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gaitRNNmodel.py
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gaitRNNmodel.py
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from nn.components.layers.RecurrentLayer import *
from nn.components.layers.SigmoidLayer import *
from nn.components.layers.SoftmaxLayer import *
from nn.components.layers.InputLayer import *
from nn.components.connections.FullConnection import *
from nn.components.connections.Bias import *
from nn.components.objectives.MSEObjective import *
from nn.components.objectives.CrossEntropyObjective import *
from nn.models.RecurrentNeuralNetwork import *
from trainers.SGD import SGDTrainer
import numpy as np
import cPickle as pickle
# Build the network
def buildNetwork(rnn_size):
"""
Build an RNN with softmax output
"""
net = RecurrentNeuralNetwork()
input_layer = InputLayer(27) # Input is 27 unit vector
recurrent_layer = RecurrentLayer(rnn_size)
output_layer = SoftmaxLayer(34)
input_to_recurrent = FullConnection(input_layer.output, recurrent_layer.input)
recurrent_to_output = FullConnection(recurrent_layer.output, output_layer.input)
recurrent_bias = Bias(recurrent_layer.input)
output_bias = Bias(output_layer.input)
target_layer = InputLayer(34) # 34 classes!
objective_layer = CrossEntropyObjective(output_layer.output, target_layer.output)
net.addLayer(input_layer)
net.addLayer(target_layer)
net.addConnection(input_to_recurrent)
net.addConnection(recurrent_bias)
net.addRecurrentLayer(recurrent_layer)
net.addConnection(recurrent_to_output)
net.addConnection(output_bias)
net.addLayer(output_layer)
net.addObjective(objective_layer)
net.setInputLayer(input_layer)
net.setTargetLayer(target_layer)
net.setOutputLayer(output_layer)
net.setObjective(objective_layer)
net.randomize()
return net
def loadNetwork(filename):
f = open(filename, 'rb')
net = pickle.load(f)
f.close()
return net
def run():
# Parameters
RNN_SIZE = 50
LEARNING_RATE = 0.00001
MOMENTUM = 0.5
WEIGHT_DECAY = 0.0001
START_EPOCH = 6750
NUMBER_EPOCHS = 15000
print "Building network and trainer..."
# Get the model and trainer
# rnn_net = buildNetwork(RNN_SIZE)
rnn_net = loadNetwork('RNN_model_snapshot_6750.pkl')
trainer = SGDTrainer(rnn_net, learning_rate = LEARNING_RATE, momentum = MOMENTUM, weight_decay = WEIGHT_DECAY)
print
training_filename = 'training_dataset.pkl'
validation_filename = 'validation_dataset.pkl'
test_filename = 'test_dataset.pkl'
print "Loading training data..."
f=open(training_filename, 'rb')
training_dataset = pickle.load(f)
f.close()
print "Loading validation data..."
f=open(validation_filename, 'rb')
validation_dataset = pickle.load(f)
f.close()
print "Loading test data..."
f=open(test_filename, 'rb')
test_dataset = pickle.load(f)
f.close()
print
print "Training and getting objective!"
print
# Let's classify things first and see how well it's starting off as
training_objective = rnn_net.getSequenceObjective(training_dataset)
validation_objective = rnn_net.getSequenceObjective(validation_dataset)
print "Step # 0:\tTraining Objective =", training_objective,", \t Validation Objective =", validation_objective
objective_file = open('RNN_model_objectives.txt', 'a')
# objective_file.write('Epoch Number, Training Objective, Validation Objective\n')
# objective_file.write(str(0) + ',' + str(training_objective) + ',' + str(validation_objective) + '\n')
# Let's start training!
for i in range(START_EPOCH, NUMBER_EPOCHS):
trainer.trainBatch(training_dataset)
training_objective = rnn_net.getSequenceObjective(training_dataset)
validation_objective = rnn_net.getSequenceObjective(validation_dataset)
print "Step #", i+1 ,":\tTraining Objective = ", training_objective,", \t Validation Objective =", validation_objective
objective_file.write(str(i+1) + ',' + str(training_objective) + ',' + str(validation_objective) + '\n')
objective_file.flush()
if (i+1)%25 == 0:
# Save the model!
f=open('RNN_model_snapshot_'+str(i+1)+'.pkl','wb')
# But don't save the dataset, cuz that part's big!
# rnn_net.setInput(np.zeros((0,0)))
# rnn_net.setTarget(np.zeros((0,0)))
pickle.dump(rnn_net, f)
f.close()
objective_file.close()
if __name__=='__main__':
run()