def __init__(self, state_num, action_num, experience_replay=True):
self.state_num = state_num
self.action_num = action_num
self.experience_replay = experience_replay
self.experience_pool = []
self.model = get_model(state_num, action_num)
train_conf = TrainerConfig()
train_conf.learning_rate = LEARNING_RATE
train_conf.weight_l2 = 0
self.trainer = SGDTrainer(self.model, train_conf)
self.trainer.training_names = []
self.trainer.training_variables = []
self.thread_lock = threading.Lock()
self.epsilon = EPSILON
self.tick = 0
ap.add_argument("--model", default="")
ap.add_argument("--small", action="store_true")
args = ap.parse_args()
vocab, lmdata = load_data(small=args.small, history_len=5, batch_size=64)
import pdb
pdb.set_trace()
model = NeuralLM(vocab.size)
model.stack(
RNN(hidden_size=100,
output_type="sequence",
hidden_activation='sigmoid',
persistent_state=True,
batch_size=lmdata.size,
reset_state_for_input=0),
ClassOutputLayer(output_size=100, class_size=100))
if os.path.exists(args.model):
model.load_params(args.model)
trainer = SGDTrainer(
model, {
"learning_rate": LearningRateAnnealer.learning_rate(1.2),
"weight_l2": 1e-7
})
annealer = LearningRateAnnealer()
trainer.run(lmdata, epoch_controllers=[annealer])
model.save_params(default_model)
# -*- coding: utf-8 -*-
import os
from deepy.networks import AutoEncoder
from deepy.layers import RNN, Dense
from deepy.trainers import SGDTrainer, LearningRateAnnealer
from util import get_data, VECTOR_SIZE, SEQUENCE_LENGTH
HIDDEN_SIZE = 50
model_path = os.path.join(os.path.dirname(__file__), "models", "rnn1.gz")
if __name__ == '__main__':
model = AutoEncoder(rep_dim=10, input_dim=VECTOR_SIZE, input_tensor=3)
model.stack_encoders(
RNN(hidden_size=HIDDEN_SIZE, input_type="sequence", output_type="one"))
model.stack_decoders(
RNN(hidden_size=HIDDEN_SIZE,
input_type="one",
output_type="sequence",
steps=SEQUENCE_LENGTH), Dense(VECTOR_SIZE, 'softmax'))
trainer = SGDTrainer(model)
annealer = LearningRateAnnealer(trainer)
trainer.run(get_data(), controllers=[annealer])
model.save_params(model_path)
"""
An auto-encoder for compress MNIST images.
"""
import logging, os
logging.basicConfig(level=logging.INFO)
from deepy.dataset import MnistDataset, MiniBatches
from deepy.networks import AutoEncoder
from deepy.layers import Dense
from deepy.trainers import SGDTrainer, LearningRateAnnealer
from deepy.utils import shared_scalar
model_path = os.path.join(os.path.dirname(__file__), "models",
"mnist_autoencoder.gz")
if __name__ == '__main__':
model = AutoEncoder(input_dim=28 * 28, rep_dim=30)
model.stack_encoders(Dense(50, 'tanh'), Dense(30))
model.stack_decoders(Dense(50, 'tanh'), Dense(28 * 28))
trainer = SGDTrainer(model, {
'learning_rate': shared_scalar(0.05),
'gradient_clipping': 3
})
mnist = MiniBatches(MnistDataset(for_autoencoder=True), batch_size=20)
trainer.run(mnist, controllers=[LearningRateAnnealer(trainer)])
model.save_params(model_path)
if __name__ == '__main__':
ap = ArgumentParser()
ap.add_argument("--model", default=os.path.join(os.path.dirname(__file__), "models", "sequence_adding_100_2.gz"))
args = ap.parse_args()
model = NeuralRegressor(input_dim=2, input_tensor=3)
model.stack(IRNN(hidden_size=100, input_type="sequence",
output_type="one"),
Dense(1))
if os.path.exists(args.model):
model.load_params(args.model)
conf = TrainerConfig()
conf.learning_rate = LearningRateAnnealer.learning_rate(0.01)
conf.gradient_clipping = 3
conf.patience = 50
conf.gradient_tolerance = 5
conf.avoid_nan = False
trainer = SGDTrainer(model, conf)
annealer = LearningRateAnnealer(patience=20)
trainer.run(batch_set, controllers=[annealer])
model.save_params(args.model)
print "Identity matrix weight:"
print model.first_layer().W_h.get_value().diagonal()
"""
An auto-encoder for compress MNIST images.
"""
import logging, os
logging.basicConfig(level=logging.INFO)
from deepy.dataset import MnistDataset, MiniBatches
from deepy.networks import AutoEncoder
from deepy.layers import Dense
from deepy.trainers import SGDTrainer, LearningRateAnnealer
from deepy.utils import shared_scalar
model_path = os.path.join(os.path.dirname(__file__), "models",
"mnist_autoencoder.gz")
if __name__ == '__main__':
model = AutoEncoder(input_dim=28 * 28, rep_dim=30)
model.stack_encoders(Dense(50, 'tanh'), Dense(30))
model.stack_decoders(Dense(50, 'tanh'), Dense(28 * 28))
trainer = SGDTrainer(model, {
'learning_rate': graph.shared(0.05),
'gradient_clipping': 3
})
mnist = MiniBatches(MnistDataset(for_autoencoder=True), batch_size=20)
trainer.run(mnist, epoch_controllers=[LearningRateAnnealer()])
model.save_params(model_path)
