (36, 'pool'), (64, 'FC')]
layer_param = [(6, 5, 1, 0, 'conv'), (6, 'pool'), (16, 5, 1, 0, 'conv'),
(16, 'pool'), (120, 'FC'), (84, 'FC')]
regulation = L2Regulation()
activation = ReluActivation()
optimizer = Adam()
cnn = CnnTest(layer_param)
cnn.load_train_data(0.7)
# letnet lr不能太大 10 ** -2 左右时由于步长太长收敛不了
bn_update = UpdateBN()
cnn.train_random_search(10, 64, 0.9, [-3.0, -5.0], optimizer, activation,
[-3, -5], regulation, 10, 1, True)
def __init__(self,
out_shape: int,
act: Activation = ReLU,
norm: NormLayer = None,
reg: Regularization = L2,
opt: Optimizer = Adam(),
opt_bias_: Optimizer = Vanilla(),
eps=1e-3,
alpha=1e-5,
input_shape: tuple = None,
lambda_=0,
bias=True,
reg_bias=False,
opt_bias=True,
init_W=stdScale) -> None:
super().__init__(out_shape, input_shape=input_shape)
# general params
self.eps: float = eps
self.bias: bool = bias
self.reg_bias: bool = reg_bias
self.opt_bias: bool = opt_bias
# layer params
self.init_W = init_W
self.W = None
self.b = init_W((out_shape, ), eps) if bias else None
self.__input_shape = input_shape
if input_shape:
assert len(input_shape) == 1
self.W = init_W((input_shape[0], out_shape), eps)
# hyper params
self.alpha, self.lambda_ = alpha, lambda_
# engine param
self.act: Activation = act
self.reg: Regularization = reg
self.norm: NormLayer = norm
self.opt: Optimizer() = opt
self.opt_bias_: Optimizer() = opt_bias_
if isinstance(self.act, ReLU):
self.numeric_stability = False
def main() -> None:
(x_train, t_train), (x_test, t_test) = sequence.load_data('data.txt')
char_to_id, id_to_char = sequence.get_vocab()
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
vocab_size = len(char_to_id)
wordvec_size = 16
hidden_size = 256
batch_size = 128
max_epoch = 10
max_grad = 5.0
model = AttentionSeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model,
question,
correct,
id_to_char,
verbose,
is_reverse=True)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print(f"val acc {acc*100}%")
model.save_params()
print("DONE")
def get_fns(self, input_dim=123, p_learning_rate=0.01, loss='prec'):
x = T.matrix('X')
y = T.vector('y')
loss_id = loss_functions.loss_dict[loss]
scores = self.get_scores(x, input_dim)
def get_score_fn():
score_th_fn = theano.function([x], [scores])
return score_th_fn
cost = T.sum((scores * y.dimshuffle(0, 'x')))
cg = ComputationGraph([cost])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
updates = Adam(cost, weights)
def get_update_fn():
update_th_fn = theano.function([x, y], [cost], updates=updates)
return update_th_fn
score_th_fn = get_score_fn()
def train_fn(x, y):
y = 2 * y - 1
update_th_fn = get_update_fn()
scores = np.asarray(score_th_fn(x))
beg = time.clock()
Y_new = mvc.mvc(y.ravel(), scores, np.sum(y == 1), np.sum(y == -1),
loss_id)
print "TIME TAKEN", str(time.clock() - beg)
update_th_fn(x, Y_new)
def valid_fns(x, y):
y = 2 * y - 1
scores = np.asarray(score_th_fn(x)).ravel()
pred_labels = 2 * ((scores.ravel() > 0).astype(np.float32)) - 1
loss_fn = loss_functions.get_loss_fn(loss_id)
loss = loss_fn(y.ravel(), pred_labels)
print np.sum(scores[y.ravel() == 1])
print y.shape, np.sum(y), np.sum(pred_labels == 1), loss
return loss
return train_fn, valid_fns
def main():
# model parameters
num_particles = 4
num_hidden = 20
interaction_param = 2.0
ramp_up_speed = 0.01
time_step = 0.001
num_samples = 10000
# initialize objects
WaveFunction = FeedForwardNeuralNetwork(num_particles, num_hidden)
Hamiltonian = CalogeroSutherland(WaveFunction, interaction_param,
ramp_up_speed)
Sampler = ImportanceSampling(Hamiltonian, time_step)
Optimizer = Adam(WaveFunction)
VMC = VariationalMonteCarlo(Optimizer, Sampler, num_samples)
# run optimization
VMC.minimize_energy()
def t3():
layer_param = [(8, 3, 1, 1, 'conv'), (8, 'pool'), (64, 3, 1, 1, 'conv'),
(64, 3, 1, 1, 'conv'), (64, 'pool'), (128, 3, 1, 1, 'conv'),
(128, 3, 1, 1, 'conv'), (128, 'pool'),
(256, 3, 1, 1, 'conv'), (256, 3, 1, 1, 'conv'), (64, 'FC')]
# layer_param = [(6, 5, 1, 0, 'conv'),
# (6, 'pool'),
# (16, 5, 1, 0, 'conv'),
# (16, 'pool'),
# (120, 'FC'),
# (84, 'FC')]
regulation = L2Regulation()
activation = ReluActivation()
optimizer = Adam()
update_bn = UpdateBN()
cnn = CnnTest2(layer_param)
cnn.load_train_data(0.7)
cnn.train_random_search(10, 64, 0.9, [-3.0, -4.0], optimizer, activation,
[-3, -5], regulation, 10, 1, update_bn, True)
def train(model, data_path, cur_iter, summ):
input_ = Input(FLAGS.train_batch_size, [FLAGS.img_height, FLAGS.img_width, FLAGS.num_channels])
images, labels = input_(data_path)
logits = model(images, cur_iter = cur_iter)
# Calculate the loss of the model.
loss = LossClassification(FLAGS.num_classes, gpu = True)(logits, labels)
# Create an optimizer that performs gradient descent.
optimizer = Adam(FLAGS.learning_rate, lr_decay = False,
lr_decay_steps = FLAGS.lr_decay_steps,
lr_decay_factor = FLAGS.lr_decay_factor)
train_op = optimizer(loss)
summ.register('train', 'train_loss', loss)
summ.register('train', 'learning_rate', optimizer.lr)
train_summ_op = summ('train')
return loss, train_op, train_summ_op
def train():
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True,
one_hot_label=True)
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
iters_num = 20000
train_size = x_train.shape[0]
batch_size = 100
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
epoch_list = []
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
optimizer = Adam()
network.train(x_batch, t_batch, optimizer)
loss = network.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
epoch_list.append(i / iter_per_epoch)
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print("train accuracy : " + str(train_acc) + ", test accuracy : " +
str(test_acc))
print("final loss : " + str(loss))
draw_acc_graph(epoch_list, train_acc_list, test_acc_list)
draw_loss_graph(train_loss_list)
def train(data_path, model, summ):
input_ = Input(FLAGS.batch_size, [FLAGS.num_chans, FLAGS.num_points])
data, labels = input_(data_path)
'''
print_op = tf.print("In train procedure -> label shape: ", tf.shape(labels), output_stream = sys.stdout)
with tf.control_dependencies([print_op]):
logits = model(data)
'''
logits = model(data)
loss = LossRegression()(logits, tf.expand_dims(labels, axis=-1))
opt = Adam(FLAGS.learning_rate,
lr_decay=FLAGS.lr_decay,
lr_decay_steps=FLAGS.lr_decay_steps,
lr_decay_factor=FLAGS.lr_decay_factor)
train_op = opt(loss)
summ.register('train', 'loss', loss)
summ.register('train', 'learning_rate', opt.lr)
train_summ_op = summ('train')
return loss, train_op, train_summ_op
def test_update(self):
for vector_size in range(1, 10):
adam = Adam()
gnn = GraphNeuralNetwork(vector_size)
expected = gnn.params
adam.update(gnn)
actual = gnn.params
self.assertEqual(expected, actual)
gnn.grads["W"] = np.random.rand(vector_size, vector_size)
gnn.grads["A"] = np.random.rand(vector_size)
gnn.grads["b"] = np.random.rand(1)
v = {}
m = {}
for key, grad in gnn.grads.items():
v[key] = np.zeros_like(grad)
m[key] = np.zeros_like(grad)
params = copy.deepcopy(gnn.params)
for i in range(1, 100):
gnn.grads["W"] = np.random.rand(vector_size, vector_size)
gnn.grads["A"] = np.random.rand(vector_size)
gnn.grads["b"] = np.random.rand(1)
adam.update(gnn)
for key, param in params.items():
m[key] = adam.beta1 * m[key] + (
(1 - adam.beta1) * gnn.grads[key])
v[key] = adam.beta2 * v[key] + (
(1 - adam.beta2) * gnn.grads[key]**2)
m_hat = m[key] / (1 - adam.beta1**i)
v_hat = v[key] / (1 - adam.beta2**i)
params[key] = param - adam.lr * m_hat / (np.sqrt(v_hat) +
1.0e-8)
expected1 = repr(
np.round(np.abs(params[key] - gnn.params[key]), 6))
actual1 = repr(np.zeros_like(params[key]))
self.assertEqual(expected1, actual1)
dim = 18 * 9 + 1
w = np.zeros([dim, 1])
x = np.concatenate((np.ones([12 * 471, 1]), x), axis=1).astype(float)
learning_rate = 100
iter_time = 100
#four different model:
adagrad_result, adagrad_loss = Adagrad(x,
y,
w,
learning_rate,
iter_time=1000,
dim=dim)
rms_result, rms_loss = RMSProp(x, y, w, learning_rate, iter_time=1000, dim=dim)
sdg_result, sdg_loss = SGDm(x, y, w, learning_rate, iter_time=20, dim=dim)
adam_result, adam_loss = Adam(x, y, w, learning_rate, iter_time=1000, dim=dim)
#Visilization:
ax1 = plt.subplot(221)
ax1.plot(range(0, 1000, 100),
adagrad_loss,
color='b',
linestyle=':',
marker='o',
markerfacecolor='r',
markersize=6)
ax1.set_xlabel("Adagrad")
ax2 = plt.subplot(222)
ax2.plot(range(0, 1000, 100),
# this is a dense layer
newLayer = Dense()
newLayer.load(layerFolder)
model.layers.append(newLayer)
self.layers = self.generator.layers + self.discriminator.layers
if __name__ == "__main__":
dataset = Dataset(name="mnist",
train_size=60000,
test_size=10000,
batch_size=128)
LATENT_SIZE = 28 * 28
# set the learning rate and optimizer for training
optimizer = Adam(0.0002, 0.5)
generator = MLP()
generator.addLayer(
Dense(inputDim=LATENT_SIZE,
outputDim=256,
activation=LeakyReLU(0.2),
optimizer=optimizer))
generator.addLayer(
Dense(inputDim=256,
outputDim=512,
activation=LeakyReLU(0.2),
optimizer=optimizer))
generator.addLayer(
Dense(inputDim=512,
outputDim=1024,
