def train(config_path):
modelArgs = NetworkConfigParser.constructModelArgs(config_path, ModelArgs)
nn = NetworkConfigParser.constructNetwork(config_path)
train_path, test_path, save_path = NetworkConfigParser.getDataInfo(
config_path)
print nn
# TODO : Arguments
num_hid = nn.layers[1].num_units
shape = (None, nn.layers[0].num_units)
train, test, cold = loadTrainTest(train_path, test_path, shape=shape)
ae = AE(nn, modelArgs)
evaluate = EvaluateNN(ae)
theta = ae.nn.getFlattenParams()
ae.setParameters(theta)
iterCounter = Counter()
optimizer = getOptimizer(modelArgs.optimizer, ae, evaluate, theta, train,
test, nn, modelArgs, iterCounter,
modelArgs.batch_size, modelArgs.max_iter[0])
optimizer.step_grow = 5.0
k = 0
for info in optimizer:
print "Iteration %d" % k
if k == 5:
optimizer.step_grow = 1.2
if k % 5 == 0:
ae.setParameters(theta)
rmse, mae = evaluate.calculateRMSEandMAE(train, test)
print "Fold :%d Test RMSE: %f Test MAE: %f" % (i, rmse, mae)
if k > modelArgs.max_iter[0]:
break
k += 1
if save_path:
_theta = ae.getParameters()
np.save(save_path, _theta)
def train(config_path):
configparser = NetworkConfigParser()
nn = configparser.constructNetwork(config_path)
modelArgs = configparser.constructModelArgs(config_path, ModelArgs)
train_path, test_path, save_path = configparser.getDataInfo(config_path)
print nn
data = yaml.load(open(config_path))
params = data["params"]
k = params["k"]
n_vis = int(nn.layers[0].num_units / k)
train, test, cold_ratings = loadTrainTest(train_path, test_path, shape=(None, n_vis))
min_rating, max_rating = train.data.min(), train.data.max()
increment = 1
mapping = dict(zip(np.arange(min_rating, max_rating + increment, increment), np.arange(k)))
modelArgs.mapping = mapping
modelArgs.k = k
bintrain = binarizeSparseMatrix(train, k, mapping)
bintest = binarizeSparseMatrix(test, k, mapping)
del train
model = RBM(nn, modelArgs)
optimizer = RbmOptimizer(model)
optimizer.minibatchTrain(bintrain, bintest, test, modelArgs.batch_size)
def train(config_path):
configparser = NetworkConfigParser()
nn = configparser.constructNetwork(config_path)
modelArgs = configparser.constructModelArgs(config_path, ModelArgs)
train_path, test_path, save_path = configparser.getDataInfo(config_path)
print nn
data = yaml.load(open(config_path))
params = data["params"]
k = params["k"]
n_vis = int(nn.layers[0].num_units / k)
train, test, cold_ratings = loadTrainTest(train_path,
test_path,
shape=(None, n_vis))
min_rating, max_rating = train.data.min(), train.data.max()
increment = 1
mapping = dict(
zip(np.arange(min_rating, max_rating + increment, increment),
np.arange(k)))
modelArgs.mapping = mapping
modelArgs.k = k
bintrain = binarizeSparseMatrix(train, k, mapping)
bintest = binarizeSparseMatrix(test, k, mapping)
del train
model = RBM(nn, modelArgs)
optimizer = RbmOptimizer(model)
optimizer.minibatchTrain(bintrain, bintest, test, modelArgs.batch_size)
def train(config_path):
modelArgs = NetworkConfigParser.constructModelArgs(config_path, ModelArgs)
nn = NetworkConfigParser.constructNetwork(config_path)
train_path, test_path, save_path = NetworkConfigParser.getDataInfo(config_path)
print nn
# TODO : Arguments
num_hid = nn.layers[1].num_units
shape = (None, nn.layers[0].num_units)
train, test, cold = loadTrainTest(train_path, test_path, shape=shape)
ae = AE(nn, modelArgs)
evaluate = EvaluateNN(ae)
theta = ae.nn.getFlattenParams()
ae.setParameters(theta)
iterCounter = Counter()
optimizer = getOptimizer(
modelArgs.optimizer,
ae,
evaluate,
theta,
train,
test,
nn,
modelArgs,
iterCounter,
modelArgs.batch_size,
modelArgs.max_iter[0],
)
optimizer.step_grow = 5.0
k = 0
for info in optimizer:
print "Iteration %d" % k
if k == 5:
optimizer.step_grow = 1.2
if k % 5 == 0:
ae.setParameters(theta)
rmse, mae = evaluate.calculateRMSEandMAE(train, test)
print "Fold :%d Final Train RMSE: %f Train MAE: %f" % (i, rmse, mae)
if k > modelArgs.max_iter[0]:
break
k += 1
if save_path:
_theta = ae.getParameters()
np.save(save_path, _theta)