def __init__(self,
input_layer,
loss_function=None,
aggregation='mean',
**args):
Objective.__init__(self, input_layer, loss_function, aggregation)
self.alpha = args['alpha']
def create_iter_functions(self,
dataset,
output_layer,
X_tensor_type=T.matrix):
batch_index = T.iscalar('batch_index')
X_batch = X_tensor_type('x')
y_batch = T.ivector('y')
batch_slice = slice(batch_index * self.batch_size,
(batch_index + 1) * self.batch_size)
objective = Objective(output_layer,
loss_function=categorical_crossentropy)
loss_train = objective.get_loss(X_batch, target=y_batch)
loss_eval = objective.get_loss(X_batch,
target=y_batch,
deterministic=True)
pred = T.argmax(lasagne.layers.get_output(output_layer,
X_batch,
deterministic=True),
axis=1)
proba = lasagne.layers.get_output(output_layer,
X_batch,
deterministic=True)
accuracy = T.mean(T.eq(pred, y_batch), dtype=theano.config.floatX)
all_params = get_all_params(output_layer)
updates = lasagne.updates.nesterov_momentum(loss_train, all_params,
self.lr, self.momentum)
iter_train = theano.function(
[batch_index],
loss_train,
updates=updates,
givens={
X_batch: dataset['X_train'][batch_slice],
y_batch: dataset['y_train'][batch_slice],
},
on_unused_input='ignore',
)
iter_valid = None
if self.use_valid:
iter_valid = theano.function(
[batch_index],
[loss_eval, accuracy, proba],
givens={
X_batch: dataset['X_valid'][batch_slice],
y_batch: dataset['y_valid'][batch_slice],
},
)
return dict(train=iter_train, valid=iter_valid)
def test_objective_no_target(self):
from lasagne.objectives import Objective
input_layer = mock.Mock()
loss_function = mock.Mock()
input = object()
objective = Objective(input_layer, loss_function)
result = objective.get_loss(input)
input_layer.get_output.assert_called_with(input)
network_output = input_layer.get_output.return_value
loss_function.assert_called_with(network_output, objective.target_var)
assert result == loss_function.return_value.mean.return_value
def test_objective_no_target():
from lasagne.objectives import Objective
input_layer = mock.Mock()
loss_function = mock.Mock()
input = object()
objective = Objective(input_layer, loss_function)
result = objective.get_loss(input)
input_layer.get_output.assert_called_with(input)
network_output = input_layer.get_output.return_value
loss_function.assert_called_with(network_output, objective.target_var)
assert result == loss_function.return_value
def test_objective_no_target(self):
from lasagne.objectives import Objective
from lasagne.layers.input import Layer, InputLayer
input_layer = mock.Mock(InputLayer((None,)))
layer = mock.Mock(Layer(input_layer))
layer.input_layer = input_layer
loss_function = mock.Mock()
input = theano.tensor.vector()
objective = Objective(layer, loss_function)
result = objective.get_loss(input)
layer.get_output_for.assert_called_with(input)
network_output = layer.get_output_for.return_value
loss_function.assert_called_with(network_output, objective.target_var)
assert result == loss_function.return_value.mean.return_value
def test_objective_no_target(self):
from lasagne.objectives import Objective
from lasagne.layers.input import Layer, InputLayer
input_layer = mock.Mock(InputLayer((None, )), output_shape=(None, ))
layer = mock.Mock(Layer(input_layer), output_shape=(None, ))
layer.input_layer = input_layer
loss_function = mock.Mock()
input = theano.tensor.vector()
objective = Objective(layer, loss_function)
result = objective.get_loss(input)
layer.get_output_for.assert_called_with(input)
network_output = layer.get_output_for.return_value
loss_function.assert_called_with(network_output, objective.target_var)
assert result == loss_function.return_value.mean.return_value
def test_objective():
from lasagne.objectives import Objective
input_layer = mock.Mock()
loss_function = mock.Mock()
input, target, arg1, kwarg1 = (object(),) * 4
objective = Objective(input_layer, loss_function)
result = objective.get_loss(input, target, arg1, kwarg1=kwarg1)
# We expect that the input layer's `get_output` was called with
# the `input` argument we provided, plus the extra positional and
# keyword arguments.
input_layer.get_output.assert_called_with(input, arg1, kwarg1=kwarg1)
network_output = input_layer.get_output.return_value
# The `network_output` and `target` are fed into the loss
# function:
loss_function.assert_called_with(network_output, target)
assert result == loss_function.return_value
def test_objective(self):
from lasagne.objectives import Objective
input_layer = mock.Mock()
loss_function = mock.Mock()
input, target, kwarg1 = object(), object(), object()
objective = Objective(input_layer, loss_function)
result = objective.get_loss(input, target, 'mean', kwarg1=kwarg1)
# We expect that the input layer's `get_output` was called with
# the `input` argument we provided, plus the extra positional and
# keyword arguments.
input_layer.get_output.assert_called_with(input, kwarg1=kwarg1)
network_output = input_layer.get_output.return_value
# The `network_output` and `target` are fed into the loss
# function:
loss_function.assert_called_with(network_output, target)
assert result == loss_function.return_value.mean.return_value
def create_iter_functions(self, dataset, output_layer, X_tensor_type=T.matrix):
batch_index = T.iscalar('batch_index')
X_batch = X_tensor_type('x')
y_batch = T.ivector('y')
batch_slice = slice(batch_index * self.batch_size, (batch_index + 1) * self.batch_size)
objective = Objective(output_layer, loss_function=categorical_crossentropy)
loss_train = objective.get_loss(X_batch, target=y_batch)
loss_eval = objective.get_loss(X_batch, target=y_batch, deterministic=True)
pred = T.argmax(output_layer.get_output(X_batch, deterministic=True), axis=1)
proba = output_layer.get_output(X_batch, deterministic=True)
accuracy = T.mean(T.eq(pred, y_batch), dtype=theano.config.floatX)
all_params = get_all_params(output_layer)
updates = adagrad(loss_train, all_params, self.lr, self.rho)
iter_train = theano.function(
[batch_index], loss_train,
updates=updates,
givens={
X_batch: dataset['X_train'][batch_slice],
y_batch: dataset['y_train'][batch_slice],
},
on_unused_input='ignore',
)
iter_valid = None
if self.use_valid:
iter_valid = theano.function(
[batch_index], [loss_eval, accuracy, proba],
givens={
X_batch: dataset['X_valid'][batch_slice],
y_batch: dataset['y_valid'][batch_slice],
},
)
return dict(train=iter_train, valid=iter_valid)
def test_objective(self):
from lasagne.objectives import Objective
from lasagne.layers.input import Layer, InputLayer
input_layer = mock.Mock(InputLayer((None,)))
layer = mock.Mock(Layer(input_layer))
layer.input_layer = input_layer
loss_function = mock.Mock()
input, target, kwarg1 = theano.tensor.vector(), object(), object()
objective = Objective(layer, loss_function)
result = objective.get_loss(input, target, 'mean', kwarg1=kwarg1)
# We expect that the layer's `get_output_for` was called with
# the `input` argument we provided, plus the extra positional and
# keyword arguments.
layer.get_output_for.assert_called_with(input, kwarg1=kwarg1)
network_output = layer.get_output_for.return_value
# The `network_output` and `target` are fed into the loss
# function:
loss_function.assert_called_with(network_output, target)
assert result == loss_function.return_value.mean.return_value
def test_objective(self):
from lasagne.objectives import Objective
from lasagne.layers.input import Layer, InputLayer
input_layer = mock.Mock(InputLayer((None, )), output_shape=(None, ))
layer = mock.Mock(Layer(input_layer), output_shape=(None, ))
layer.input_layer = input_layer
loss_function = mock.Mock()
input, target, kwarg1 = theano.tensor.vector(), object(), object()
objective = Objective(layer, loss_function)
result = objective.get_loss(input, target, 'mean', kwarg1=kwarg1)
# We expect that the layer's `get_output_for` was called with
# the `input` argument we provided, plus the extra positional and
# keyword arguments.
layer.get_output_for.assert_called_with(input, kwarg1=kwarg1)
network_output = layer.get_output_for.return_value
# The `network_output` and `target` are fed into the loss
# function:
loss_function.assert_called_with(network_output, target)
assert result == loss_function.return_value.mean.return_value
def get_loss(self, loss_function, output, target, aggregation=None):
from lasagne.objectives import Objective
input_layer = self.input_layer(output)
obj = Objective(input_layer, loss_function)
return obj.get_loss(target=target, aggregation=aggregation)
def get_loss(self, loss_function, output, target, aggregation=None):
from lasagne.objectives import Objective
input_layer = self.input_layer(output)
obj = Objective(input_layer, loss_function)
return obj.get_loss(target=target, aggregation=aggregation)
def __init__(self, input_layer, loss_function=None, aggregation='mean',**args): Objective.__init__(self, input_layer, loss_function, aggregation) if 'alpha' in args: self.alpha=args['alpha'] else: self.alpha=0.0
min_sigma=0
)
)
print("Total parameters: {}".format(
sum([p.get_value().size
for p in lasagne.layers.get_all_params(layers[-1])])))
X = T.tensor3('X')
t = T.matrix('t')
# add test values
X.tag.test_value = floatX(np.random.rand(*SHAPE))
t.tag.test_value = floatX(np.random.rand(N_SEQ_PER_BATCH * SEQ_LENGTH, 1))
objective = Objective(layers[-1], loss_function=mdn_nll)
loss = objective.get_loss(X, t)
all_params = lasagne.layers.get_all_params(layers[-1])
updates = lasagne.updates.momentum(loss, all_params, learning_rate)
# Theano functions for training, getting output, and computing loss
print("Compiling Theano functions...")
train = theano.function([X, t], loss, updates=updates)
y_pred = theano.function([X], layers[-1].get_output(X))
compute_loss = theano.function([X, t], loss)
print("Done compiling Theano functions.")
# Train the net
costs = []
t_val = t_val.reshape((N_SEQ_PER_BATCH * SEQ_LENGTH, 1))
def __init__(self, input_layer, loss_function=None, aggregation='mean',**args): Objective.__init__(self, input_layer, loss_function, aggregation)
num_components=N_COMPONENTS,
min_sigma=0))
print("Total parameters: {}".format(
sum([
p.get_value().size for p in lasagne.layers.get_all_params(layers[-1])
])))
X = T.tensor3('X')
t = T.matrix('t')
# add test values
X.tag.test_value = floatX(np.random.rand(*SHAPE))
t.tag.test_value = floatX(np.random.rand(N_SEQ_PER_BATCH * SEQ_LENGTH, 1))
objective = Objective(layers[-1], loss_function=mdn_nll)
loss = objective.get_loss(X, t)
all_params = lasagne.layers.get_all_params(layers[-1])
updates = lasagne.updates.momentum(loss, all_params, learning_rate)
# Theano functions for training, getting output, and computing loss
print("Compiling Theano functions...")
train = theano.function([X, t], loss, updates=updates)
y_pred = theano.function([X], layers[-1].get_output(X))
compute_loss = theano.function([X, t], loss)
print("Done compiling Theano functions.")
# Train the net
costs = []
t_val = t_val.reshape((N_SEQ_PER_BATCH * SEQ_LENGTH, 1))
