def test_mnist():
(train_x, train_y), (test_x, test_y) = mnist.load_data()
val_x = train_x[50000:]
val_y = train_y[50000:]
train_x = train_x[:50000]
train_y = train_y[:50000]
batch_size = 200
modle = models.Sequential()
modle.add(layers.Linear(28, input_shape=(None, train_x.shape[1])))
modle.add(layers.ReLU())
modle.add(layers.Linear(10))
modle.add(layers.ReLU())
modle.add(layers.Linear(10))
modle.add(layers.Softmax())
acc = losses.categorical_accuracy.__name__
modle.compile(losses.CrossEntropy(),
optimizers.SGD(lr=0.001),
metrics=[losses.categorical_accuracy])
modle.summary()
history = modle.train(train_x,
train_y,
batch_size,
epochs=32,
validation_data=(val_x, val_y))
epochs = range(1, len(history["loss"]) + 1)
plt.plot(epochs, history["loss"], 'ro', label="Traning loss")
plt.plot(epochs, history["val_loss"], 'go', label="Validating loss")
plt.plot(epochs, history[acc], 'r', label="Traning accuracy")
plt.plot(epochs, history["val_" + acc], 'g', label="Validating accuracy")
plt.title('Training/Validating loss/accuracy')
plt.xlabel('Epochs')
plt.ylabel('Loss/Accuracy')
plt.legend()
plt.show(block=True)
def __init__(self, settings):
#
print "building controller ... "
self.seq_info = tensor.tensor3(dtype=dtype, name='seq_info')
self.seq_lang = tensor.tensor3(dtype=dtype, name='seq_lang')
self.seq_target = tensor.tensor3(dtype=dtype, name='seq_target')
#
self.model = models.SelGen(settings)
#
self.model.compute_loss(self.seq_info, self.seq_lang, self.seq_target)
#
assert (settings['optimizer'] == 'adam'
or settings['optimizer'] == 'sgd')
if settings['optimizer'] == 'adam':
self.adam_optimizer = optimizers.Adam(adam_params=None)
elif settings['optimizer'] == 'sgd':
self.adam_optimizer = optimizers.SGD(adam_params=None)
else:
print "Choose a optimizer ! "
#
self.adam_optimizer.compute_updates(self.model.params,
self.model.grad_params)
#
print "compiling training function ... "
self.model_learn = theano.function(
inputs=[self.seq_info, self.seq_lang, self.seq_target],
outputs=self.model.cost,
updates=self.adam_optimizer.updates)
print "compiling dev function ... "
self.model_dev = theano.function(
inputs=[self.seq_info, self.seq_lang, self.seq_target],
outputs=self.model.cost)
self.save_model = self.model.save_model
self.get_model = self.model.get_model
def compile(self, optimizer=optimizers.SGD()) :
"""
Takes all of the layers and connects them together, prepares the network
to be run.
@optimizer - instance of the optimizer class hierarchy
"""
# symbolically run through the entire network
temp_x = self.inputs
for layer in self.layers :
temp_x = layer.feed(temp_x)
# self.feed_forward hold the symbolic result for an output given an input
self.feed_forward = temp_x
self.costfn = optimizer.compile(self.feed_forward, self.outputs)
updates = optimizer.updates(self.layers)
# define a symbolic training iteration based on the input and output data,
# the cost function, and the update algorithm defined in the optimizer class
self._train = theano.function(
inputs=[self.inputs, self.outputs],
outputs=self.costfn,
updates=updates,
name="train"
)
self._evaluate = theano.function(
inputs=[self.inputs, self.outputs],
outputs=[self.costfn, self.feed_forward], # grab the cost and the raw output for
name="evaluate" # the evaluation steps
)
def __init__(self, env_name='HalfCheetah-v1',
policy_params=None,
num_workers=32,
num_deltas=320,
deltas_used=320,
delta_std=0.02,
logdir=None,
rollout_length=1000,
step_size=0.01,
shift='constant zero',
params=None,
seed=123):
logz.configure_output_dir(logdir)
logz.save_params(params)
env = minitaur_gym_env.MinitaurBulletEnv() #gym.make(env_name)
self.timesteps = 0
self.action_size = env.action_space.shape[0]
self.ob_size = env.observation_space.shape[0]
self.num_deltas = num_deltas
self.deltas_used = deltas_used
self.rollout_length = rollout_length
self.step_size = step_size
self.delta_std = delta_std
self.logdir = logdir
self.shift = shift
self.params = params
self.max_past_avg_reward = float('-inf')
self.num_episodes_used = float('inf')
# create shared table for storing noise
print("Creating deltas table.")
deltas_id = create_shared_noise.remote()
self.deltas = SharedNoiseTable(ray.get(deltas_id), seed = seed + 3)
print('Created deltas table.')
# initialize workers with different random seeds
print('Initializing workers.')
self.num_workers = num_workers
self.workers = [Worker.remote(seed + 7 * i,
env_name=env_name,
policy_params=policy_params,
deltas=deltas_id,
rollout_length=rollout_length,
delta_std=delta_std) for i in range(num_workers)]
# initialize policy
if policy_params['type'] == 'linear':
self.policy = LinearPolicy(policy_params)
self.w_policy = self.policy.get_weights()
else:
raise NotImplementedError
# initialize optimization algorithm
self.optimizer = optimizers.SGD(self.w_policy, self.step_size)
print("Initialization of ARS complete.")
def __init__(self, step_size=0.1):
Base_ARS_Agent.__init__(self)
# Hmm. Maybe this shouldn't be here.
# initialize optimization algorithm
self.optimizer = optimizers.SGD(self.weights, step_size)
print("Initialization of ARS complete.")
def train():
num_class = 10
mnist = tf.keras.datasets.mnist
fashion = False
if fashion:
from keras.datasets import fashion_mnist
mnist = fashion_mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train = np.reshape(x_train, (-1, 1, 28 * 28))
x_test = np.reshape(x_test, (-1, 1, 28 * 28))
# x_train = np.reshape(x_train, (-1, 1, 28, 28))
# x_test = np.reshape(x_test, (-1, 1, 28, 28))
X = np.array(np.append(x_train, x_test, axis=0))
Y = np.eye(num_class)[np.append(y_train, y_test)].reshape(
-1, 1, 10) # one hot vectors shape: (70000, 1, 10)
ip = Input(input_size=(1, 784))
# x = Conv2d(number_of_kernel=3, kernel_size=5, activation="relu")(ip)
# x = Pool2d(kernel_size=5)(x)
# y = Conv2d(number_of_kernel=3, kernel_size=5, activation="relu")(ip)
# y = Pool2d(kernel_size=5)(y)
# a = Add(weights_of_layers=[1, 3])([x, y])
# c = Concat(axis=1)([x, y])
# f = Flatten()(a)
x1 = Dense(units=256, activation="sigmoid")(ip)
# y1 = Dense(units=20, activation="sigmoid")(x1)
# y2 = Dense(units=20, activation="sigmoid", learning_rate=1)(x1)
# c1 = Concat(axis=1)([y1, y2])
#
# x2 = Dense(units=50, activation="sigmoid", learning_rate=1)(ip)
# z1 = Dense(units=20, activation="sigmoid", learning_rate=1)(x2)
# z2 = Dense(units=20, activation="sigmoid", learning_rate=1)(x2)
# c2 = Concat(axis=1)([z1, z2])
# c = Concat(axis=1)([c1, c2])
op = Dense(units=num_class, activation="sigmoid")(x1)
nn = NeuralNet(ip, op)
sgd = optimizers.SGD(gamma=0.9, nesterov=False)
adagrad = optimizers.Adagrad()
adadelta = optimizers.Adadelta()
rmsprop = optimizers.RMSProp(first_order_momentum=False, gamma=0)
nn.build_model(loss="XE",
optimizer=rmsprop,
learning_rate=None,
batch_size=100)
nn.train(train_x=X[:60000],
train_y=Y[:60000],
test_x=X[60000:],
test_y=Y[60000:],
epochs=10)
def compile(self, loss, optimizer=optimizers.SGD()):
self.optimizer = optimizer
self.loss = loss
curr_out_size = 0
for layer in self.layers:
curr_out_size = layer.compile(curr_out_size)
self.optimizer.compile([l.weights for l in self.layers[1:]])
def fcl02():
net = n.NeuralNetwork([
l.InputLayer(height=28, width=28),
l.FullyConnectedLayer(
10, init_func=f.glorot_uniform, act_func=f.softmax)
], f.categorical_crossentropy)
optimizer = o.SGD(0.1)
num_epochs = 1
batch_size = 10
return net, optimizer, num_epochs, batch_size
def create_mlp():
model = Mlp()
model.add_layer(16, activation('relu'), in_dims=784)
model.add_layer(32, activation('relu'))
model.add_layer(64, activation('relu'))
model.add_layer(32, activation('relu'))
model.add_layer(16, activation('relu'))
model.add_layer(10, activation('softmax'))
optimizer = optim.SGD(learning_rate=1e-3, decay=1e-6, momentum=0.9)
model.compile(loss='mean_squared_error', optimizer='sgd')
return model
def fcl01():
net = n.NeuralNetwork([
l.InputLayer(height=28, width=28),
l.FullyConnectedLayer(
100, init_func=f.glorot_uniform, act_func=f.sigmoid),
l.FullyConnectedLayer(
10, init_func=f.glorot_uniform, act_func=f.sigmoid)
], f.quadratic)
optimizer = o.SGD(3.0)
num_epochs = 1
batch_size = 100
return net, optimizer, num_epochs, batch_size
def __init__(self, model_settings):
print "building trainer ... "
self.seq_lang = tensor.ivector(name='seq_lang')
self.seq_world = tensor.matrix(
name='seq_world', dtype=dtype
)
# shape -- len_path * dim_raw_world_input
self.seq_action = tensor.ivector(name='seq_action')
#
self.model_settings = model_settings
#
self.neural_walker = models.NeuralWalker(
model_settings = self.model_settings
)
self.neural_walker.compute_loss(
self.seq_lang, self.seq_world,
self.seq_action
)
#
#
assert(
self.model_settings['optimizer'] == 'adam' or self.model_settings['optimizer'] == 'sgd'
)
if self.model_settings['optimizer'] == 'adam':
self.optimizer = optimizers.Adam()
else:
self.optimizer = optimizers.SGD()
#
self.optimizer.compute_updates(
self.neural_walker.params,
self.neural_walker.grad_params
)
#
self.model_learn = theano.function(
inputs = [
self.seq_lang, self.seq_world,
self.seq_action
],
outputs = self.neural_walker.cost,
updates = self.optimizer.updates
)
#
self.model_dev = theano.function(
inputs = [
self.seq_lang, self.seq_world,
self.seq_action
],
outputs = self.neural_walker.cost,
)
#
self.get_model = self.neural_walker.get_model
self.save_model = self.neural_walker.save_model
def cnn01():
net = n.NeuralNetwork([
l.InputLayer(height=28, width=28),
l.ConvolutionalLayer(
2, kernel_size=5, init_func=f.glorot_uniform, act_func=f.sigmoid),
l.MaxPoolingLayer(pool_size=2),
l.FullyConnectedLayer(
height=10, init_func=f.glorot_uniform, act_func=f.softmax)
], f.log_likelihood)
optimizer = o.SGD(0.1)
num_epochs = 3
batch_size = 10
return net, optimizer, num_epochs, batch_size
def cnn02():
net = n.NeuralNetwork([
l.InputLayer(height=28, width=28),
l.ConvolutionalLayer(
2, kernel_size=5, init_func=f.glorot_uniform, act_func=f.sigmoid),
l.MaxPoolingLayer(pool_size=3),
l.FullyConnectedLayer(
height=10, init_func=f.glorot_uniform, act_func=f.softmax)
], f.categorical_crossentropy)
optimizer = o.SGD(0.1)
num_epochs = 2
batch_size = 8
return net, optimizer, num_epochs, batch_size
def train(self,
X,
y,
optimizer,
epochs=1000,
X_va=None,
y_va=None,
**kwargs):
"""
This function implements the neural network's training routine.
Parameters
----------
X : numpy.ndarray
the design matrix
y : numpy.ndarray
the target column vector
optimizer: str
the type of optimizer, either SGD or CGD, that has to be used
during the training
epochs: int
the training routine's number of epochs
(Default value = 1000)
X_va: numpy.ndarray
the design matrix used for the validation
(Default value = None)
y_va: numpy.ndarray
the target column vector used for the validation
(Default value = None)
kwargs: dict
additional parameters for the optimizers' initialization
Returns
-------
"""
assert optimizer in ['SGD', 'CGD']
if optimizer == 'SGD':
self.optimizer = opt.SGD(self, **kwargs)
self.optimizer.optimize(self, X, y, X_va, y_va, epochs)
else:
self.optimizer = opt.CGD(self, **kwargs)
self.optimizer.optimize(self, X, y, X_va, y_va, **kwargs)
def run_regression():
df = np.array(pd.read_csv('data/Dataset/Training/Features_Variant_1.csv'))
model = Model.Model()
model.add_layer(layers.Input(53))
model.add_layer(layers.Dense(20, activation=af.relu))
model.add_layer(layers.Dense(1, activation=af.sigmoid))
model.compile(losses.mse, optimizers.SGD())
input_set = np.array([x[:-1] for x in df])
output_set = np.array([x[-1] for x in df]).reshape(len(input_set), 1)
# Model.save_model(model, "test")
# tmp = Model.load_model("test")
# tmp.fit(input_set, output_set, 50, 50, metric_callback=regression_metric_mse)
input_set = helpers.standard_scaler(input_set)
output_set = helpers.standard_scaler(output_set)
np.seterr(all="raise")
model.fit(input_set, output_set, 50, 100, metric_callback=regression_metric_mse)
Model.save_model(model,"SGD")
def __init__(self,
env_name='HalfCheetah-v1',
policy_params=None,
num_workers=32,
num_deltas=320,
deltas_used=320,
delta_std=0.02,
logdir=None,
rollout_length=1000,
step_size=0.01,
shift='constant zero',
params=None,
seed=123):
logz.configure_output_dir(logdir)
logz.save_params(params)
env = gym.make(env_name)
self.timesteps = 0
self.action_size = env.action_space.shape[0]
self.ob_size = env.observation_space.shape[0]
self.num_deltas = num_deltas
self.deltas_used = deltas_used
self.rollout_length = rollout_length
self.step_size = step_size
self.delta_std = delta_std
self.logdir = logdir
self.shift = shift
self.params = params
self.env_name = env_name
self.seed = seed
# exp statistics
self.max_past_avg_reward = float('-inf')
self.num_episodes_used = float('inf')
# create shared table for storing noise
print("Creating deltas table.")
# generate noise sequence of fixed size in object stores
deltas_id = create_shared_noise.remote()
self.deltas = SharedNoiseTable(ray.get(deltas_id), seed=seed + 3)
print('Created deltas table.')
# initialize workers with different random seeds
print('Initializing workers.')
self.num_workers = num_workers
self.workers = [
Worker.remote(seed + 7 * i,
env_name=env_name,
policy_params=policy_params,
deltas=deltas_id,
rollout_length=rollout_length,
delta_std=delta_std) for i in range(num_workers)
]
# initialize policy
if policy_params['type'] == 'linear':
self.policy = LinearPolicy(policy_params)
self.w_policy = self.policy.get_weights()
else:
raise NotImplementedError
# initialize optimization algorithm
self.optimizer = optimizers.SGD(self.w_policy, self.step_size)
print("Initialization of ARS complete.")
# params->return tuple dataset
self.dataset_x = []
self.dataset_y = []
self.batch_size = 2 * self.num_deltas # set batch_size equal to num_directions
self.reward_func = RewardFunction(params_dim=self.w_policy.size,
hidden_dim=100,
lr=1e-2,
seed=self.seed)
torchvision.datasets.MNIST('./data/', train=False, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
])), shuffle=True, batch_size=batch_size)
return train_loader, test_loader
if __name__ == '__main__':
torch.random.manual_seed(1234)
np.random.seed(1234)
epochs = 10
lr = 0.01
batch_size = 32
optimizer = optimizers.SGD(learning_rate=lr)
criterion = loss.CrossEntropy()
layers = [
layers.LinearLayer(784, 512),
layers.ReLU(),
layers.Dropout(keep_rate=0.8),
layers.LinearLayer(512, 512),
layers.ReLU(),
layers.Dropout(keep_rate=0.8),
layers.LinearLayer(512, 10)
]
model = Model(layers, optimizer, criterion)
train_loader, test_loader = get_dataset(batch_size)
for epoch_id in range(epochs):
model.train()
def __init__(self, env_name='HalfCheetah-v1',
policy_params=None,
num_workers=32,
num_deltas=320,
deltas_used=320,
delta_std=0.02,
logdir=None,
rollout_length=1000,
step_size=0.01,
shift='constant zero',
params=None,
seed=123):
logz.configure_output_dir(logdir)
logz.save_params(params)
env = gym.make(env_name)
self.timesteps = 0
self.action_size = env.action_space.shape[0]
self.ob_size = env.observation_space.shape[0]
self.num_deltas = num_deltas
self.deltas_used = deltas_used
self.rollout_length = rollout_length
self.step_size = step_size
self.delta_std = delta_std
self.logdir = logdir
self.shift = shift
self.params = params
self.max_past_avg_reward = float('-inf')
self.num_episodes_used = float('inf')
# Parameters for Q Learner
self.memory = ReplayMemory(10000)
self.BATCH_SIZE = 128
self.GAMMA = 0.999
self.TARGET_UPDATE = 5
# create shared table for storing noise
print("Creating deltas table.")
deltas_id = create_shared_noise.remote()
self.deltas = SharedNoiseTable(ray.get(deltas_id), seed = seed + 3)
print('Created deltas table.')
# initialize workers with different random seeds
print('Initializing workers.')
self.num_workers = num_workers
self.workers = [Worker.remote(seed + 7 * i,
env_name=env_name,
policy_params=policy_params,
deltas=deltas_id,
rollout_length=rollout_length,
delta_std=delta_std) for i in range(num_workers)]
print(self.workers[0])
# initialize policy
if policy_params['type'] == 'linear':
self.policy = LinearPolicy(policy_params)
self.w_policy = self.policy.get_weights()
elif policy_params['type'] == 'bilayer':
self.policy = BilayerPolicy(policy_params)
self.w_policy = self.policy.get_weights()
elif policy_params['type'] == 'bilayer_safe_explorer':
self.policy = SafeBilayerExplorerPolicy(policy_params,trained_weights='/home/harshit/work/ARS/trained_policies/Madras-explore7/safeQ_torch119.pt')
self.w_policy = self.policy.get_weights()
else:
raise NotImplementedError
# initialize optimization algorithm
self.optimizer = optimizers.SGD(self.w_policy, self.step_size)
print("Initialization of ARS complete.")
def __init__(self):
self.layers = []
self.loss = losses.mse
self.optimizer = optimizers.SGD()
model.add(layer.Dense(5))
w = numpy.array([[1], [9]])
w2 = numpy.array([[5, 4]])
w3 = numpy.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
text_x = numpy.random.randn(1000, 2)
text_y = numpy.dot(text_x, w)
text_y = numpy.dot(text_y, w2)
text_y = numpy.dot(text_y, w3)
text_y = text_y
model.init()
model.fit(text_x, text_y, epoch=10000, batch_num=100)
model.compile(loss="Mean_squared_error",
optimizer=optimizers.SGD(model, speed=0.000001))
model.train()
t = ""
isfirst = True
for now in model.now_model:
print(now.w)
if isfirst:
isfirst = False
t = now.w
else:
t = numpy.dot(t, now.w)
print(numpy.dot(numpy.dot(w, w2), w3))
print(w)
print(t)
def __init__(self, settings):
print("building controller ... ")
'''
seq_time_to_current : T * size_batch -- t_i - t_i-1
seq_type_event : (T+1) * size_batch -- k_i
seq_time_rep : (T+1) * size_batch * dim_time --
for each data and each time step, track the time features of event k_i
time_since_start_to_end : size_batch -- time for seq
num_sims_start_to_end : size_batch -- N for each seq
seq_mask : T * size_batch -- 1/0
seq_sims_time_to_current : N * size_batch -- s_j - t_i
seq_sims_index_in_hidden : N * size_batch -- int32
seq_sims_mask : N * size_batch -- 1/0
'''
#self.seq_time_to_end = tensor.matrix(
# dtype=dtype, name='seq_time_to_end'
#)
self.seq_time_to_current = tensor.matrix(dtype=dtype,
name='seq_time_to_current')
self.seq_type_event = tensor.imatrix(name='seq_type_event')
#self.seq_time_rep = tensor.tensor3(
# dtype=dtype, name='seq_time_rep'
#)
self.seq_time_values = tensor.matrix(dtype=dtype,
name='seq_time_values')
#
self.time_since_start_to_end = tensor.vector(
dtype=dtype, name='time_since_start_to_end')
self.num_sims_start_to_end = tensor.vector(
dtype=dtype, name='num_sims_start_to_end')
self.seq_mask = tensor.matrix(dtype=dtype, name='seq_mask')
self.seq_sims_time_to_current = tensor.matrix(
dtype=dtype, name='seq_sims_time_to_current')
self.seq_sims_index_in_hidden = tensor.imatrix(
name='seq_sims_index_in_hidden')
self.seq_sims_mask = tensor.matrix(dtype=dtype, name='seq_sims_mask')
#
self.hawkes_ctsm = models.GeneralizedNeuralHawkesCTSM_time(settings)
#
self.hawkes_ctsm.compute_loss(
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
#self.seq_time_rep,
self.seq_time_values,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden,
self.seq_sims_mask)
#
assert (settings['optimizer'] == 'adam'
or settings['optimizer'] == 'sgd')
if settings['optimizer'] == 'adam':
self.adam_optimizer = optimizers.Adam(adam_params=None)
elif settings['optimizer'] == 'sgd':
self.adam_optimizer = optimizers.SGD(adam_params=None)
else:
print("Choose a optimizer ! ")
#
self.adam_optimizer.compute_updates(self.hawkes_ctsm.params,
self.hawkes_ctsm.grad_params,
list_constrain=[])
# in this version, no hard constraints on parameters
#
print("compiling training function ... ")
self.model_learn = theano.function(
inputs=[
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
#self.seq_time_rep,
self.seq_time_values,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden,
self.seq_sims_mask
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events
],
updates=self.adam_optimizer.updates)
print("compiling dev function ... ")
self.model_dev = theano.function(
inputs=[
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
#self.seq_time_rep,
self.seq_time_values,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden,
self.seq_sims_mask
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events
])
#
#self.get_model = self.hawkes_ctsm.get_model
self.save_model = self.hawkes_ctsm.save_model
def __init__(self, settings):
print("building controller ... ")
'''
seq_time_to_end : T * size_batch -- T - t_i
seq_time_to_current : T * T * size_batch --
for each batch, it is T * T, and at each time step t,
it tracks the ( t_i - t_i' ) for all t_i' < t_i
seq_type_event : T * size_batch -- for each data
and each time step, tracks the type of event k_i
time_since_start_to_end : size_batch -- time for seq
#
seq_mask : T * size_batch -- 1/0
seq_mask_to_current : T * T * size_batch -- 1/0
'''
self.seq_time_to_end = tensor.matrix(dtype=dtype,
name='seq_time_to_end')
self.seq_time_to_current = tensor.tensor3(dtype=dtype,
name='seq_time_to_current')
self.seq_type_event = tensor.imatrix(name='seq_type_event')
self.time_since_start_to_end = tensor.vector(
dtype=dtype, name='time_since_start_to_end')
self.seq_mask = tensor.matrix(dtype=dtype, name='seq_mask')
self.seq_mask_to_current = tensor.tensor3(dtype=dtype,
name='seq_mask_to_current')
#
self.seq_sims_time_to_current = tensor.tensor3(
dtype=dtype, name='seq_sims_time_to_current')
self.seq_sims_mask = tensor.matrix(dtype=dtype, name='seq_sims_mask')
self.seq_sims_mask_to_current = tensor.tensor3(
dtype=dtype, name='seq_sims_mask_to_current')
#
#
self.hawkes_ctsm = models.HawkesCTSM(settings)
#
self.hawkes_ctsm.compute_loss(self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
self.time_since_start_to_end,
self.seq_mask, self.seq_mask_to_current)
#
assert (settings['optimizer'] == 'adam'
or settings['optimizer'] == 'sgd')
if settings['optimizer'] == 'adam':
self.adam_optimizer = optimizers.Adam(adam_params=None)
elif settings['optimizer'] == 'sgd':
self.adam_optimizer = optimizers.SGD(adam_params=None)
else:
print("Choose a optimizer ! ")
#
if 'learn_rate' in settings:
print("learn rate is set to : ", settings['learn_rate'])
self.adam_optimizer.set_learn_rate(settings['learn_rate'])
#
self.adam_optimizer.compute_updates(self.hawkes_ctsm.params,
self.hawkes_ctsm.grad_params,
list_constrain=range(3))
#
print("compiling training function ... ")
self.model_learn = theano.function(
inputs=[
self.seq_time_to_end, self.seq_time_to_current,
self.seq_type_event, self.time_since_start_to_end,
self.seq_mask, self.seq_mask_to_current
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events
],
updates=self.adam_optimizer.updates)
print("compiling dev function ... ")
self.model_dev = theano.function(
inputs=[
self.seq_time_to_end, self.seq_time_to_current,
self.seq_type_event, self.time_since_start_to_end,
self.seq_mask, self.seq_mask_to_current
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events
])
if settings['predict_lambda']:
print("compiling dev function for intensity computation ... ")
self.hawkes_ctsm.compute_lambda(self.seq_type_event,
self.seq_sims_time_to_current,
self.seq_sims_mask,
self.seq_sims_mask_to_current)
self.model_dev_lambda = theano.function(
inputs=[
self.seq_type_event, self.seq_sims_time_to_current,
self.seq_sims_mask, self.seq_sims_mask_to_current
],
outputs=[
self.hawkes_ctsm.lambda_samples,
self.hawkes_ctsm.num_of_samples
])
#
#self.get_model = self.hawkes_ctsm.get_model
self.save_model = self.hawkes_ctsm.save_model
def __init__(self, settings):
print("building controller ... ")
'''
seq_time_to_current : T * size_batch -- t_i - t_i-1
seq_type_event : (T+1) * size_batch -- k_i
seq_time_rep : (T+1) * size_batch * dim_time --
for each data and each time step, track the time features of event k_i
time_since_start_to_end : size_batch -- time for seq
num_sims_start_to_end : size_batch -- N for each seq
seq_mask : T * size_batch -- 1/0
seq_sims_time_to_current : N * size_batch -- s_j - t_i
seq_sims_index_in_hidden : N * size_batch -- int32
seq_sims_mask : N * size_batch -- 1/0
'''
#self.seq_time_to_end = tensor.matrix(
# dtype=dtype, name='seq_time_to_end'
#)
self.seq_time_to_current = tensor.matrix(dtype=dtype,
name='seq_time_to_current')
self.seq_type_event = tensor.imatrix(name='seq_type_event')
#self.seq_time_rep = tensor.tensor3(
# dtype=dtype, name='seq_time_rep'
#)
self.seq_time_values = tensor.matrix(dtype=dtype,
name='seq_time_values')
#
self.time_since_start_to_end = tensor.vector(
dtype=dtype, name='time_since_start_to_end')
self.num_sims_start_to_end = tensor.vector(
dtype=dtype, name='num_sims_start_to_end')
self.seq_mask = tensor.matrix(dtype=dtype, name='seq_mask')
self.seq_sims_time_to_current = tensor.matrix(
dtype=dtype, name='seq_sims_time_to_current')
self.seq_sims_index_in_hidden = tensor.imatrix(
name='seq_sims_index_in_hidden')
self.seq_sims_mask = tensor.matrix(dtype=dtype, name='seq_sims_mask')
self.time_diffs = tensor.vector(dtype=dtype, name='time_diffs')
#
#
if settings['model'] == 'neuraladapttime':
self.hawkes_ctsm = models.NeuralHawkesAdaptiveBaseCTSM_time(
settings)
list_constrain = []
elif settings['model'] == 'neuraladapttimescale':
self.hawkes_ctsm = models.NeuralHawkesAdaptiveBaseCTSM_time_scale(
settings)
list_constrain = [0]
elif settings['model'] == 'neuralreduce':
self.hawkes_ctsm = models.NeuralHawkesAdaptiveBaseCTSM_time_scale_r(
settings)
list_constrain = [0]
elif settings['model'] == 'conttime':
self.hawkes_ctsm = models.NeuralHawkesCTLSTM(settings)
list_constrain = [0]
else:
print("called wrong controller")
#
assert (settings['loss_type'] == 'loglikehood'
or settings['loss_type'] == 'prediction')
#
if settings['loss_type'] == 'loglikehood':
print("train with log-likelihood ... ")
self.hawkes_ctsm.compute_loss(
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
#self.seq_time_rep,
self.seq_time_values,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden,
self.seq_sims_mask)
else:
print("train with prediction ... ")
#TODO: need to add switch for less memory
#or faster speed
#self.hawkes_ctsm.compute_prediction_loss(
self.hawkes_ctsm.compute_prediction_loss_lessmem(
self.seq_type_event, self.seq_time_values, self.seq_mask,
self.time_diffs)
#
#self.hawkes_ctsm.compute_prediction(
# self.seq_type_event,
# self.seq_time_values,
# self.seq_mask,
# self.time_diffs
#)
#
assert (settings['optimizer'] == 'adam'
or settings['optimizer'] == 'sgd')
if settings['optimizer'] == 'adam':
self.adam_optimizer = optimizers.Adam(adam_params=None)
elif settings['optimizer'] == 'sgd':
self.adam_optimizer = optimizers.SGD(adam_params=None)
else:
print("Choose a optimizer ! ")
#
if 'learn_rate' in settings:
print("learn rate is set to : ", settings['learn_rate'])
self.adam_optimizer.set_learn_rate(settings['learn_rate'])
#
self.adam_optimizer.compute_updates(self.hawkes_ctsm.params,
self.hawkes_ctsm.grad_params,
list_constrain=list_constrain)
# in this version, no hard constraints on parameters
#
if settings['loss_type'] == 'loglikehood':
print("optimize loglikehood ... ")
print("compiling training function ... ")
self.model_learn = theano.function(
inputs=[
self.seq_time_to_current, self.seq_type_event,
self.seq_time_values, self.time_since_start_to_end,
self.num_sims_start_to_end, self.seq_mask,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden, self.seq_sims_mask
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events
],
updates=self.adam_optimizer.updates,
on_unused_input='ignore')
print("compiling dev function ... ")
self.model_dev = theano.function(
inputs=[
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
#self.seq_time_rep,
self.seq_time_values,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden,
self.seq_sims_mask
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events,
self.hawkes_ctsm.last_hidden_t,
self.hawkes_ctsm.last_cell_t,
self.hawkes_ctsm.last_cell_target,
self.hawkes_ctsm.last_cell,
self.hawkes_ctsm.last_cell_decay,
self.hawkes_ctsm.last_gate_output
],
on_unused_input='ignore')
if settings['predict_lambda']:
print("compiling dev function for intensity computation ... ")
self.hawkes_ctsm.compute_lambda(self.seq_type_event,
self.seq_time_values,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden,
self.seq_sims_mask)
self.model_dev_lambda = theano.function(
inputs=[
self.seq_type_event, self.seq_time_values,
self.seq_sims_time_to_current,
self.seq_sims_index_in_hidden, self.seq_sims_mask
],
outputs=[
self.hawkes_ctsm.lambda_samples,
self.hawkes_ctsm.num_of_samples
],
on_unused_input='ignore')
else:
print("optimize prediction ... ")
print("compiling training function ... ")
self.model_learn = theano.function(
inputs=[
self.seq_type_event, self.seq_time_values, self.seq_mask,
self.time_diffs
],
outputs=[
self.hawkes_ctsm.log_likelihood_type_predict,
self.hawkes_ctsm.num_of_errors,
self.hawkes_ctsm.square_errors,
self.hawkes_ctsm.num_of_events
#self.hawkes_ctsm.abs_grad_params
],
updates=self.adam_optimizer.updates,
on_unused_input='ignore')
print("compiling dev function ... ")
self.model_dev = theano.function(
inputs=[
self.seq_type_event, self.seq_time_values, self.seq_mask,
self.time_diffs
],
outputs=[
self.hawkes_ctsm.log_likelihood_type_predict,
self.hawkes_ctsm.num_of_errors,
self.hawkes_ctsm.square_errors,
self.hawkes_ctsm.num_of_events
#self.hawkes_ctsm.abs_grad_params
#
],
on_unused_input='ignore')
#
#
self.get_model = self.hawkes_ctsm.get_model
self.save_model = self.hawkes_ctsm.save_model
def __init__(self, settings):
print("building controller ... ")
'''
seq_time_to_end : T * size_batch -- T - t_i
seq_time_to_current : T * T * size_batch --
for each batch, it is T * T, and at each time step t,
it tracks the ( t_i - t_i' ) for all t_i' < t_i
seq_type_event : T * size_batch -- for each data
and each time step, tracks the type of event k_i
time_since_start_to_end : size_batch -- time for seq
num_sims_start_to_end : size_batch -- # of samples for seq
#
seq_mask : T * size_batch -- 1/0
seq_mask_to_current : T * T * size_batch -- 1/0
seq_sims_time_to_current : N * T * size_batch
seq_sims_mask_to_current : N * T * size_batch
seq_sims_mask : N * size_batch
'''
#self.seq_time_to_end = tensor.matrix(
# dtype=dtype, name='seq_time_to_end'
#)
self.seq_time_to_current = tensor.tensor3(dtype=dtype,
name='seq_time_to_current')
self.seq_type_event = tensor.imatrix(name='seq_type_event')
self.time_since_start_to_end = tensor.vector(
dtype=dtype, name='time_since_start_to_end')
self.num_sims_start_to_end = tensor.vector(
dtype=dtype, name='num_sims_start_to_end')
self.seq_mask = tensor.matrix(dtype=dtype, name='seq_mask')
self.seq_mask_to_current = tensor.tensor3(dtype=dtype,
name='seq_mask_to_current')
self.seq_sims_time_to_current = tensor.tensor3(
dtype=dtype, name='seq_sims_time_to_current')
self.seq_sims_mask_to_current = tensor.tensor3(
dtype=dtype, name='seq_sims_mask_to_current')
self.seq_sims_mask = tensor.matrix(dtype=dtype, name='seq_sims_mask')
#
if settings['model'] == 'hawkesinhib':
self.hawkes_ctsm = models.HawkesInhibCTSM(settings)
list_constrain = [2]
elif settings['model'] == 'hawkesinhibscale':
self.hawkes_ctsm = models.HawkesInhibCTSM_scale(settings)
list_constrain = [0, 3]
else:
print("called wrong controller")
#
#
self.hawkes_ctsm.compute_loss(
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_mask_to_current,
self.seq_sims_time_to_current,
self.seq_sims_mask_to_current,
self.seq_sims_mask)
#
assert (settings['optimizer'] == 'adam'
or settings['optimizer'] == 'sgd')
if settings['optimizer'] == 'adam':
self.adam_optimizer = optimizers.Adam(adam_params=None)
elif settings['optimizer'] == 'sgd':
self.adam_optimizer = optimizers.SGD(adam_params=None)
else:
print("Choose a optimizer ! ")
#
self.adam_optimizer.compute_updates(self.hawkes_ctsm.params,
self.hawkes_ctsm.grad_params,
list_constrain=list_constrain)
#
print("compiling training function ... ")
self.model_learn = theano.function(
inputs=[
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_mask_to_current,
self.seq_sims_time_to_current,
self.seq_sims_mask_to_current,
self.seq_sims_mask
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events
],
updates=self.adam_optimizer.updates)
print("compiling dev function ... ")
self.model_dev = theano.function(
inputs=[
#self.seq_time_to_end,
self.seq_time_to_current,
self.seq_type_event,
self.time_since_start_to_end,
self.num_sims_start_to_end,
self.seq_mask,
self.seq_mask_to_current,
self.seq_sims_time_to_current,
self.seq_sims_mask_to_current,
self.seq_sims_mask
],
outputs=[
self.hawkes_ctsm.log_likelihood_seq,
self.hawkes_ctsm.log_likelihood_time,
self.hawkes_ctsm.log_likelihood_type,
self.hawkes_ctsm.num_of_events
])
#
#self.get_model = self.hawkes_ctsm.get_model
self.save_model = self.hawkes_ctsm.save_model
train_set, val_set, test_set = pickle.load(open("mnist.pkl", "rb"),
encoding='latin1')
model = model.Sequence()
model.add(layer.Dense(300, input_dim=28 * 28, activation="Relu"))
#model.add(layer.Dense(300, activation="Relu"))
model.add(layer.Dense(10))
train_y = util.to_categorical(train_set[1])
idx = numpy.random.choice(train_set[0].shape[0], 50000)
train_set = train_set[0][idx]
train_y = train_y[idx]
model.init()
model.fit(input_data=train_set, output_data=train_y, epoch=500, batch_num=10)
model.compile(optimizer=optimizers.SGD(model, 0.1), loss="Mean_squared_error")
model.train()
id = 0
rightnum = 0
for now in val_set[0]:
# plt.imshow(numpy.reshape(now,(28,28)))
# plt.show()
ans = val_set[1][id]
res = model.predict(now)
ansnum = numpy.argmax(res)
if (ansnum == ans):
rightnum += 1
else:
print(ans, ansnum)
id += 1
batch_size = 50
num_epochs = 100
num_classes = 2
hidden_units = 100
hidden_units2 = 10
dimensions = 2
# PeaksData da, SwissRollData, GMMData
X_train, y_train, X_test, y_test = utils.get_data('PeaksData')
X_train, y_train = shuffle(X_train, y_train)
# gradient and jacobian tests
grad_test_W(X_train, y_train)
grad_test_b(X_train, y_train)
jacobian_test_W(X_train, y_train)
jacobian_test_b(X_train, y_train)
grad_test_W_whole_network(X_train, y_train)
grad_test_b_whole_network(X_train, y_train)
model = models.MyNeuralNetwork()
model.add(layers.Linear(dimensions, hidden_units))
model.add(activations.ReLU())
model.add(layers.Softmax(hidden_units, 5))
optimizer = optimizers.SGD(model.parameters, lr=0.1)
losses, train_accuracy, test_accuracy = model.fit(X_train, y_train, X_test,
y_test, batch_size,
num_epochs, optimizer)
# plotting
utils.plot_scores(train_accuracy, test_accuracy)
IN_DIM = train_x.shape[1]
HIDDEN_DIM = 100
autoencoder = AutoEncoder(IN_DIM, HIDDEN_DIM, weight_sharing=True)
N_EPOCHS = 1000
bar = Progbar(N_EPOCHS)
BATCH_SIZE = 64
lr = 0.1
NOISE = 1.
cross_entropy = binary_cross_entropy()
mse_loss = mean_square_error()
optimizer = optim.SGD(autoencoder.parameters(), lr=0.01)
for epoch in xrange(N_EPOCHS):
steps = (train_x.shape[0] //
BATCH_SIZE) if train_x.shape[0] % BATCH_SIZE == 0 else (
train_x.shape[0] // BATCH_SIZE) + 1
entropy_train_loss = 0.
mse_train_loss = 0.
for ix in xrange(steps):
batch_x = train_x[ix:ix + BATCH_SIZE]
mask = np.random.binomial(1, NOISE, batch_x.shape)
input_x = batch_x * mask
reconstruction = autoencoder(input_x)
loss = cross_entropy(batch_x, reconstruction)
entropy_train_loss += loss
mse_train_loss += mse_loss(batch_x, reconstruction)
def __init__(self,
env_name='CarEnv',
policy_params=None,
num_workers=32,
num_deltas=320,
deltas_used=320,
delta_std=0.02,
std_decay=0.0,
logdir=None,
rollout_length=1000,
learning_rate=0.01,
lr_decay=0.0,
shift='constant zero',
params=None,
seed=123,
seconds_per_episode=15,
eval_rollouts=None,
log_every=10,
show_cam=1,
enable_gpu=False):
logz.configure_output_dir(logdir)
logz.save_params(params)
env = CarEnv()
# Create base CNN for finding edges
#base_model = VGG19(weights='imagenet',
# include_top=False,
# input_shape=(env.img_height,
# env.img_width,
# 3
# )
# )
self.timesteps = 0
self.action_size = env.action_space.shape[0]
self.num_deltas = num_deltas
self.deltas_used = deltas_used
self.rollout_length = rollout_length
self.learning_rate = learning_rate
self.lr_decay = lr_decay
self.delta_std = delta_std
self.std_decay = std_decay
self.logdir = logdir
self.shift = shift
self.params = params
self.max_past_avg_reward = float('-inf')
self.num_episodes_used = float('inf')
self.log_every = log_every
self.eval_rollouts = eval_rollouts or self.num_deltas
# create shared table for storing noise
print("Creating deltas table.")
deltas_id = create_shared_noise.remote()
self.deltas = SharedNoiseTable(ray.get(deltas_id), seed=seed + 3)
print('Created deltas table.')
# initialize workers with different random seeds
print('Initializing workers.')
self.num_workers = num_workers
self.workers = [
Worker.remote(
seed + 7 * i,
env_name=env_name,
policy_params=policy_params,
deltas=deltas_id,
rollout_length=rollout_length,
delta_std=delta_std,
show_cam=False,
num_workers=self.num_workers,
enable_gpu=enable_gpu
#initial_weights=initial_weights,
#initial_mean=initial_mean,
#initial_std=initial_std
) for i in range(num_workers - show_cam)
]
# Show the number of desired worker cams
for i in range(show_cam):
self.workers.append(
Worker.remote(
seed + 7 * i,
env_name=env_name,
policy_params=policy_params,
deltas=deltas_id,
rollout_length=rollout_length,
delta_std=delta_std,
#initial_weights=initial_weights,
show_cam=True,
num_workers=self.num_workers,
enable_gpu=enable_gpu))
# initialize policy
if policy_params['type'] == 'linear':
self.policy = LinearPolicy(policy_params)
self.w_policy = self.policy.get_weights()
else:
raise NotImplementedError
# initialize optimization algorithm
self.optimizer = optimizers.SGD(self.w_policy, self.learning_rate,
self.lr_decay)
print("Initialization of ARS complete.")
y_test = convert_to_one_hot_labels(x_train, y_test, -1)
### Testing the speed of our own framework ###
# Defining the model architecture
model = containers.Sequential(layers.Linear(2, 25, with_bias=True),
activations.ReLU(),
layers.Linear(25, 25, with_bias=True),
activations.ReLU(),
layers.Linear(25, 25, with_bias=True),
activations.ReLU(),
layers.Linear(25, 2, with_bias=True),
activations.Tanh())
criterion = losses.LossMSE()
optimizer = optimizers.SGD(model.param(), learning_rate=0.001)
def compute_nb_errors(model, data_input, data_target):
mini_batch_size = 100
n_misclassified = 0
for b in range(0, data_input.size(0), mini_batch_size):
batch_output = model.forward(data_input.narrow(0, b, mini_batch_size))
batch_target = data_target.narrow(0, b, mini_batch_size)
output_class = batch_output.max(1)[1]
target_class = batch_target.max(1)[1]
n_misclassified += (output_class != target_class).sum()
error = n_misclassified / data_input.size(0)
return error
'''
PFN internship 2019 coding task
machine learning
task-3
Issei NAKASONE
'''
import datasets as D
import optimizers as op
from gnn import GNN, TrainGNN
from iterator import Iterator
dirpath = '../datasets/train/'
batch_size = 128
train, test = D.get_dataset(dirpath, test_ratio=0.25)
train_iter = Iterator(train, batch_size)
test_iter = Iterator(test, batch_size)
model = GNN()
optimizer = op.SGD()
#optimizer = op.MomentumSGD()
optimizer.setup(model)
trainer = TrainGNN(optimizer, train_iter, test_iter)
trainer.start(epoch=50)
