def test_aggregate_weighted_mean():
from lasagne.objectives import aggregate
x = theano.tensor.matrix('x')
w = theano.tensor.matrix('w')
assert theano.gof.graph.is_same_graph(aggregate(x, w), (x * w).mean())
assert theano.gof.graph.is_same_graph(aggregate(x, w, mode='mean'),
(x * w).mean())
def test_aggregate_weighted_mean():
from lasagne.objectives import aggregate
x = theano.tensor.matrix('x')
w = theano.tensor.matrix('w')
assert theano.gof.graph.is_same_graph(aggregate(x, w), (x * w).mean())
assert theano.gof.graph.is_same_graph(aggregate(x, w, mode='mean'),
(x * w).mean())
def test_aggregate_invalid():
from lasagne.objectives import aggregate
with pytest.raises(ValueError) as exc:
aggregate(theano.tensor.matrix(), mode='asdf')
assert 'mode must be' in exc.value.args[0]
with pytest.raises(ValueError) as exc:
aggregate(theano.tensor.matrix(), mode='normalized_sum')
assert 'require weights' in exc.value.args[0]
def test_aggregate_invalid():
from lasagne.objectives import aggregate
with pytest.raises(ValueError) as exc:
aggregate(theano.tensor.matrix(), mode='asdf')
assert 'mode must be' in exc.value.args[0]
with pytest.raises(ValueError) as exc:
aggregate(theano.tensor.matrix(), mode='normalized_sum')
assert 'require weights' in exc.value.args[0]
def objective(layers_, target, **kwargs):
out_a_layer = layers_['output_a']
out_b_layer = layers_['output_b']
# Get the outputs
out_a, out_b = get_output([out_a_layer, out_b_layer])
# Get the targets
gt_a = T.cast(target[:, 0], 'int32')
gt_b = target[:, 1].reshape((-1, 1))
# Calculate the multi task loss
cls_loss = aggregate(categorical_crossentropy(out_a, gt_a))
reg_loss = aggregate(categorical_crossentropy(out_b, gt_b))
loss = cls_loss + reg_loss
return loss
def objective(layers,
loss_function,
target,
aggregate=aggregate,
deterministic=False,
l1=0,
l2=0,
tv=0,
get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(
output_layer, deterministic=deterministic, **get_output_kw)
loss = aggregate(loss_function(network_output, target))
if l1:
loss += regularization.regularize_layer_params(
layers[-2], regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(
layers[-2], regularization.l2) * l2
if tv:
loss += T.mean(T.abs_(network_output[:, 1:] -
network_output[:, :-1]))*tv
return loss
def test_maxpool_layer():
l_in1 = InputLayer((None, 2))
l_in2 = InputLayer((None, 20))
l_hid = DenseLayer(l_in2, num_units=30, nonlinearity=rectify)
l_pool = MaxpoolLayer([l_in1, l_hid])
l_out = DenseLayer(l_pool, num_units=1, nonlinearity=sigmoid)
bounds = theano.tensor.lmatrix('bounds')
data = theano.tensor.matrix('data')
targets = theano.tensor.matrix('targets')
predictions = get_output(l_out, {l_in1: bounds, l_in2: data})
loss = categorical_crossentropy(predictions, targets)
loss = aggregate(loss, mode='mean')
params = get_all_params(l_out)
updates_sgd = sgd(loss, params, learning_rate=0.0001)
train_function = theano.function([bounds, data, targets], updates=updates_sgd, allow_input_downcast=True)
test_bounds = np.array([[0, 3], [3, 5], [5, 7]])
test_X = np.random.randn(10, 20)
test_Y = np.array([[0], [1], [0]])
train_function(test_bounds, test_X, test_Y)
def objective(layers, loss_function, target, aggregate=aggregate, deterministic=False, get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(output_layer, deterministic=deterministic, **get_output_kw)
losses = loss_function(network_output, target)
return aggregate(losses)
def objective(layers,
loss_function,
target,
aggregate=aggregate,
aggregation_weights=None,
deterministic=False,
l1=0,
l2=0,
get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(
output_layer, deterministic=deterministic, **get_output_kw)
if isfunction(aggregation_weights):
weights = aggregation_weights(layers)
else:
weights = aggregation_weights
loss = aggregate(loss_function(network_output, target), weights)
if l1:
loss += regularization.regularize_layer_params(
layers.values(), regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(
layers.values(), regularization.l2) * l2
return loss
def objective(layers_, target, **kwargs):
out_a_layer = layers_['output_a']
out_b_layer = layers_['output_b']
# Get the outputs
out_a, out_b = get_output([out_a_layer, out_b_layer])
# Get the targets
gt_a = T.cast(target[:, 0], 'int32')
gt_b = target[:, 1].reshape((-1, 1))
# Calculate the multi task loss
cls_loss = aggregate(categorical_crossentropy(out_a, gt_a))
reg_loss = aggregate(categorical_crossentropy(out_b, gt_b))
loss = cls_loss + reg_loss
return loss
def objective(layers,
loss_function,
target,
aggregate=aggregate,
aggregation_weights=None,
deterministic=False,
l1=0,
l2=0,
get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(output_layer,
deterministic=deterministic,
**get_output_kw)
if isfunction(aggregation_weights):
weights = aggregation_weights(layers)
else:
weights = aggregation_weights
loss = aggregate(loss_function(network_output, target), weights)
if l1:
loss += regularization.regularize_layer_params(layers.values(),
regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(layers.values(),
regularization.l2) * l2
return loss
def objective(layers,
loss_function,
target,
aggregate=aggregate,
mode='mean',
weights=None,
deterministic=False,
l1=0,
l2=0,
l3=0,
l3_layers=[],
get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(
output_layer, deterministic=deterministic, **get_output_kw)
loss = aggregate(loss_function(network_output, target), weights=weights, mode=mode)
if l1:
loss += regularization.regularize_layer_params(
layers.values(), regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(
layers.values(), regularization.l2) * l2
if l3:
for layer in l3_layers:
loss += regularization.regularize_layer_params(
layer, regularization.l2) * l3
return loss
def objective(layers,
loss_function,
target,
aggregate=aggregate,
deterministic=False,
l1=0,
l2=0,
get_output_kw=None):
"""
Default implementation of the NeuralNet Objective.
:param layers: The underlying layers of the NeuralNetwork
:param loss_function: The callable loss function to use
:param target: the expected output
:param aggregate: the aggregation function to use
:param deterministic: Whether or not to get a deterministic output
:param l1: Optional l1 regularization parameter
:param l2: Optional l2 regularization parameter
:param get_output_kw: optional kwargs to pass to :meth:`NeuralNetwork.get_output`
:return: The total calculated loss
"""
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(
output_layer, deterministic=deterministic, **get_output_kw)
loss = aggregate(loss_function(network_output, target))
if l1:
loss += regularization.regularize_layer_params(
layers.values(), regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(
layers.values(), regularization.l2) * l2
return loss
def bc_with_ranking(y_prediction, y_true):
""" Trying to combine ranking loss with numeric precision"""
# first get the log loss like normal
#logloss = aggregate(T.nnet.binary_crossentropy(y_pred, y_true))
# clip the probabilities to keep stability
y_pred_clipped = T.clip(y_prediction, _EPSILON, 1.-_EPSILON)
logloss = aggregate(T.nnet.categorical_crossentropy(y_prediction, y_true))
y_pred = y_pred_clipped[:,1]
# next, build a rank loss
# translate into the raw scores before the logit
y_pred_score = T.log(y_pred / (1. - y_pred))
# determine what the maximum score for a zero outcome is
max_zerooutcome = T.max(y_pred_score * (y_true <1.))
mean_oneoutcome = T.mean(y_pred_score * (y_true > 0.1))
border = ifelse(T.gt(max_zerooutcome, mean_oneoutcome), mean_oneoutcome, max_zerooutcome)
# determine how much each score is above or below it
rankloss = y_pred_score - border
# only keep losses for positive outcomes
rankloss = rankloss * y_true
# only keep losses where the score is below the max
rankloss = T.sqr(T.clip(rankloss, -100., 0.))
# average the loss for just the positive outcomes
rankloss = T.sum(rankloss) / (T.sum(y_true > 0.1) + 1.)
# determine what the maximum score for a zero outcome is
min_oneoutcome = T.min(y_pred_score * (y_true > 0.1))
mean_zerooutcome = T.mean(y_pred_score * (y_true < 1.))
border = ifelse(T.lt(min_oneoutcome, mean_zerooutcome), mean_zerooutcome, min_oneoutcome)
# determine how much each score is above or below it
rankloss_ = y_pred_score - border
# only keep losses for positive outcomes
rankloss_ = rankloss_ * (1. - y_true)
# only keep losses where the score is below the max
rankloss_ = T.sqr(T.clip(rankloss_, 0., 100.))
# average the loss for just the positive outcomes
rankloss_ = T.sum(rankloss_, axis=0) / (T.sum(y_true < 1.) + 1.)
# return (rankloss + 1) * logloss - an alternative to try
#return rankloss + logloss
return 0.01*rankloss_ + 0.01*rankloss + logloss
def build_model(self, train_set, test_set, validation_set=None):
super(CAE, self).build_model(train_set, test_set, validation_set)
y_train = get_output(self.model, self.sym_x)
loss = aggregate(squared_error(y_train, self.sym_x), mode='mean')
loss += +1e-4 * lasagne.regularization.regularize_network_params(
self.model, lasagne.regularization.l2)
y_test = get_output(self.model, self.sym_x, deterministic=True)
loss_test = aggregate(squared_error(y_test, self.sym_x), mode='mean')
grads_collect = T.grad(loss, self.trainable_model_params)
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
clip_grad, max_norm = 1, 5
mgrads = total_norm_constraint(grads_collect, max_norm=max_norm)
mgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads]
updates = adam(mgrads, self.trainable_model_params, self.sym_lr,
sym_beta1, sym_beta2)
# Training function
x_batch = self.sh_train_x[self.batch_slice]
givens = {self.sym_x: x_batch}
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
outputs = [loss]
f_train = theano.function(inputs=inputs,
outputs=outputs,
givens=givens,
updates=updates)
# Validation and test function
givens = {self.sym_x: self.sh_test_x}
f_test = theano.function(inputs=[], outputs=[loss_test], givens=givens)
self.train_args['inputs']['batchsize'] = 128
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 1e-6
self.train_args['outputs']['loss'] = '%0.6f'
self.test_args['outputs']['loss_test'] = '%0.6f'
return f_train, f_test, None, self.train_args, self.test_args, self.validate_args
def _get_loss_function(self):
# TODO: remove `or True`
if self._loss is None:
if self._regression:
self._loss = squared_error
else:
self._loss = categorical_crossentropy
return aggregate(self._loss(self._get_output(), self.t_label), mode='mean')
def __call__(self,layers,
loss_function,
target,
aggregate=T.mean,
**kwargs):
output_layer = layers[-1]
network_output = get_output(output_layer, **kwargs)
return -aggregate(self.auc_error(network_output[:,1], target))
def _get_loss_function(self):
# TODO: remove `or True`
if self._loss is None:
if self._regression:
self._loss = squared_error
else:
self._loss = categorical_crossentropy
return aggregate(self._loss(self._get_output(), self.t_label), mode='mean')
def compute_cost(rnn_outputs, forward_probabilities, backward_pointers, x_end, y_end, label): def backward_step(backlinks, position): new_position = backlinks[position] return new_position, position initial_state = T.argmax(forward_probabilities[x_end-1,y_end-2:y_end]) + y_end - 2 results, _ = theano.scan(fn = backward_step, sequences = backward_pointers[0:x_end,:], outputs_info = [initial_state, None], go_backwards = True) alignment = label[results[1][::-1]] return aggregate(categorical_crossentropy(rnn_outputs[0:x_end], alignment), mode='sum')
def __init__(self,
num_features,
num_layers,
num_nodes,
dropout,
learning_rate,
weight_decay,
verbose=False):
self.verbose = verbose
self.input_var = T.matrix('inputs')
self.target_var = T.ivector('targets')
self.num_features = num_features
self.num_layers = num_layers
self.num_nodes = num_nodes
self.dropout = dropout
self.network = self.build_network()
self.prediction = lasagne.layers.get_output(self.network,
deterministic=True)
self.predict_function = theano.function([self.input_var],
self.prediction,
allow_input_downcast=True)
self.loss = categorical_crossentropy(self.prediction, self.target_var)
self.loss = aggregate(self.loss, mode='mean')
if not os.path.exists('models'):
os.mkdir('models')
# L2 regularization with weight decay
weightsl2 = lasagne.regularization.regularize_network_params(
self.network, lasagne.regularization.l2)
weightsl1 = lasagne.regularization.regularize_network_params(
self.network, lasagne.regularization.l1)
self.loss += weight_decay * weightsl2 #+ 1e-5*weightsl1
# ADAM training
params = lasagne.layers.get_all_params(self.network, trainable=True)
updates = lasagne.updates.adagrad(self.loss,
params,
learning_rate=learning_rate)
#updates = lasagne.updates.adam(self.loss, params)
#updates = lasagne.updates.nesterov_momentum(self.loss, params,
# learning_rate=learning_rate, momentum=momentum)
self.train = theano.function([self.input_var, self.target_var],
self.loss,
updates=updates)
self.create_test_function()
self.create_bayes_test_function()
def get_network(model):
input_data = tensor.dmatrix('x')
targets_var = tensor.dmatrix('y')
network = layers.InputLayer((model['batch_size'], model['input_vars']), input_data)
nonlin = nonlinearities.rectify
if model['hidden_nonlinearity'] != 'ReLu':
nonlin = nonlinearities.tanh
prev_layer = network
for l in range(model['nlayers']):
fc = layers.DenseLayer(prev_layer, model['units'], nonlinearity=nonlin)
if model['dropout']:
fc = layers.DropoutLayer(fc, 0.5)
prev_layer = fc
output_lin = None
if model['output_mode'] == OUTPUT_LOG:
output_lin = nonlinearities.tanh
output_layer = layers.DenseLayer(prev_layer, 1, nonlinearity=output_lin)
predictions = layers.get_output(output_layer)
if model['output_mode'] == OUTPUT_BOUNDED:
(minth, maxth) = model['maxmin'][model['control']]
maxt = theano.shared(np.ones((model['batch_size'], 1)) * maxth)
mint = theano.shared(np.ones((model['batch_size'], 1)) * minth)
predictions = tensor.min(tensor.concatenate([maxt, predictions], axis=1), axis=1)
predictions = tensor.reshape(predictions, (model['batch_size'], 1))
predictions = tensor.max(tensor.concatenate([mint, predictions], axis=1), axis=1)
predictions = tensor.reshape(predictions, (model['batch_size'], 1))
loss = objectives.squared_error(predictions, targets_var)
loss = objectives.aggregate(loss, mode='mean')
params = layers.get_all_params(output_layer)
test_prediction = layers.get_output(output_layer, deterministic=True)
test_loss = objectives.squared_error(test_prediction, targets_var)
test_loss = test_loss.mean()
updates_sgd = updates.sgd(loss, params, learning_rate=model['lr'])
ups = updates.apply_momentum(updates_sgd, params, momentum=0.9)
train_fn = theano.function([input_data, targets_var], loss, updates=ups)
pred_fn = theano.function([input_data], predictions)
val_fn = theano.function([input_data, targets_var], test_loss)
return {'train': train_fn, 'eval': val_fn, 'pred': pred_fn, 'layers': output_layer}
def objective(layers,
loss_function,
target,
aggregate=aggregate,
deterministic=False,
get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(output_layer,
deterministic=deterministic,
**get_output_kw)
losses = loss_function(network_output, target)
return aggregate(losses)
def build_loss(targets, prediction, optimization): """ setup loss function with weight decay regularization """ if optimization["objective"] == 'categorical': loss = objectives.categorical_crossentropy(prediction, targets) elif optimization["objective"] == 'binary': prediction = T.clip(prediction, 1e-7, 1-1e-7) loss = -(targets*T.log(prediction) + (1.0-targets)*T.log(1.0-prediction)) # loss = objectives.binary_crossentropy(prediction[:,loss_index], targets[:,loss_index]) elif (optimization["objective"] == 'squared_error'): loss = objectives.squared_error(prediction, targets) loss = objectives.aggregate(loss, mode='mean') return loss
def objective(layers, loss_function, target, aggregate=aggregate,
deterministic=False, get_output_kw=None):
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
first_layer = layers[1]
network_output = lasagne.layers.get_output(
output_layer, deterministic=deterministic, **get_output_kw)
if not deterministic:
losses = loss_function(network_output, target) \
+ l2 * regularization.regularize_network_params(
output_layer, regularization.l2) \
+ l1 * regularization.regularize_layer_params(
first_layer, regularization.l1)
else:
losses = loss_function(network_output, target)
return aggregate(losses)
def get_functions():
input_layer=layers.InputLayer(shape=(BATCH_SIZE, INPUT_LENGTH))
print "input_layer size: " + str(input_layer.shape[0])+","+ str(input_layer.shape[1])
layer = input_layer
for layer_num in range(len(NUM_UNITS_HIDDEN_LAYER)):
print "layer_num-"+str(layer_num)
layer=layers.DenseLayer(layer,
num_units=NUM_UNITS_HIDDEN_LAYER[layer_num],
W=lasagne.init.Normal(0.01),
nonlinearity=nonlinearities.tanh)
output_layer=layers.DenseLayer(layer,
num_units=OUTPUT_SIZE,
nonlinearity=nonlinearities.softmax)
network_output=get_output(output_layer)
expected_output=T.ivector()
loss_train=aggregate(categorical_crossentropy(network_output, expected_output), mode='mean')
all_weigths=layers.get_all_params(output_layer)
update_rule=lasagne.updates.nesterov_momentum(loss_train, all_weigths, learning_rate=LEARNING_RATE)
print "input_layer_end size: " + str(input_layer.shape[0])+","+ str(input_layer.shape[1])
train_function=theano.function(inputs=[input_layer.input_var, expected_output],
outputs=loss_train,
updates=update_rule,
allow_input_downcast=True)
prediction = T.argmax(network_output, axis=1)
accuracy = T.mean(T.eq(prediction, expected_output), dtype=theano.config.floatX) # @UndefinedVariable
test_function=theano.function(inputs=[input_layer.input_var, expected_output],
outputs=[loss_train, accuracy, prediction],
allow_input_downcast=True)
output_function=theano.function([input_layer.input_var],get_output(output_layer),
allow_input_downcast=True)
return train_function,test_function,output_function
def compute_cost(rnn_outputs, forward_probabilities, backward_pointers,
x_end, y_end, label):
def backward_step(backlinks, position):
new_position = backlinks[position]
return new_position, position
initial_state = T.argmax(
forward_probabilities[x_end - 1, y_end - 2:y_end]) + y_end - 2
results, _ = theano.scan(fn=backward_step,
sequences=backward_pointers[0:x_end, :],
outputs_info=[initial_state, None],
go_backwards=True)
alignment = label[results[1][::-1]]
return aggregate(categorical_crossentropy(rnn_outputs[0:x_end],
alignment),
mode='sum')
def grad_supervised(l_ram, labels):
"""
return:
loss = 1 / M * sum_i_{1..M} cross_entroy_loss(groundtruth, a_T)
grads = theano.grad(loss, params)
inputs:
labels = (n_batch,)
[theano tensor variable]
"""
loc_mean_t, loc_t, h_t, prob, pred = lasagne.layers.get_output(l_ram)
params = lasagne.layers.get_all_params(l_ram, trainable=True)
### loss estimation (cross entropy loss)
loss = categorical_crossentropy(prob, labels)
loss = aggregate(loss, mode='mean')
### gradient estimation
grads = theano.grad(loss, params, disconnected_inputs='ignore')
return loss, grads
def weight_decay_objective(layers,
loss_function,
target,
penalty_conv=1e-8,
penalty_conv_type = l2,
penalty_output=1e-8,
penalty_output_type = l2,
aggregate=aggregate,
deterministic=False,
get_output_kw={}):
'''
Defines L2 weight decay on network weights.
'''
net_out = get_output(layers[-1], deterministic=deterministic,
**get_output_kw)
loss = loss_function(net_out, target)
p1 = penalty_conv * regularize_layer_params(layers[1], penalty_conv_type)
p2 = penalty_output * regularize_layer_params(layers[-1], penalty_output_type)
losses = loss + p1 + p2
return aggregate(losses)
def grad_supervised(l_ram, labels):
"""
return:
loss = 1 / M * sum_i_{1..M} cross_entroy_loss(groundtruth, a_T)
grads = theano.grad(loss, params)
inputs:
labels = (n_batch,)
[theano tensor variable]
"""
loc_mean_t, loc_t, h_t, prob, pred = lasagne.layers.get_output(l_ram)
params = lasagne.layers.get_all_params(l_ram, trainable=True)
### loss estimation (cross entropy loss)
loss = categorical_crossentropy(prob, labels)
loss = aggregate(loss, mode='mean')
### gradient estimation
grads = theano.grad(loss, params, disconnected_inputs='ignore')
return loss, grads
def objective(
output_layer,
regularize_layers,
target,
loss_function=squared_error,
aggregate=aggregate,
deterministic=False,
l1=0,
l2=0,
tv=0,
):
network_output = layers.get_output(output_layer, deterministic=deterministic)
loss = aggregate(loss_function(network_output, target))
for layer in regularize_layers:
if l1:
loss += regularization.regularize_layer_params(layer, regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(layer, regularization.l2) * l2
if tv:
loss += T.mean(T.abs_(network_output[:, 1:] - network_output[:, :-1])) * tv
return loss
def weight_decay_objective(layers,
loss_function,
target,
penalty_conv=1e-8,
penalty_conv_type=l2,
penalty_output=1e-8,
penalty_output_type=l2,
aggregate=aggregate,
deterministic=False,
get_output_kw={}):
'''
Defines L2 weight decay on network weights.
'''
net_out = get_output(layers[-1],
deterministic=deterministic,
**get_output_kw)
loss = loss_function(net_out, target)
p1 = penalty_conv * regularize_layer_params(layers[1], penalty_conv_type)
p2 = penalty_output * regularize_layer_params(layers[-1],
penalty_output_type)
losses = loss + p1 + p2
return aggregate(losses)
def objective(layers,
loss_function,
target,
aggregate=aggregate,
deterministic=False,
l1=0,
l2=0,
get_output_kw=None):
"""
Default implementation of the NeuralNet objective.
:param layers: The underlying layers of the NeuralNetwork
:param loss_function: The callable loss function to use
:param target: the expected output
:param aggregate: the aggregation function to use
:param deterministic: Whether or not to get a deterministic output
:param l1: Optional l1 regularization parameter
:param l2: Optional l2 regularization parameter
:param get_output_kw: optional kwargs to pass to
:meth:`NeuralNetwork.get_output`
:return: The total calculated loss
"""
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
network_output = get_output(output_layer,
deterministic=deterministic,
**get_output_kw)
loss = aggregate(loss_function(network_output, target))
if l1:
loss += regularization.regularize_layer_params(layers.values(),
regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(layers.values(),
regularization.l2) * l2
return loss
def test_aggregate_sum():
from lasagne.objectives import aggregate
x = theano.tensor.matrix('x')
assert theano.gof.graph.is_same_graph(aggregate(x, mode='sum'), x.sum())
def train(options):
# -------- setup options and data ------------------
np.random.seed(options['seed'])
# Load options
host = socket.gethostname() # get computer hostname
start_time = datetime.datetime.now().strftime("%y-%m-%d-%H-%M")
model = importlib.import_module(options['model_file'])
# ---------- build model and compile ---------------
input_batch = T.tensor4() # input image sequences
target = T.tensor4() # target image
print('Build model...')
model = model.Model(**options['modelOptions'])
print('Compile ...')
net, outputs, filters = model.build_model(input_batch)
# compute loss
outputs = get_output(outputs + [filters])
output_frames = outputs[:-1]
output_filter = outputs[-1]
train_losses = []
for i in range(options['modelOptions']['target_seqlen']):
output_frame = output_frames[i]
if options['loss'] == 'squared_error':
frame_loss = squared_error(output_frame, target[:, [i], :, :])
elif options['loss'] == 'binary_crossentropy':
# Clipping to avoid NaN's in binary crossentropy: https://github.com/Lasagne/Lasagne/issues/436
output_frame = T.clip(output_frame, np.finfo(np.float32).eps, 1-np.finfo(np.float32).eps)
frame_loss = binary_crossentropy(output_frame, target[:,[i],:,:])
else:
assert False
train_losses.append(aggregate(frame_loss))
train_loss = sum(train_losses) / options['modelOptions']['target_seqlen']
# update
sh_lr = theano.shared(lasagne.utils.floatX(options['learning_rate'])) # to allow dynamic learning rate
layers = get_all_layers(net)
all_params = get_all_params(layers, trainable = True)
updates = adam(train_loss, all_params, learning_rate=sh_lr)
_train = theano.function([input_batch, target], train_loss, updates=updates, allow_input_downcast=True)
_test = theano.function([input_batch, target], [train_loss, output_filter] + output_frames, allow_input_downcast=True)
# ------------ data setup ----------------
print('Prepare data...')
dataset = importlib.import_module(options['dataset_file'])
dh = dataset.DataHandler(**options['datasetOptions'])
# ------------ training setup ----------------
if options['pretrained_model_path'] is not None:
checkpoint = pickle.load(open(options['pretrained_model_path'], 'rb'))
model_values = checkpoint['model_values'] # overwrite the values of model parameters
lasagne.layers.set_all_param_values(layers, model_values)
history_train = checkpoint['history_train']
start_epoch = checkpoint['epoch'] + 1
options['batch_size'] = checkpoint['options']['batch_size']
sh_lr.set_value(floatX(checkpoint['options']['learning_rate']))
else:
start_epoch = 0
history_train = []
# ------------ actual training ----------------
print 'Start training ...'
input_seqlen = options['modelOptions']['input_seqlen']
for epoch in range(start_epoch, start_epoch + options['num_epochs']):
epoch_start_time = time.time()
history_batch = []
for batch_index in range(0, options['batches_per_epoch']):
batch = dh.GetBatch() # generate data on the fly
if options['dataset_file'] == 'datasets.stereoCarsColor':
batch_input = batch[..., :input_seqlen].squeeze(axis=4) # first frames
batch_target = batch[..., input_seqlen:].squeeze(axis=4) # last frame
else:
batch_input = batch[..., :input_seqlen].transpose(0,4,2,3,1).squeeze(axis=4) # first frames
batch_target = batch[..., input_seqlen:].transpose(0,4,2,3,1).squeeze(axis=4) # last frame
# train
loss_train = _train(batch_input, batch_target)
history_batch.append(loss_train)
print("Epoch {} of {}, batch {} of {}, took {:.3f}s".format(epoch + 1, options['num_epochs'], batch_index+1, options['batches_per_epoch'], time.time() - epoch_start_time))
print(" training loss:\t{:.6f}".format(loss_train.item()))
# clear the screen
display.clear_output(wait=True)
# print statistics
history_train.append(np.mean(history_batch))
history_batch = []
print("Epoch {} of {}, took {:.3f}s".format(epoch + 1, options['num_epochs'], time.time() - epoch_start_time))
print(" training loss:\t{:.6f}".format(history_train[epoch].item()))
# set new learning rate (maybe this is unnecessary with adam updates)
if (epoch+1) % options['decay_after'] == 0:
options['learning_rate'] = sh_lr.get_value() * 0.5
print "New LR:", options['learning_rate']
sh_lr.set_value(floatX(options['learning_rate']))
# save the model
if (epoch+1) % options['save_after'] == 0:
save_model(layers, epoch, history_train, start_time, host, options)
print("Model saved")
def modifiedObjective(layers,
loss_function,
target,
aggregate=aggregate,
deterministic=False,
l1=0,
l2=0,
logitSens=0,
probSens=0,
lossSens=0,
std=None,
get_output_kw=None):
"""
Modified implementation of the NeuralNet objective.
:param layers: The underlying layers of the NeuralNetwork
:param loss_function: The callable loss function to use
:param target: the expected output
:param aggregate: the aggregation function to use
:param deterministic: Whether or not to get a deterministic output
:param l1: Optional l1 regularization parameter
:param l2: Optional l2 regularization parameter
:param lossSens: Optional loss sensitivity regularization parameter
:param lossSens: Optional loss sensitivity regularization parameter
:param lossSens: Optional loss sensitivity regularization parameter
:param get_output_kw: optional kwargs to pass to
:meth:`NeuralNetwork.get_output`
:return: The total calculated loss
"""
if get_output_kw is None:
get_output_kw = {}
output_layer = layers[-1]
logit_layer = layers[-2]
input_layer = layers[0]
network_input = input_layer.input_var
network_output = get_output(output_layer,
deterministic=deterministic,
**get_output_kw)
logit_output = get_output(logit_layer,
deterministic=deterministic,
**get_output_kw)
L = loss_function(
network_output,
lasagne.utils.one_hot(target, output_layer.output_shape[1]))
loss = aggregate(L)
if l1:
loss += regularization.regularize_layer_params(layers.values(),
regularization.l1) * l1
if l2:
loss += regularization.regularize_layer_params(layers.values(),
regularization.l2) * l2
# logit sensitivity
if logitSens:
logit = T.sum(
logit_output *
lasagne.utils.one_hot(target, output_layer.output_shape[1]),
axis=1)
G_logit = T.grad(T.sum(logit), network_input)
if std is not None:
G_logit = std * G_logit
# Sparse saliency regularization
absG_logit = T.abs_(G_logit)
sumAbsG_logit = T.sum(absG_logit, axis=(1, 2, 3))
loss += aggregate(sumAbsG_logit) * logitSens
# probability sensitivity
if probSens:
prob = T.sum(
network_output *
lasagne.utils.one_hot(target, output_layer.output_shape[1]),
axis=1)
G_prob = T.grad(T.sum(prob), network_input)
if std is not None:
G_prob = std * G_prob
# Sparse saliency regularization
absG_prob = T.abs_(G_prob)
sumAbsG_prob = T.sum(absG_prob, axis=(1, 2, 3))
loss += aggregate(sumAbsG_prob) * probSens
# Loss sensitivity
if lossSens:
G_loss = theano.grad(T.sum(L), network_input)
if std is not None:
G_loss = std * G_loss
absG_loss = T.abs_(G_loss)
loss += aggregate(T.sum(absG_loss, axis=(1, 2, 3))) * lossSens
# Double Backpropagation, uncomment if desired
#sqG = G**2
#sumSqG = T.sum(sqG,axis = (1,2,3))
#loss += aggregate(sumSqG) * tv
return loss
def test_aggregate_weighted_normalized_sum():
from lasagne.objectives import aggregate
x = theano.tensor.matrix('x')
w = theano.tensor.matrix('w')
assert theano.gof.graph.is_same_graph(aggregate(x, w, 'normalized_sum'),
(x * w).sum() / w.sum())
def loss(x, t):
return aggregate(binary_crossentropy(x, t))
def run_network(data=None, num_epochs=10, ratio=0.5):
try:
global_start_time = time()
sequence_length = 50
batchsize = 512
path_to_dataset = 'household_power_consumption.txt'
# Loading the data
if data is None:
print 'Loading data... '
X_train, y_train, X_test, y_test = data_power_consumption(
path_to_dataset, sequence_length, ratio)
else:
X_train, y_train, X_test, y_test = data
val_ratio = 0.005
val_rows = round(val_ratio * X_train.shape[0])
X_val = X_train[:val_rows]
y_val = y_train[:val_rows]
y_val = np.reshape(y_val, (y_val.shape[0], 1))
X_train = X_train[val_rows:]
y_train = y_train[val_rows:]
# Creating the Theano variables
input_var = T.tensor3('inputs')
target_var = T.matrix('targets')
# Building the Theano expressions on these variables
network = build_model(input_var)
prediction = lasagne.layers.get_output(network)
loss = squared_error(prediction, target_var)
loss = aggregate(loss)
params = lasagne.layers.get_all_params(network, trainable=True)
updates = rmsprop(loss, params, learning_rate=0.001)
test_prediction = lasagne.layers.get_output(network,
deterministic=True)
test_loss = squared_error(test_prediction, target_var)
test_loss = aggregate(test_loss)
# Compiling the graph by declaring the Theano functions
compile_time = time()
print 'Data:'
print 'X_train ', X_train.shape, ' y_train ', y_train.shape
print 'X_val ', X_val.shape, ' y_val ', y_val.shape
print 'X_test ', X_test.shape, ' y_test ', y_test.shape
print "Compiling..."
train_fn = theano.function([input_var, target_var],
loss, updates=updates)
val_fn = theano.function([input_var, target_var],
test_loss)
get_pred_fn = theano.function([input_var], prediction)
print "Compiling time : ", time() - compile_time
# For loop that goes each time through the hole training
# and validation data
# T R A I N I N G
# - - - - - - - -
print "Starting training...\n"
for epoch in range(num_epochs):
# Going over the training data
train_err = 0
train_batches = 0
start_time = time()
nb_batches = X_train.shape[0] / batchsize
time_line = np.zeros(nb_batches)
for batch in iterate_minibatches(X_train, y_train,
batchsize, shuffle=True):
current_time = time()
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
str_out = "\rTrain Batch " + str(train_batches)
str_out += "/" + str(nb_batches)
str_out += " | Loss : " + str(train_err / train_batches)[:7]
str_out += " | Remaining time (s) : "
remaining_seconds = time() - current_time
remaining_seconds *= (nb_batches - train_batches)
time_line[train_batches - 1] = round(remaining_seconds)
if (train_batches - 1) % 5 == 0:
durations = time_line[train_batches-1: train_batches+50]
durations = np.mean([t for t in durations if t > 0])
str_out += str(durations)
sys.stdout.write(str_out)
sys.stdout.flush()
print "\nGoing through validation data"
# Going over the validation data
val_err = 0
val_batches = 0
for batch in iterate_minibatches(
X_val, y_val, batchsize, shuffle=False):
inputs, targets = batch
err = val_fn(inputs, targets)
val_err += err
val_batches += 1
# Then we print the results for this epoch:
# train_batches - 1 because started at 1 and not 0
print "training loss:\t\t\t" + str(train_err / train_batches)
print "validation loss:\t\t" + str(val_err / val_batches)
print("Epoch {} of {} took {:.3f}s \n\n".format(
epoch + 1, num_epochs, time() - start_time))
# Now that the training is over, let's test the network:
test_err = 0
test_batches = 0
for batch in iterate_minibatches(
X_test, y_test, batchsize, shuffle=False):
inputs, targets = batch
err = val_fn(inputs, targets)
test_err += err
test_batches += 1
print "\nFinal results in {0} seconds:".format(
time()-global_start_time)
print "Test loss:\t\t\t{:.6f}".format(test_err / test_batches)
prediction_size = 200
predicted = get_pred_fn(X_test[:prediction_size])
try:
plt.plot(predicted)
plt.plot(y_test[prediction_size])
plt.show(block=False)
except Exception as e:
print str(e)
print "predicted = ", repr(
np.reshape(predicted[:prediction_size], (prediction_size,)))
print '\n'
print "y = ", repr(
np.reshape(y_test[:prediction_size], (prediction_size,)))
return network
except KeyboardInterrupt:
return network
def test_aggregate_sum():
from lasagne.objectives import aggregate
x = theano.tensor.matrix('x')
assert theano.gof.graph.is_same_graph(aggregate(x, mode='sum'), x.sum())
def aggregated_loss_func(prediction, target, weights=None):
loss = loss_func(prediction, target)
return aggregate(loss, mode=loss_aggregation_mode, weights=weights)
def do_regression(num_epochs=2, # No. of epochs to train
init_file=None, # Saved parameters to initialise training
epoch_size=680780, # Whole dataset size
valid_size=34848,
train_batch_multiple=10637, # No. of minibatches per batch
valid_batch_multiple=1089, # No. of minibatches per batch
train_minibatch_size=64,
valid_minibatch_size=32,
eval_multiple=50, # No. of minibatches to ave. in report
save_model=True,
input_width=19,
rng_seed=100009,
cross_val=0, # Cross-validation subset label
dataver=1, # Label for different runs/architectures/etc
rate_init=1.0,
rate_decay=0.999983):
###################################################
################# 0. User inputs ##################
###################################################
for i in range(1,len(sys.argv)):
if sys.argv[i].startswith('-'):
option = sys.argv[i][1:]
if option == 'i': init_file = sys.argv[i+1]
elif option[0:2] == 'v=' : dataver = int(option[2:])
elif option[0:3] == 'cv=' : cross_val = int(option[3:])
elif option[0:3] == 'rs=' : rng_seed = int(option[3:])
elif option[0:3] == 'ri=' : rate_init = np.float32(option[3:])
elif option[0:3] == 'rd=' : rate_decay = np.float32(option[3:])
print("Running with dataver %s" % (dataver))
print("Running with cross_val %s" % (cross_val))
###################################################
############# 1. Housekeeping values ##############
###################################################
# Batch size is possibly not equal to epoch size due to memory limits
train_batch_size = train_batch_multiple*train_minibatch_size
assert epoch_size >= train_batch_size
# Number of times we expect the training/validation generator to be called
max_train_gen_calls = (num_epochs*epoch_size)//train_batch_size
# Number of evaluations (total minibatches / eval_multiple)
num_eval = max_train_gen_calls*train_batch_multiple / eval_multiple
###################################################
###### 2. Define model and theano variables #######
###################################################
if rng_seed is not None:
print("Setting RandomState with seed=%i" % (rng_seed))
rng = np.random.RandomState(rng_seed)
set_rng(rng)
print("Defining variables...")
index = T.lscalar() # Minibatch index
x = T.tensor3('x') # Inputs
y = T.fvector('y') # Target
print("Defining model...")
network_0 = build_1Dregression_v1(
input_var=x,
input_width=input_width,
nin_units=12,
h_num_units=[64,128,256,128,64],
h_grad_clip=1.0,
output_width=1
)
if init_file is not None:
print("Loading initial model parametrs...")
init_model = np.load(init_file)
init_params = init_model[init_model.files[0]]
LL.set_all_param_values([network_0], init_params)
###################################################
################ 3. Import data ###################
###################################################
# Loading data generation model parameters
print("Defining shared variables...")
train_set_y = theano.shared(np.zeros(1, dtype=theano.config.floatX),
borrow=True)
train_set_x = theano.shared(np.zeros((1,1,1), dtype=theano.config.floatX),
borrow=True)
valid_set_y = theano.shared(np.zeros(1, dtype=theano.config.floatX),
borrow=True)
valid_set_x = theano.shared(np.zeros((1,1,1), dtype=theano.config.floatX),
borrow=True)
# Validation data (pick a single augmented instance, rand0 here)
print("Creating validation data...")
chunk_valid_data = np.load(
"./valid/data_valid_augmented_cv%s_t%s_rand0.npy"
% (cross_val, input_width)
).astype(theano.config.floatX)
chunk_valid_answers = np.load(
"./valid/data_valid_expected_cv%s.npy"
% (cross_val)
).astype(theano.config.floatX)
print("chunk_valid_answers.shape", chunk_valid_answers.shape)
print("Assigning validation data...")
valid_set_y.set_value(chunk_valid_answers[:])
valid_set_x.set_value(chunk_valid_data.transpose(0,2,1))
# Create output directory
if not os.path.exists("output_cv%s_v%s" % (cross_val, dataver)):
os.makedirs("output_cv%s_v%s" % (cross_val, dataver))
###################################################
########### 4. Create Loss expressions ############
###################################################
print("Defining loss expressions...")
prediction_0 = LL.get_output(network_0)
train_loss = aggregate(T.abs_(prediction_0 - y.dimshuffle(0,'x')))
valid_prediction_0 = LL.get_output(network_0, deterministic=True)
valid_loss = aggregate(T.abs_(valid_prediction_0 - y.dimshuffle(0,'x')))
###################################################
############ 5. Define update method #############
###################################################
print("Defining update choices...")
params = LL.get_all_params(network_0, trainable=True)
learn_rate = T.scalar('learn_rate', dtype=theano.config.floatX)
updates = lasagne.updates.adadelta(train_loss, params,
learning_rate=learn_rate)
###################################################
######### 6. Define train/valid functions #########
###################################################
print("Defining theano functions...")
train_model = theano.function(
[index, learn_rate],
train_loss,
updates=updates,
givens={
x: train_set_x[(index*train_minibatch_size):
((index+1)*train_minibatch_size)],
y: train_set_y[(index*train_minibatch_size):
((index+1)*train_minibatch_size)]
}
)
validate_model = theano.function(
[index],
valid_loss,
givens={
x: valid_set_x[index*valid_minibatch_size:
(index+1)*valid_minibatch_size],
y: valid_set_y[index*valid_minibatch_size:
(index+1)*valid_minibatch_size]
}
)
###################################################
################ 7. Begin training ################
###################################################
print("Begin training...")
sys.stdout.flush()
cum_iterations = 0
this_train_loss = 0.0
this_valid_loss = 0.0
best_valid_loss = np.inf
best_iter = 0
train_eval_scores = np.empty(num_eval)
valid_eval_scores = np.empty(num_eval)
eval_index = 0
aug_index = 0
for batch in range(max_train_gen_calls):
start_time = time.time()
chunk_train_data = np.load(
"./train/data_train_augmented_cv%s_t%s_rand%s.npy" %
(cross_val, input_width, aug_index)
).astype(theano.config.floatX)
chunk_train_answers = np.load(
"./train/data_train_expected_cv%s.npy" %
(cross_val)
).astype(theano.config.floatX)
train_set_y.set_value(chunk_train_answers[:])
train_set_x.set_value(chunk_train_data.transpose(0, 2, 1))
# Iterate over minibatches in each batch
for mini_index in range(train_batch_multiple):
this_rate = np.float32(rate_init*(rate_decay**cum_iterations))
this_train_loss += train_model(mini_index, this_rate)
cum_iterations += 1
# Report loss
if (cum_iterations % eval_multiple == 0):
this_train_loss = this_train_loss / eval_multiple
this_valid_loss = np.mean([validate_model(i) for
i in range(valid_batch_multiple)])
train_eval_scores[eval_index] = this_train_loss
valid_eval_scores[eval_index] = this_valid_loss
# Save report every five evaluations
if ((eval_index+1) % 5 == 0):
np.savetxt(
"output_cv%s_v%s/training_scores.txt" %
(cross_val, dataver),
train_eval_scores, fmt="%.5f"
)
np.savetxt(
"output_cv%s_v%s/validation_scores.txt" %
(cross_val, dataver),
valid_eval_scores, fmt="%.5f"
)
np.savetxt(
"output_cv%s_v%s/last_learn_rate.txt" %
(cross_val, dataver),
[np.array(this_rate)], fmt="%.5f"
)
# Save model if best validation score
if (this_valid_loss < best_valid_loss):
best_valid_loss = this_valid_loss
best_iter = cum_iterations-1
if save_model:
np.savez("output_cv%s_v%s/model.npz" %
(cross_val, dataver),
LL.get_all_param_values(network_0))
# Reset evaluation reports
eval_index += 1
this_train_loss = 0.0
this_valid_loss = 0.0
aug_index += 1
end_time = time.time()
print("Computing time for batch %d: %f" % (batch, end_time-start_time))
print("Best validation loss %f after %d epochs" %
(best_valid_loss, (best_iter*train_minibatch_size//epoch_size)))
del train_set_x, train_set_y, valid_set_x, valid_set_y
gc.collect()
return None
def run_mlp(train, val, num_epochs):
# Partition Data
train_rows, train_cols = train.shape
train_rows, train_cols = train_rows, (train_cols - 1)
val_rows, val_cols = val.shape
val_rows, val_cols = val_rows, (val_cols-1)
X_train,y_train = train[0:train_rows ,0:train_cols],train[0:train_rows,train_cols:]
X_val,y_val = val[0:val_rows,0:val_cols],val[0:val_rows,val_cols:]
# Theano variables
input_var = T.matrix('inputs')
target_var = T.matrix('targets')
network = build_mlp(input_var, train_cols, 1)
# """loading weight values from the previous model"""
# with np.load('model_first_run.npz') as f:
# param_values = [f['arr_%d'%i] for i in range(len(f.files))]
# param_values[0] = param_values[0][4:43]
# lasagne.layers.set_all_param_values(network,param_values)
prediction = lasagne.layers.get_output(network,input_var, deterministic = True)
loss = lasagne.objectives.binary_crossentropy(prediction, target_var)
loss = aggregate(loss, mode='mean')
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.sgd(loss, params, learning_rate=0.5)
# Train Function
train_fn = theano.function([input_var, target_var],loss, updates=updates)
# Validation function
val_fn = theano.function([input_var, target_var],loss)
# test function
f_test = theano.function([input_var], prediction)
val_err_list,train_err_list = list(),list()
print("Starting training...")
val_err_list,train_err_list = list(),list()
for epoch in range(num_epochs):
start_time = timeit.default_timer()
train_err = 0
train_batches = 0
#start_time = time.time()
for batch in iterate_minibatches(X_train,y_train,100,shuffle=True):
inputs,targets = batch
#print (inputs.shape, targets.shape)
batch_error = train_fn(inputs,targets)
#print (list(f_test(inputs)))
train_err += batch_error
train_batches += 1
#print (batch_error, train_batches)
#if (train_batches%10==0):
#print(train_batches)
#print (batch_error)
train_err_list.append(train_err)
val_err = 0
val_batches = 0
for batch in iterate_minibatches(X_val, y_val, 50, shuffle=False):
inputs, targets = batch
err = val_fn(inputs, targets)
val_err += err
val_batches += 1
#print (err,val_batches)
val_err_list.append(val_err)
# Save results of the epoch
if (epoch%1 == 0):
file_name = 'model_epoch_' + str(epoch) + '.npz'
np.savez(file_name,*lasagne.layers.get_all_param_values(network))
print ('Epoch: ',epoch, ' train error: ',train_err,' val erro:',val_err)
train_err_line = ('Epoch:,'+str(epoch)+", train_error:, "+str(train_err)+", val_error:, "+str(val_err)+"\n")
file_result = 'C:\\Users\\Administrator\\Desktop\\Input_FUT\\NN_model\\' + str(train_dt) + '\\python runs'
filepath = os.path.join(file_result, 'train_val_err.csv')
fout = open(filepath,"a")
# train_err_file.writelines(train_err_line)
fout.write(train_err_line)
fout.close()
end_time = timeit.default_timer()
print (end_time - start_time)
return val_err_list,train_err_list
def loss(prediction, target):
return aggregate(categorical_crossentropy_logdomain(prediction,target))
def get_network(model):
input_data = tensor.dmatrix('x')
targets_var = tensor.dmatrix('y')
network = layers.InputLayer((model['batch_size'], model['input_vars']),
input_data)
nonlin = nonlinearities.rectify
if model['hidden_nonlinearity'] != 'ReLu':
nonlin = nonlinearities.tanh
prev_layer = network
for l in range(model['nlayers']):
W = None
if model['hidden_nonlinearity'] == 'ReLu':
W = lasagne.init.GlorotUniform('relu')
else:
W = lasagne.init.GlorotUniform(1)
fc = layers.DenseLayer(prev_layer,
model['units'],
nonlinearity=nonlin,
W=W)
if model['dropout']:
fc = layers.DropoutLayer(fc, 0.5)
prev_layer = fc
output_lin = None
if model['output_mode'] == OUTPUT_LOG:
output_lin = nonlinearities.tanh
output_layer = layers.DenseLayer(prev_layer, 1, nonlinearity=output_lin)
predictions = layers.get_output(output_layer)
if model['output_mode'] != OUTPUT_LOG:
(minth, maxth) = model['maxmin'][model['control']]
maxt = theano.shared(np.ones((model['batch_size'], 1)) * maxth)
mint = theano.shared(np.ones((model['batch_size'], 1)) * minth)
predictions = tensor.min(tensor.concatenate([maxt, predictions],
axis=1),
axis=1)
predictions = tensor.reshape(predictions, (model['batch_size'], 1))
predictions = tensor.max(tensor.concatenate([mint, predictions],
axis=1),
axis=1)
predictions = tensor.reshape(predictions, (model['batch_size'], 1))
if model['output_mode'] == OUTPUT_NO:
prediction_unboun = layers.get_output(output_layer)
loss = objectives.squared_error(prediction_unboun, targets_var)
else:
loss = objectives.squared_error(predictions, targets_var)
loss = objectives.aggregate(loss, mode='mean')
params = layers.get_all_params(output_layer)
# test_prediction = layers.get_output(output_layer, deterministic=True) #fix for dropout
test_loss = objectives.squared_error(predictions, targets_var)
test_loss = test_loss.mean()
if model['hidden_nonlinearity'] == 'ReLu':
model['lr'] *= 0.5
updates_sgd = updates.sgd(loss, params, learning_rate=model['lr'])
ups = updates.apply_momentum(updates_sgd, params, momentum=0.9)
train_fn = theano.function([input_data, targets_var], loss, updates=ups)
pred_fn = theano.function([input_data], predictions)
# pred_fn = theano.function([input_data], prediction_unboun)
val_fn = theano.function([input_data, targets_var], test_loss)
return {
'train': train_fn,
'eval': val_fn,
'pred': pred_fn,
'layers': output_layer
}
def build_model(self, train_set, test_set, validation_set=None):
super(UFCNN, self).build_model(train_set, test_set, validation_set)
epsilon = 1e-8
loss_cc = aggregate(categorical_crossentropy(
T.clip(get_output(self.model, self.sym_x), epsilon, 1),
self.sym_t),
mode='mean')
y = T.clip(get_output(self.model, self.sym_x, deterministic=True),
epsilon, 1)
loss_eval = aggregate(categorical_crossentropy(y, self.sym_t),
mode='mean')
loss_acc = categorical_accuracy(y, self.sym_t).mean()
all_params = get_all_params(self.model, trainable=True)
grads = T.grad(loss_cc, all_params)
for idx, param in enumerate(all_params):
param_name = param.name
if ('h2.W' in param_name) or ('g2.W' in param_name):
print(param_name)
grads[idx] *= self.l2_mask
if ('h3.W' in param_name) or ('g3.W' in param_name):
print(param_name)
grads[idx] *= self.l3_mask
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
updates = adam(grads, all_params, self.sym_lr, sym_beta1, sym_beta2)
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
f_train = theano.function(
inputs,
[loss_cc],
updates=updates,
givens={
self.sym_x: self.sh_train_x[self.batch_slice],
self.sym_t: self.sh_train_t[self.batch_slice],
},
)
f_test = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_eval],
givens={
self.sym_x: self.sh_test_x[self.batch_slice],
self.sym_t: self.sh_test_t[self.batch_slice],
},
)
f_validate = None
if validation_set is not None:
f_validate = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_eval, loss_acc],
givens={
self.sym_x: self.sh_valid_x[self.batch_slice],
self.sym_t: self.sh_valid_t[self.batch_slice],
},
)
self.train_args['inputs']['batchsize'] = 128
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 0.999
self.train_args['outputs']['loss_cc'] = '%0.6f'
self.test_args['inputs']['batchsize'] = 128
self.test_args['outputs']['loss_eval'] = '%0.6f'
self.validate_args['inputs']['batchsize'] = 128
self.validate_args['outputs']['loss_eval'] = '%0.6f'
self.validate_args['outputs']['loss_acc'] = '%0.6f%%'
return f_train, f_test, f_validate, self.train_args, self.test_args, self.validate_args
def build_model(self, train_set, test_set, validation_set=None):
super(FCAE, self).build_model(train_set, test_set, validation_set)
y_train = get_output(self.model, self.sym_x)
loss = aggregate(squared_error(y_train, self.sym_x), mode='mean')
# loss += + 1e-4 * lasagne.regularization.regularize_network_params(self.model, lasagne.regularization.l2)
y_test = get_output(self.model, self.sym_x, deterministic=True)
loss_test = aggregate(squared_error(y_test, self.sym_x), mode='mean')
all_params = get_all_params(self.model, trainable=True)
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
grads = T.grad(loss, all_params)
ngrads = lasagne.updates.total_norm_constraint(grads, 5)
cgrads = [T.clip(g, -5, 5) for g in ngrads]
updates = rmsprop(cgrads, all_params, self.sym_lr, sym_beta1,
sym_beta2)
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
f_train = theano.function(
inputs,
[loss],
updates=updates,
givens={
self.sym_x: self.sh_train_x[self.batch_slice],
},
)
f_test = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_test],
givens={
self.sym_x: self.sh_test_x[self.batch_slice],
},
on_unused_input='ignore',
)
f_ae = None
# f_ae = theano.function(
# [self.sym_batchsize], [y_test],
# givens={
# self.sym_x: self.sh_valid_x,
# },
# on_unused_input='ignore',
# )
self.train_args['inputs']['batchsize'] = 128
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 1e-6
self.train_args['outputs']['loss'] = '%0.6f'
self.test_args['inputs']['batchsize'] = 128
self.test_args['outputs']['loss_test'] = '%0.6f'
# self.validate_args['inputs']['batchsize'] = 128
# self.validate_args['outputs']['loss_eval'] = '%0.6f'
# self.validate_args['outputs']['loss_acc'] = '%0.6f'
return f_train, f_test, f_ae, self.train_args, self.test_args, self.validate_args
def msq_err(self, train_output, target_values):
loss = squared_error(train_output, target_values)
loss = aggregate(loss, mode='mean')
return loss
def do_regression(
num_epochs=60, # No. of epochs to train
init_file=None, # Saved parameters to initialise training
epoch_size=680780, # Whole dataset size
valid_size=34848,
train_batch_multiple=10637, # No. of minibatches per batch
valid_batch_multiple=1089, # No. of minibatches per batch
train_minibatch_size=64,
valid_minibatch_size=32,
eval_multiple=50, # No. of minibatches to ave. in report
save_model=True,
input_width=19,
rng_seed=100009,
cross_val=0, # Cross-validation subset label
dataver=1, # Label for different runs/architectures/etc
rate_init=1.0,
rate_decay=0.999983):
###################################################
################# 0. User inputs ##################
###################################################
for i in range(1, len(sys.argv)):
if sys.argv[i].startswith('-'):
option = sys.argv[i][1:]
if option == 'i': init_file = sys.argv[i + 1]
elif option[0:2] == 'v=': dataver = int(option[2:])
elif option[0:3] == 'cv=': cross_val = int(option[3:])
elif option[0:3] == 'rs=': rng_seed = int(option[3:])
elif option[0:3] == 'ri=': rate_init = np.float32(option[3:])
elif option[0:3] == 'rd=': rate_decay = np.float32(option[3:])
print("Running with dataver %s" % (dataver))
print("Running with cross_val %s" % (cross_val))
###################################################
############# 1. Housekeeping values ##############
###################################################
# Batch size is possibly not equal to epoch size due to memory limits
train_batch_size = train_batch_multiple * train_minibatch_size
assert epoch_size >= train_batch_size
# Number of times we expect the training/validation generator to be called
max_train_gen_calls = (num_epochs * epoch_size) // train_batch_size
# Number of evaluations (total minibatches / eval_multiple)
num_eval = max_train_gen_calls * train_batch_multiple / eval_multiple
###################################################
###### 2. Define model and theano variables #######
###################################################
if rng_seed is not None:
print("Setting RandomState with seed=%i" % (rng_seed))
rng = np.random.RandomState(rng_seed)
set_rng(rng)
print("Defining variables...")
index = T.lscalar() # Minibatch index
x = T.tensor3('x') # Inputs
y = T.fvector('y') # Target
print("Defining model...")
network_0 = build_1Dregression_v1(input_var=x,
input_width=input_width,
nin_units=12,
h_num_units=[64, 128, 256, 128, 64],
h_grad_clip=1.0,
output_width=1)
if init_file is not None:
print("Loading initial model parametrs...")
init_model = np.load(init_file)
init_params = init_model[init_model.files[0]]
LL.set_all_param_values([network_0], init_params)
###################################################
################ 3. Import data ###################
###################################################
# Loading data generation model parameters
print("Defining shared variables...")
train_set_y = theano.shared(np.zeros(1, dtype=theano.config.floatX),
borrow=True)
train_set_x = theano.shared(np.zeros((1, 1, 1),
dtype=theano.config.floatX),
borrow=True)
valid_set_y = theano.shared(np.zeros(1, dtype=theano.config.floatX),
borrow=True)
valid_set_x = theano.shared(np.zeros((1, 1, 1),
dtype=theano.config.floatX),
borrow=True)
# Validation data (pick a single augmented instance, rand0 here)
print("Creating validation data...")
chunk_valid_data = np.load(
"./valid/data_valid_augmented_cv%s_t%s_rand0.npy" %
(cross_val, input_width)).astype(theano.config.floatX)
chunk_valid_answers = np.load("./valid/data_valid_expected_cv%s.npy" %
(cross_val)).astype(theano.config.floatX)
print "chunk_valid_answers.shape", chunk_valid_answers.shape
print("Assigning validation data...")
valid_set_y.set_value(chunk_valid_answers[:])
valid_set_x.set_value(chunk_valid_data.transpose(0, 2, 1))
# Create output directory
if not os.path.exists("output_cv%s_v%s" % (cross_val, dataver)):
os.makedirs("output_cv%s_v%s" % (cross_val, dataver))
###################################################
########### 4. Create Loss expressions ############
###################################################
print("Defining loss expressions...")
prediction_0 = LL.get_output(network_0)
train_loss = aggregate(T.abs_(prediction_0 - y.dimshuffle(0, 'x')))
valid_prediction_0 = LL.get_output(network_0, deterministic=True)
valid_loss = aggregate(T.abs_(valid_prediction_0 - y.dimshuffle(0, 'x')))
###################################################
############ 5. Define update method #############
###################################################
print("Defining update choices...")
params = LL.get_all_params(network_0, trainable=True)
learn_rate = T.scalar('learn_rate', dtype=theano.config.floatX)
updates = lasagne.updates.adadelta(train_loss,
params,
learning_rate=learn_rate)
###################################################
######### 6. Define train/valid functions #########
###################################################
print("Defining theano functions...")
train_model = theano.function(
[index, learn_rate],
train_loss,
updates=updates,
givens={
x:
train_set_x[(index * train_minibatch_size):((index + 1) *
train_minibatch_size)],
y:
train_set_y[(index * train_minibatch_size):((index + 1) *
train_minibatch_size)]
})
validate_model = theano.function(
[index],
valid_loss,
givens={
x:
valid_set_x[index * valid_minibatch_size:(index + 1) *
valid_minibatch_size],
y:
valid_set_y[index * valid_minibatch_size:(index + 1) *
valid_minibatch_size]
})
###################################################
################ 7. Begin training ################
###################################################
print("Begin training...")
sys.stdout.flush()
cum_iterations = 0
this_train_loss = 0.0
this_valid_loss = 0.0
best_valid_loss = np.inf
best_iter = 0
train_eval_scores = np.empty(num_eval)
valid_eval_scores = np.empty(num_eval)
eval_index = 0
aug_index = 0
for batch in xrange(max_train_gen_calls):
start_time = time.time()
chunk_train_data = np.load(
"./train/data_train_augmented_cv%s_t%s_rand%s.npy" %
(cross_val, input_width, aug_index)).astype(theano.config.floatX)
chunk_train_answers = np.load("./train/data_train_expected_cv%s.npy" %
(cross_val)).astype(theano.config.floatX)
train_set_y.set_value(chunk_train_answers[:])
train_set_x.set_value(chunk_train_data.transpose(0, 2, 1))
# Iterate over minibatches in each batch
for mini_index in xrange(train_batch_multiple):
this_rate = np.float32(rate_init * (rate_decay**cum_iterations))
this_train_loss += train_model(mini_index, this_rate)
cum_iterations += 1
# Report loss
if (cum_iterations % eval_multiple == 0):
this_train_loss = this_train_loss / eval_multiple
this_valid_loss = np.mean(
[validate_model(i) for i in xrange(valid_batch_multiple)])
train_eval_scores[eval_index] = this_train_loss
valid_eval_scores[eval_index] = this_valid_loss
# Save report every five evaluations
if ((eval_index + 1) % 5 == 0):
np.savetxt("output_cv%s_v%s/training_scores.txt" %
(cross_val, dataver),
train_eval_scores,
fmt="%.5f")
np.savetxt("output_cv%s_v%s/validation_scores.txt" %
(cross_val, dataver),
valid_eval_scores,
fmt="%.5f")
np.savetxt("output_cv%s_v%s/last_learn_rate.txt" %
(cross_val, dataver), [np.array(this_rate)],
fmt="%.5f")
# Save model if best validation score
if (this_valid_loss < best_valid_loss):
best_valid_loss = this_valid_loss
best_iter = cum_iterations - 1
if save_model:
np.savez(
"output_cv%s_v%s/model.npz" % (cross_val, dataver),
LL.get_all_param_values(network_0))
# Reset evaluation reports
eval_index += 1
this_train_loss = 0.0
this_valid_loss = 0.0
aug_index += 1
end_time = time.time()
print("Computing time for batch %d: %f" %
(batch, end_time - start_time))
print("Best validation loss %f after %d epochs" %
(best_valid_loss, (best_iter * train_minibatch_size // epoch_size)))
del train_set_x, train_set_y, valid_set_x, valid_set_y
gc.collect()
return None
def test_aggregate_weighted_normalized_sum():
from lasagne.objectives import aggregate
x = theano.tensor.matrix('x')
w = theano.tensor.matrix('w')
assert theano.gof.graph.is_same_graph(aggregate(x, w, 'normalized_sum'),
(x * w).sum() / w.sum())
def build_model(self,
train_set,
test_set,
validation_set=None,
weights=None):
super(BRNN, self).build_model(train_set, test_set, validation_set)
def brier_score(given, predicted, weight_vector, mask):
return T.mean(
T.power(given - predicted, 2.0).dot(weight_vector) * mask)
epsilon = 1e-8
mask = get_output(self.mask, self.sym_x)
y_train = T.clip(get_output(self.model, self.sym_x), epsilon, 1)
train_brier = brier_score(y_train, self.sym_t, weights, mask)
train_cc = aggregate(categorical_crossentropy(y_train, self.sym_t),
mode='mean')
loss_train_acc = categorical_accuracy(y_train, self.sym_t).mean()
y_test = T.clip(get_output(self.model, self.sym_x, deterministic=True),
epsilon, 1)
test_brier = brier_score(y_test, self.sym_t, weights, mask)
test_cc = aggregate(categorical_crossentropy(y_test, self.sym_t),
mode='mean')
test_acc = categorical_accuracy(y_test, self.sym_t).mean()
all_params = get_all_params(self.model, trainable=True)
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
grads = T.grad(train_brier, all_params)
grads = [T.clip(g, -1, 1) for g in grads]
updates = adam(grads, all_params, self.sym_lr, sym_beta1, sym_beta2)
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
f_train = theano.function(
inputs,
[train_cc, train_brier],
updates=updates,
givens={
self.sym_x: self.sh_train_x[self.batch_slice],
self.sym_t: self.sh_train_t[self.batch_slice],
},
)
f_test = theano.function(
[],
[test_cc, test_brier],
givens={
self.sym_x: self.sh_test_x,
self.sym_t: self.sh_test_t,
},
)
f_validate = None
if validation_set is not None:
f_validate = theano.function(
[self.sym_index, self.sym_batchsize],
[test_cc, test_acc],
givens={
self.sym_x: self.sh_valid_x[self.batch_slice],
self.sym_t: self.sh_valid_t[self.batch_slice],
},
)
predict = theano.function([self.sym_x], [y_test])
self.train_args['inputs']['batchsize'] = 64
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 0.999 # 1e-6
self.train_args['outputs']['train_cc'] = '%0.4f'
# self.train_args['outputs']['train_acc'] = '%0.4f'
self.train_args['outputs']['train_brier'] = '%0.4f'
# self.test_args['inputs']['batchsize'] = 64
self.test_args['outputs']['test_cc'] = '%0.4f'
# self.test_args['outputs']['test_acc'] = '%0.4f'
self.test_args['outputs']['test_brier'] = '%0.4f'
# self.validate_args['inputs']['batchsize'] = 64
# self.validate_args['outputs']['loss_eval'] = '%0.6f'
# self.validate_args['outputs']['test_acc'] = '%0.6f'
return f_train, f_test, f_validate, self.train_args, self.test_args, self.validate_args, predict
def __init__(self, hidden_size=100, nclasses=73, num_embeddings=11359, embedding_dim=100, window_size=1,
memory_size=40, n_memory_slots=8, go_code=1, depth=2, load_dir=None):
articles, titles = T.imatrices('articles', 'titles')
n_article_slots = int(n_memory_slots / 2) # TODO derive this from an arg
n_title_slots = n_memory_slots - n_article_slots
n_instances = articles.shape[0]
self.window_size = window_size
randoms = {
# attr: shape
# 'emb': (num_embeddings + 1, embedding_dim),
'M_a': (memory_size, n_article_slots),
'M_t': (memory_size, n_title_slots),
'w_a': (n_article_slots,),
'w_t': (n_title_slots,),
'Wg_a': (window_size * embedding_dim, n_article_slots),
'Wg_t': (window_size * embedding_dim, n_title_slots),
'Wk': (hidden_size, memory_size),
'Wb': (hidden_size, 1),
'Wv': (hidden_size, memory_size),
'We_a': (hidden_size, n_article_slots),
'We_t': (hidden_size, n_title_slots),
'Wx': (window_size * embedding_dim, hidden_size),
'Wh': (memory_size, hidden_size),
'W': (hidden_size, nclasses),
'h0': hidden_size
}
zeros = {
# attr: shape
'bg_a': n_article_slots,
'bg_t': n_title_slots,
'bk': memory_size,
'bb': 1,
'bv': memory_size,
'be_a': n_article_slots,
'be_t': n_title_slots,
'bh': hidden_size,
'b': nclasses,
}
for l in range(depth):
randoms['gru' + str(l)] = (1, embedding_dim)
def random_shared(name):
shape = randoms[name]
return theano.shared(
0.2 * np.random.normal(size=shape).astype(theano.config.floatX),
name=name)
def zeros_shared(name):
shape = zeros[name]
return theano.shared(np.zeros(shape, dtype=theano.config.floatX), name=name)
for key in randoms:
# create an attribute with associated shape and random values
setattr(self, key, random_shared(key))
for key in zeros:
# create an attribute with associated shape and values equal to 0
setattr(self, key, zeros_shared(key))
self.names = randoms.keys() + zeros.keys()
# self.names.remove('emb') # no need to save or update embeddings
scan_vars = 'h0 w_a M_a w_t M_t'.split()
def repeat_for_each_instance(param):
""" repeat param along new axis once for each instance """
return T.repeat(T.shape_padleft(param), repeats=n_instances, axis=0)
for key in scan_vars:
setattr(self, key, repeat_for_each_instance(self.__getattribute__(key)))
self.names.remove(key)
if load_dir is not None:
with open(os.path.join(load_dir, 'params.pkl')) as handle:
params = pickle.load(handle)
self.__dict__.update(params)
def recurrence(i,
h_tm1,
w_a,
M_a,
*args,
**kwargs):
"""
notes
Headers from paper in all caps
mem = n_article slots if is_article else n_title_slots
:param i: center index of sliding window
:param h_tm1: h_{t-1} (hidden state)
:param w_a: attention weights for article memory
:param M_a: article memory
:param args: gru_weights, maybe w_t, maybe M_t
gru_weights: weights with which to initialize GRULayer on each time step
w_t: attention weights for titles memory
M_t: titles memory
:param kwargs: is_training, is_article
is_training:
is_article: we use different parts of memory when working with a article
:return: [y = model outputs,
i + 1 = increment index,
h w, M (see above)]
"""
is_training = kwargs['is_training']
is_article = kwargs['is_article']
gru_weights = args[:depth]
if len(args) > depth:
w_t = args[depth]
M_t = args[depth + 1]
i_type = T.iscalar if is_article or is_training else T.ivector
assert i.type == i_type
if not is_article:
assert w_t is not None and M_t is not None
word_idxs = i
if is_article or is_training:
# get representation of word window
document = articles if is_article else titles # [instances, bucket_width]
word_idxs = document[:, i:i+1] # [instances, 1]
# x_i = self.emb[word_idxs].flatten(ndim=2) # [instances, embedding_dim]
input = InputLayer(shape=(None, 1),
input_var=word_idxs)
embed = EmbeddingLayer(input, num_embeddings, embedding_dim)
gru = GRULayer(incoming=embed, num_units=embedding_dim, hid_init=self.gru0)
for weight in gru_weights:
gru = GRULayer(incoming=gru, num_units=embedding_dim,
hid_init=weight)
x_i = get_output(gru).flatten(ndim=2)
x_i = Print('x_i')(x_i) # [instances, embedding_dim]
gru_weights = []
if is_article:
M_read = M_a # [instances, memory_size, n_article_slots]
w_read = w_a # [instances, n_article_slots]
else:
M_read = T.concatenate([M_a, M_t], axis=2) # [instances, memory_size, n_title_slots]
w_read = T.concatenate([w_a, w_t], axis=1) # [instances, n_title_slots]
# eqn 15
c = T.batched_dot(M_read, w_read) # [instances, memory_size]
# EXTERNAL MEMORY READ
def get_attention(Wg, bg, M, w):
g = T.nnet.sigmoid(T.dot(x_i, Wg) + bg) # [instances, mem]
# eqn 11
k = T.dot(h_tm1, self.Wk) + self.bk # [instances, memory_size]
# eqn 13
beta = T.dot(h_tm1, self.Wb) + self.bb
beta = T.nnet.softplus(beta)
beta = T.addbroadcast(beta, 1) # [instances, 1]
# eqn 12
w_hat = T.nnet.softmax(beta * cosine_dist(M, k))
# eqn 14
return (1 - g) * w + g * w_hat # [instances, mem]
w_a = get_attention(self.Wg_a, self.bg_a, M_a, w_a) # [instances, n_article_slots]
if not is_article:
w_t = get_attention(self.Wg_t, self.bg_t, M_t, w_t) # [instances, n_title_slots]
# MODEL INPUT AND OUTPUT
# eqn 9
h = T.dot(c, self.Wh) + T.dot(x_i, self.Wx) + self.bh # [instances, hidden_size]
# eqn 10
y = T.nnet.softmax(T.dot(h, self.W) + self.b) # [instances, nclasses]
# EXTERNAL MEMORY UPDATE
def update_memory(We, be, w_update, M_update):
# eqn 17
e = T.nnet.sigmoid(T.dot(h_tm1, We) + be) # [instances, mem]
f = 1. - w_update * e # [instances, mem]
# eqn 16
v = T.tanh(T.dot(h, self.Wv) + self.bv) # [instances, memory_size]
# need to add broadcast layers for memory update
f = f.dimshuffle(0, 'x', 1) # [instances, 1, mem]
u = w_update.dimshuffle(0, 'x', 1) # [instances, 1, mem]
v = v.dimshuffle(0, 1, 'x') # [instances, memory_size, 1]
# eqn 19
return M_update * f + T.batched_dot(v, u) * (1 - f) # [instances, memory_size, mem]
M_a = update_memory(self.We_a, self.be_a, w_a, M_a)
attention_and_memory = [w_a, M_a]
if not is_article:
M_t = update_memory(self.We_t, self.be_t, w_t, M_t)
attention_and_memory += [w_t, M_t]
y_max = y.argmax(axis=1).astype(int32)
next_idxs = i + 1 if is_training or is_article else y_max
return [y, y_max, next_idxs, h] + attention_and_memory
read_article = partial(recurrence, is_training=True, is_article=True)
# for read_article, it actually doesn't matter whether is_training is true
i0 = T.constant(0, dtype=int32, name='first_value_of_i')
gru_weights = [eval('self.gru' + str(l)) for l in range(depth)]
outputs_info = [None, None, i0, self.h0, self.w_a, self.M_a] + gru_weights
[_, _, _, h, w, M], _ = theano.scan(fn=read_article,
outputs_info=outputs_info,
n_steps=articles.shape[1],
name='read_scan')
produce_title = partial(recurrence, is_training=True, is_article=False)
outputs_info[3:6] = [param[-1, :, :] for param in (h, w, M)]
outputs_info.extend([self.w_t, self.M_t])
bucket_width = titles.shape[1] - 1 # subtract 1 because <go> is omitted in y_true
[y, y_max, _, _, _, _, _, _], _ = theano.scan(fn=produce_title,
outputs_info=outputs_info,
n_steps=bucket_width,
name='train_scan')
# loss and updates
y_clip = T.clip(y, .01, .99)
y_flatten = y_clip.dimshuffle(2, 1, 0).flatten(ndim=2).T
y_true = titles[:, 1:].ravel() # [:, 1:] in order to omit <go>
counts = T.extra_ops.bincount(y_true, assert_nonneg=True)
weights = 1.0 / (counts[y_true] + 1) * T.neq(y_true, 0)
losses = T.nnet.categorical_crossentropy(y_flatten, y_true)
loss = objectives.aggregate(losses, weights, mode='sum')
updates = adadelta(loss, self.params())
self.learn = theano.function(inputs=[articles, titles],
outputs=[y_max.T, loss],
updates=updates,
allow_input_downcast=True,
name='learn')
produce_title_test = partial(recurrence, is_training=False, is_article=False)
self.test = theano.function(inputs=[articles, titles],
outputs=[y_max.T],
on_unused_input='ignore')
outputs_info[2] = T.zeros([n_instances], dtype=int32) + go_code
[_, y_max, _, _, _, _, _, _], _ = theano.scan(fn=produce_title_test,
outputs_info=outputs_info,
n_steps=bucket_width,
name='test_scan')
self.predict = theano.function(inputs=[articles, titles],
outputs=y_max.T,
name='infer')
def build_model(self, train_set, test_set, validation_set=None):
super(CNN, self).build_model(train_set, test_set, validation_set)
epsilon = 1e-8
y_train = T.clip(get_output(self.model, self.sym_x), epsilon, 1)
loss_cc = aggregate(categorical_crossentropy(y_train, self.sym_t),
mode='mean')
loss_train_acc = categorical_accuracy(y_train, self.sym_t).mean()
y = T.clip(get_output(self.model, self.sym_x, deterministic=True),
epsilon, 1)
loss_eval = aggregate(categorical_crossentropy(y, self.sym_t),
mode='mean')
loss_acc = categorical_accuracy(y, self.sym_t).mean()
all_params = get_all_params(self.model, trainable=True)
sym_beta1 = T.scalar('beta1')
sym_beta2 = T.scalar('beta2')
grads = T.grad(loss_cc, all_params)
grads = [T.clip(g, -5, 5) for g in grads]
updates = rmsprop(grads, all_params, self.sym_lr, sym_beta1, sym_beta2)
inputs = [
self.sym_index, self.sym_batchsize, self.sym_lr, sym_beta1,
sym_beta2
]
f_train = theano.function(
inputs,
[loss_cc, loss_train_acc],
updates=updates,
givens={
self.sym_x: self.sh_train_x[self.batch_slice],
self.sym_t: self.sh_train_t[self.batch_slice],
},
)
f_test = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_eval, loss_acc],
givens={
self.sym_x: self.sh_test_x[self.batch_slice],
self.sym_t: self.sh_test_t[self.batch_slice],
},
)
f_validate = None
if validation_set is not None:
f_validate = theano.function(
[self.sym_index, self.sym_batchsize],
[loss_eval, loss_acc],
givens={
self.sym_x: self.sh_valid_x[self.batch_slice],
self.sym_t: self.sh_valid_t[self.batch_slice],
},
)
self.train_args['inputs']['batchsize'] = 128
self.train_args['inputs']['learningrate'] = 1e-3
self.train_args['inputs']['beta1'] = 0.9
self.train_args['inputs']['beta2'] = 0.999
self.train_args['outputs']['loss_cc'] = '%0.6f'
self.train_args['outputs']['loss_train_acc'] = '%0.6f'
self.test_args['inputs']['batchsize'] = 128
self.test_args['outputs']['loss_eval'] = '%0.6f'
self.test_args['outputs']['loss_acc'] = '%0.6f'
self.validate_args['inputs']['batchsize'] = 128
# self.validate_args['outputs']['loss_eval'] = '%0.6f'
# self.validate_args['outputs']['loss_acc'] = '%0.6f'
return f_train, f_test, f_validate, self.train_args, self.test_args, self.validate_args
def build_update_functions(train_set_x, train_set_y,
valid_set_x, valid_set_y,
network,
y, X,
train_MASK, val_MASK,
batch_size=32,
l2_reg=.0001):
# build update functions
# extract tensor representing the network predictions
prediction = get_output(network)
###################New#########################
# Aggregate the element-wise error into a scalar value using a mask
# note that y should note contain NAN, replace them with 0 or -1. The value does not matter. It
# is not used to calculate the aggregated error and update of the network.
# MASK should be a matrix of size(y), with 0s in place of NaN values and 1s everywhere else.
# build tensor variable for mask
trainMASK = T.matrix('trainMASK')
# collect squared error
loss_RMSE = squared_error(prediction, y)
# Drop nan values and average over the remaining values
loss_RMSE = aggregate(loss_RMSE, weights=trainMASK, mode='normalized_sum')
# compute the square root
loss_RMSE = loss_RMSE.sqrt()
###############################################
# add l2 regularization
# l2_penalty = regularize_layer_params(network, l2)
# regc = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1]
# layers = get_all_layers(network)
# reg_weights = {key: value for (key, value) in zip(layers, regc)}
# l2_penalty = regularize_layer_params_weighted(reg_weights, l2)
loss = loss_RMSE#(1 - l2_reg) * loss_RMSE + l2_reg * l2_penalty
# get network params
params = get_all_params(network)
# subset_params = params
#subset network params to extract the ones that you want to train
# print 'length of params',len(params), '\n'
subset_params = [params[0], params[1], params[10], params[11], params[12], params[13]]
#
print('RMSPROP \n')
updates = rmsprop(loss, subset_params, learning_rate=1e-4)
# create validation/test loss expression
# the loss represents the loss for all the labels
test_prediction = get_output(network, deterministic=True)
###################New#########################
# Aggregate the element-wise error into a scalar value using a mask
# note that y should note contain NAN, replace them with 0 or -1. The value does not matter. It
# is not used to calculate the aggregated error and update of the network.
# MASK should be a matrix of size(y), with 0s in place of NaN values and 1s everywhere else.
# build tensor variable for mask
valMASK = T.matrix('valMASK')
# collect squared error
test_loss = squared_error(test_prediction, y)
# Drop nan values and average over the remaining values
test_loss = aggregate(test_loss, weights=valMASK, mode='normalized_sum')
# compute the square root
test_loss = test_loss.sqrt()
################################################
# index for mini-batch slicing
index = T.lscalar()
# training function
train_set_x_size = train_set_x.get_value().shape[0]
val_set_x_size = valid_set_x.get_value().shape[0]
train_fn = theano.function(inputs=[index],
outputs=[loss, loss_RMSE],
updates=updates,
givens={X: train_set_x[
index * batch_size: T.minimum((index + 1) * batch_size, train_set_x_size)],
y: train_set_y[
index * batch_size: T.minimum((index + 1) * batch_size, train_set_x_size)],
trainMASK: train_MASK[index * batch_size: T.minimum((index + 1) * batch_size,
train_set_x_size)]})
# validation function
val_fn = theano.function(inputs=[index],
outputs=[test_loss, prediction],
givens={X: valid_set_x[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)],
y: valid_set_y[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)],
valMASK: val_MASK[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)]})
return train_fn, val_fn
def __init__(self,
hidden_size=100,
nclasses=73,
num_embeddings=11359,
embedding_dim=100,
window_size=1,
memory_size=40,
n_memory_slots=8,
go_code=1,
depth=2,
load_dir=None):
articles, titles = T.imatrices('articles', 'titles')
n_article_slots = int(n_memory_slots /
2) # TODO derive this from an arg
n_title_slots = n_memory_slots - n_article_slots
n_instances = articles.shape[0]
self.window_size = window_size
randoms = {
# attr: shape
# 'emb': (num_embeddings + 1, embedding_dim),
'M_a': (memory_size, n_article_slots),
'M_t': (memory_size, n_title_slots),
'w_a': (n_article_slots, ),
'w_t': (n_title_slots, ),
'Wg_a': (window_size * embedding_dim, n_article_slots),
'Wg_t': (window_size * embedding_dim, n_title_slots),
'Wk': (hidden_size, memory_size),
'Wb': (hidden_size, 1),
'Wv': (hidden_size, memory_size),
'We_a': (hidden_size, n_article_slots),
'We_t': (hidden_size, n_title_slots),
'Wx': (window_size * embedding_dim, hidden_size),
'Wh': (memory_size, hidden_size),
'W': (hidden_size, nclasses),
'h0': hidden_size
}
zeros = {
# attr: shape
'bg_a': n_article_slots,
'bg_t': n_title_slots,
'bk': memory_size,
'bb': 1,
'bv': memory_size,
'be_a': n_article_slots,
'be_t': n_title_slots,
'bh': hidden_size,
'b': nclasses,
}
for l in range(depth):
randoms['gru' + str(l)] = (1, embedding_dim)
def random_shared(name):
shape = randoms[name]
return theano.shared(
0.2 *
np.random.normal(size=shape).astype(theano.config.floatX),
name=name)
def zeros_shared(name):
shape = zeros[name]
return theano.shared(np.zeros(shape, dtype=theano.config.floatX),
name=name)
for key in randoms:
# create an attribute with associated shape and random values
setattr(self, key, random_shared(key))
for key in zeros:
# create an attribute with associated shape and values equal to 0
setattr(self, key, zeros_shared(key))
self.names = randoms.keys() + zeros.keys()
# self.names.remove('emb') # no need to save or update embeddings
scan_vars = 'h0 w_a M_a w_t M_t'.split()
def repeat_for_each_instance(param):
""" repeat param along new axis once for each instance """
return T.repeat(T.shape_padleft(param),
repeats=n_instances,
axis=0)
for key in scan_vars:
setattr(self, key,
repeat_for_each_instance(self.__getattribute__(key)))
self.names.remove(key)
if load_dir is not None:
with open(os.path.join(load_dir, 'params.pkl')) as handle:
params = pickle.load(handle)
self.__dict__.update(params)
def recurrence(i, h_tm1, w_a, M_a, *args, **kwargs):
"""
notes
Headers from paper in all caps
mem = n_article slots if is_article else n_title_slots
:param i: center index of sliding window
:param h_tm1: h_{t-1} (hidden state)
:param w_a: attention weights for article memory
:param M_a: article memory
:param args: gru_weights, maybe w_t, maybe M_t
gru_weights: weights with which to initialize GRULayer on each time step
w_t: attention weights for titles memory
M_t: titles memory
:param kwargs: is_training, is_article
is_training:
is_article: we use different parts of memory when working with a article
:return: [y = model outputs,
i + 1 = increment index,
h w, M (see above)]
"""
is_training = kwargs['is_training']
is_article = kwargs['is_article']
gru_weights = args[:depth]
if len(args) > depth:
w_t = args[depth]
M_t = args[depth + 1]
i_type = T.iscalar if is_article or is_training else T.ivector
assert i.type == i_type
if not is_article:
assert w_t is not None and M_t is not None
word_idxs = i
if is_article or is_training:
# get representation of word window
document = articles if is_article else titles # [instances, bucket_width]
word_idxs = document[:, i:i + 1] # [instances, 1]
# x_i = self.emb[word_idxs].flatten(ndim=2) # [instances, embedding_dim]
input = InputLayer(shape=(None, 1), input_var=word_idxs)
embed = EmbeddingLayer(input, num_embeddings, embedding_dim)
gru = GRULayer(incoming=embed,
num_units=embedding_dim,
hid_init=self.gru0)
for weight in gru_weights:
gru = GRULayer(incoming=gru,
num_units=embedding_dim,
hid_init=weight)
x_i = get_output(gru).flatten(ndim=2)
x_i = Print('x_i')(x_i) # [instances, embedding_dim]
gru_weights = []
if is_article:
M_read = M_a # [instances, memory_size, n_article_slots]
w_read = w_a # [instances, n_article_slots]
else:
M_read = T.concatenate(
[M_a, M_t],
axis=2) # [instances, memory_size, n_title_slots]
w_read = T.concatenate([w_a, w_t],
axis=1) # [instances, n_title_slots]
# eqn 15
c = T.batched_dot(M_read, w_read) # [instances, memory_size]
# EXTERNAL MEMORY READ
def get_attention(Wg, bg, M, w):
g = T.nnet.sigmoid(T.dot(x_i, Wg) + bg) # [instances, mem]
# eqn 11
k = T.dot(h_tm1, self.Wk) + self.bk # [instances, memory_size]
# eqn 13
beta = T.dot(h_tm1, self.Wb) + self.bb
beta = T.nnet.softplus(beta)
beta = T.addbroadcast(beta, 1) # [instances, 1]
# eqn 12
w_hat = T.nnet.softmax(beta * cosine_dist(M, k))
# eqn 14
return (1 - g) * w + g * w_hat # [instances, mem]
w_a = get_attention(self.Wg_a, self.bg_a, M_a,
w_a) # [instances, n_article_slots]
if not is_article:
w_t = get_attention(self.Wg_t, self.bg_t, M_t,
w_t) # [instances, n_title_slots]
# MODEL INPUT AND OUTPUT
# eqn 9
h = T.dot(c, self.Wh) + T.dot(
x_i, self.Wx) + self.bh # [instances, hidden_size]
# eqn 10
y = T.nnet.softmax(T.dot(h, self.W) +
self.b) # [instances, nclasses]
# EXTERNAL MEMORY UPDATE
def update_memory(We, be, w_update, M_update):
# eqn 17
e = T.nnet.sigmoid(T.dot(h_tm1, We) + be) # [instances, mem]
f = 1. - w_update * e # [instances, mem]
# eqn 16
v = T.tanh(T.dot(h, self.Wv) +
self.bv) # [instances, memory_size]
# need to add broadcast layers for memory update
f = f.dimshuffle(0, 'x', 1) # [instances, 1, mem]
u = w_update.dimshuffle(0, 'x', 1) # [instances, 1, mem]
v = v.dimshuffle(0, 1, 'x') # [instances, memory_size, 1]
# eqn 19
return M_update * f + T.batched_dot(v, u) * (
1 - f) # [instances, memory_size, mem]
M_a = update_memory(self.We_a, self.be_a, w_a, M_a)
attention_and_memory = [w_a, M_a]
if not is_article:
M_t = update_memory(self.We_t, self.be_t, w_t, M_t)
attention_and_memory += [w_t, M_t]
y_max = y.argmax(axis=1).astype(int32)
next_idxs = i + 1 if is_training or is_article else y_max
return [y, y_max, next_idxs, h] + attention_and_memory
read_article = partial(recurrence, is_training=True, is_article=True)
# for read_article, it actually doesn't matter whether is_training is true
i0 = T.constant(0, dtype=int32, name='first_value_of_i')
gru_weights = [eval('self.gru' + str(l)) for l in range(depth)]
outputs_info = [None, None, i0, self.h0, self.w_a, self.M_a
] + gru_weights
[_, _, _, h, w, M], _ = theano.scan(fn=read_article,
outputs_info=outputs_info,
n_steps=articles.shape[1],
name='read_scan')
produce_title = partial(recurrence, is_training=True, is_article=False)
outputs_info[3:6] = [param[-1, :, :] for param in (h, w, M)]
outputs_info.extend([self.w_t, self.M_t])
bucket_width = titles.shape[
1] - 1 # subtract 1 because <go> is omitted in y_true
[y, y_max, _, _, _, _, _,
_], _ = theano.scan(fn=produce_title,
outputs_info=outputs_info,
n_steps=bucket_width,
name='train_scan')
# loss and updates
y_clip = T.clip(y, .01, .99)
y_flatten = y_clip.dimshuffle(2, 1, 0).flatten(ndim=2).T
y_true = titles[:, 1:].ravel() # [:, 1:] in order to omit <go>
counts = T.extra_ops.bincount(y_true, assert_nonneg=True)
weights = 1.0 / (counts[y_true] + 1) * T.neq(y_true, 0)
losses = T.nnet.categorical_crossentropy(y_flatten, y_true)
loss = objectives.aggregate(losses, weights, mode='sum')
updates = adadelta(loss, self.params())
self.learn = theano.function(inputs=[articles, titles],
outputs=[y_max.T, loss],
updates=updates,
allow_input_downcast=True,
name='learn')
produce_title_test = partial(recurrence,
is_training=False,
is_article=False)
self.test = theano.function(inputs=[articles, titles],
outputs=[y_max.T],
on_unused_input='ignore')
outputs_info[2] = T.zeros([n_instances], dtype=int32) + go_code
[_, y_max, _, _, _, _, _,
_], _ = theano.scan(fn=produce_title_test,
outputs_info=outputs_info,
n_steps=bucket_width,
name='test_scan')
self.predict = theano.function(inputs=[articles, titles],
outputs=y_max.T,
name='infer')
def loss(x, t):
return aggregate(binary_crossentropy(x, t))
"""loading weight values from the previous model"""
"""load layer parameters. To be used only when learning walk forward"""
# with np.load('model.npz') as f:
# param_values = [f['arr_%d'%i] for i in range(len(f.files))]
# lasagne.layers.set_all_param_values(network,param_values)
prediction = lasagne.layers.get_output(network,input_var, deterministic = True)
loss = lasagne.objectives.binary_crossentropy(prediction, target_var)
"""loss function aggregation only to be used when using cost sensitive training"""
# class_weights = np.empty((50,2),dtype=float)
# class_weights_global = class_weights
# loss = aggregate(loss, weights=theano.shared(class_weights_global),mode='normalized_sum')
"""loss function aggregation to be used without cost sensitive training"""
loss = aggregate(loss,mode='mean')
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.sgd(loss, params, learning_rate=0.2)
test_prediction = lasagne.layers.get_output(network,input_var, deterministic=True)
test_loss = lasagne.objectives.binary_crossentropy(test_prediction,target_var)
test_loss = test_loss.mean()
# test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),dtype=theano.config.floatX)
# test_acc = test_acc.mean()
train_fn = theano.function([input_var, target_var],loss, updates=updates)
# val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
val_fn = theano.function([input_var, target_var],test_loss)
"""Training"""
print("Starting training...")
def build_update_functions(train_set_x, train_set_y,
valid_set_x, valid_set_y,
network,
y, X,
train_MASK, val_MASK,
batch_size=32,
l2_reg=.0001,
learning_rate=.005,
momentum=.9):
# build update functions
# extract tensor representing the network predictions
prediction = get_output(network)
################################################
##################old###########################
# # collect squared error
# loss_RMSE = squared_error(prediction, y)
# # compute the root mean squared error
# loss_RMSE = loss_RMSE.mean().sqrt()
###################New#########################
# Aggregate the element-wise error into a scalar value using a mask
# note that y should note contain NAN, replace them with 0 or -1. The value does not matter. It
# is not used to calculate the aggregated error and update of the network.
# MASK should be a matrix of size(y), with 0s in place of NaN values and 1s everywhere else.
# build tensor variable for mask
trainMASK = T.matrix('trainMASK')
# collect squared error
loss_RMSE = squared_error(prediction, y)
# Drop nan values and average over the remaining values
loss_RMSE = aggregate(loss_RMSE, weights=trainMASK, mode='normalized_sum')
# compute the square root
loss_RMSE = loss_RMSE.sqrt()
###############################################
# add l2 regularization
l2_penalty = regularize_network_params(network, l2)
loss = (1 - l2_reg) * loss_RMSE + l2_reg * l2_penalty
# get network params
params = get_all_params(network, trainable = True)
# # create update criterion
# print('nestrov')
# updates = nesterov_momentum( loss, params, learning_rate=.01, momentum=.9)
# print('AdaGrad')
# updates = adagrad(loss, params,learning_rate= 1e-2)
#
print('RMSPROP \n')
updates = rmsprop(loss, params, learning_rate=learning_rate)
# create validation/test loss expression
# the loss represents the loss for all the labels
test_prediction = get_output(network, deterministic=True)
################################################
##################old###########################
# # collect squared error
# test_loss = squared_error(test_prediction,y)
# # compute the root mean squared error
# test_loss = test_loss.mean().sqrt()
# # test_loss_withl2 = (1-l2_reg) * test_loss + l2_reg * l2_penalty
################################################
###################New#########################
# Aggregate the element-wise error into a scalar value using a mask
# note that y should note contain NAN, replace them with 0 or -1. The value does not matter. It
# is not used to calculate the aggregated error and update of the network.
# MASK should be a matrix of size(y), with 0s in place of NaN values and 1s everywhere else.
# build tensor variable for mask
valMASK = T.matrix('valMASK')
# collect squared error
test_loss = squared_error(test_prediction, y)
# Drop nan values and average over the remaining values
test_loss = aggregate(test_loss, weights=valMASK, mode='normalized_sum')
# compute the square root
test_loss = test_loss.sqrt()
################################################
# index for mini-batch slicing
index = T.lscalar()
# training function
train_set_x_size = train_set_x.get_value().shape[0]
val_set_x_size = valid_set_x.get_value().shape[0]
train_fn = theano.function(inputs=[index],
outputs=[loss, loss_RMSE],
updates=updates,
givens={X: train_set_x[
index * batch_size: T.minimum((index + 1) * batch_size, train_set_x_size)],
y: train_set_y[
index * batch_size: T.minimum((index + 1) * batch_size, train_set_x_size)],
trainMASK: train_MASK[index * batch_size: T.minimum((index + 1) * batch_size,
train_set_x_size)]})
# validation function
val_fn = theano.function(inputs=[index],
outputs=[test_loss, prediction],
givens={X: valid_set_x[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)],
y: valid_set_y[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)],
valMASK: val_MASK[
index * batch_size: T.minimum((index + 1) * batch_size, val_set_x_size)]})
return train_fn, val_fn
def loss(x, t):
return LO.aggregate(
LO.categorical_crossentropy(T.clip(x, 1e-6, 1. - 1e-6), t))
def loss(x, t):
return LO.aggregate(LO.categorical_crossentropy(T.clip(x, 1e-6, 1. - 1e-6), t))
def build_model(self):
# Define the model prior to building it
if not hasattr(self, 'network'):
self.model()
self._generate_layer_list()
# Print the model architecture
self.log_msg("Network Architecture")
self.log_msg(self.model_str())
# Training loss
train_prediction = get_output(self.network)
train_loss = aggregate(self.objective(train_prediction,
self.output_var),
mode='mean')
self.log_msg("Objective: {}".format(self.objective.__name__))
# Validation loss
validation_prediction = get_output(self.network, deterministic=True)
validation_loss = aggregate(self.objective(validation_prediction,
self.output_var),
mode='mean')
# Update the parameters
params = get_all_params(self.network, trainable=True)
popts = {
'loss_or_grads': train_loss,
'params': params,
'learning_rate': self.learning_rate_tensor,
'momentum': self.momentum_tensor,
}
# Inspect to see if momentum is a valid argument for the update
update_args = inspect.getargspec(self.updates)[0]
# Remove momentum if not applicable
if not 'momentum' in update_args:
del popts['momentum']
updates = self.updates(**popts)
# Print the learning rate type
self.log_msg('Update: %s' % self.updates.__name__)
self.log_msg("Learning Rate: %s" % self.learning_rate.__name__)
if 'momentum' in popts.keys():
self.log_msg("Momentum: %s" % self.momentum.__name__)
# Define training loss function
self.train_loss = theano.function(
inputs=[self.input_var, self.output_var],
outputs=train_loss,
updates=updates,
allow_input_downcast=True,
)
# Define the accuracy function for categorisation problems
cat_accuracy = T.mean(
T.eq(
T.argmax(validation_prediction, axis=1),
T.argmax(self.output_var, axis=1),
))
# Define validation loss function
if self.objective is squared_error:
self.valid_loss = theano.function(
inputs=[self.input_var, self.output_var],
outputs=validation_loss,
)
else:
self.valid_loss = theano.function(
inputs=[self.input_var, self.output_var],
outputs=[validation_loss, cat_accuracy],
)
# Define predict
self.predict = theano.function(inputs=[self.input_var],
outputs=validation_prediction)
self.train_predict = theano.function(inputs=[self.input_var],
outputs=train_prediction)