def test_apply_penalty(self):
from lasagne.regularization import apply_penalty, l2
A = T.vector()
B = T.matrix()
assert apply_penalty([], l2) == 0
assert equal_computations([apply_penalty(A, l2)], [l2(A)])
assert equal_computations([apply_penalty([A, B], l2)],
[sum([l2(A), l2(B)])])
def regularization(network, optimization): all_params = layers.get_all_params(network, regularizable=True) # weight-decay regularization loss = 0 if "l1" in optimization: l1_penalty = apply_penalty(all_params, l1) * optimization["l1"] loss += l1_penalty if "l2" in optimization: l2_penalty = apply_penalty(all_params, l2)* optimization["l2"] loss += l2_penalty return loss
def test_apply_penalty(self):
from lasagne.regularization import apply_penalty, l2
A = T.vector()
B = T.matrix()
assert apply_penalty([], l2) == 0
assert equal_computations([apply_penalty(A, l2)],
[l2(A)])
assert equal_computations([apply_penalty([A, B], l2)],
[sum([l2(A), l2(B)])])
def test_regularize_layer_params_weighted(self, layers):
from lasagne.regularization import regularize_layer_params_weighted
from lasagne.regularization import apply_penalty, l2
l_1, l_2, l_3 = layers
layers = OrderedDict()
layers[l_2] = 0.1
layers[l_3] = 0.5
loss = regularize_layer_params_weighted(layers,
lasagne.regularization.l2)
assert equal_computations([loss],
[sum([0.1 * apply_penalty([l_2.W], l2),
0.5 * apply_penalty([l_3.W], l2)])])
def prepare():
X = T.tensor4('X')
y = T.ivector('y')
output_layer = lenet_skinny()
all_params = lasagne.layers.get_all_params(output_layer)
loss_fn = x_ent
prediction = lasagne.layers.get_output(output_layer, X)
loss = loss_fn(prediction, y).mean() + \
args["lambda"]*apply_penalty(lasagne.layers.get_all_params(output_layer, regularizable=True), l2 )
label_vector = lasagne.layers.get_output(output_layer, X)
pred = T.argmax(label_vector, axis=1)
accuracy = T.mean(T.eq(pred, y))
return Container({
"X": X,
"y": y,
"output_layer": output_layer,
"all_params": all_params,
"loss": loss,
"label_vector": label_vector,
"pred": pred,
"accuracy": accuracy
})
def build_trainer(input_data,
input_mask,
target_data,
target_mask,
network_params,
network_reg_params,
output_layer,
weight_decay,
updater,
learning_rate,
max_grad_norm=0.0,
load_updater_params=None):
output_score = get_output(output_layer, deterministic=False)
frame_prd_idx = T.argmax(output_score, axis=-1)
one_hot_target = T.extra_ops.to_one_hot(y=T.flatten(target_data, 1),
nb_class=output_dim,
dtype=floatX)
output_score = T.reshape(x=output_score,
newshape=(-1, output_dim),
ndim=2)
output_score = output_score - T.max(output_score, axis=-1, keepdims=True)
output_score = output_score - T.log(T.sum(T.exp(output_score), axis=-1, keepdims=True))
train_ce = -T.sum(T.mul(one_hot_target, output_score), axis=-1)*T.flatten(target_mask, 1)
train_loss = T.sum(train_ce)/target_mask.shape[0]
frame_loss = T.sum(train_ce)/T.sum(target_mask)
frame_accr = T.sum(T.eq(frame_prd_idx, target_data)*target_mask)/T.sum(target_mask)
train_total_loss = train_loss
if weight_decay > 0:
train_total_loss += apply_penalty(network_reg_params, l2)*10**(-weight_decay)
network_grads = theano.grad(cost=train_total_loss, wrt=network_params)
if max_grad_norm > 0.:
network_grads, network_grads_norm = total_norm_constraint(tensor_vars=network_grads,
max_norm=max_grad_norm,
return_norm=True)
else:
network_grads_norm = T.sqrt(sum(T.sum(grad ** 2) for grad in network_grads))
train_lr = theano.shared(lasagne.utils.floatX(learning_rate))
train_updates, updater_params = updater(loss_or_grads=network_grads,
params=network_params,
learning_rate=train_lr,
load_params_dict=load_updater_params)
training_fn = theano.function(inputs=[input_data,
input_mask,
target_data,
target_mask],
outputs=[frame_loss,
frame_accr,
network_grads_norm],
updates=train_updates)
return training_fn, train_lr, updater_params
def test_regularize_layer_params_weighted(self, layers):
from lasagne.regularization import regularize_layer_params_weighted
from lasagne.regularization import apply_penalty, l2
l_1, l_2, l_3 = layers
layers = OrderedDict()
layers[l_2] = 0.1
layers[l_3] = 0.5
loss = regularize_layer_params_weighted(layers,
lasagne.regularization.l2)
assert equal_computations([loss], [
sum([
0.1 * apply_penalty([l_2.W], l2),
0.5 * apply_penalty([l_3.W], l2)
])
])
def execute(dataset,
n_hidden_t_enc,
n_hidden_s,
num_epochs=500,
learning_rate=.001,
learning_rate_annealing=1.0,
gamma=1,
lmd=0.,
disc_nonlinearity="sigmoid",
keep_labels=1.0,
prec_recall_cutoff=True,
missing_labels_val=-1.0,
which_fold=1,
early_stop_criterion='loss',
embedding_input='raw',
save_path='/Tmp/romerosa/feature_selection/',
save_copy='/Tmp/romerosa/feature_selection/',
dataset_path='/Tmp/carriepl/datasets/',
resume=False,
exp_name=None):
# Load the dataset
print("Loading data")
x_train, y_train, x_valid, y_valid, x_test, y_test, \
x_unsup, training_labels = mlh.load_data(
dataset, dataset_path, None,
which_fold=which_fold, keep_labels=keep_labels,
missing_labels_val=missing_labels_val,
embedding_input=embedding_input)
# Extract required information from data
n_samples, n_feats = x_train.shape
print("Number of features : ", n_feats)
print("Glorot init : ", 2.0 / (n_feats + n_hidden_t_enc[-1]))
n_targets = y_train.shape[1]
# Set some variables
batch_size = 1
# Preparing folder to save stuff
print("Experiment: " + exp_name)
save_path = os.path.join(save_path, dataset, exp_name)
save_copy = os.path.join(save_copy, dataset, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
# Prepare Theano variables for inputs and targets
input_var_sup = T.matrix('input_sup')
target_var_sup = T.matrix('target_sup')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
# Build model
print("Building model")
discrim_net = InputLayer((None, n_feats), input_var_sup)
discrim_net = DenseLayer(discrim_net,
num_units=n_hidden_t_enc[-1],
nonlinearity=rectify)
# Reconstruct the input using dec_feat_emb
if gamma > 0:
reconst_net = DenseLayer(discrim_net,
num_units=n_feats,
nonlinearity=linear)
nets = [reconst_net]
else:
nets = [None]
# Add supervised hidden layers
for hid in n_hidden_s:
discrim_net = DropoutLayer(discrim_net)
discrim_net = DenseLayer(discrim_net, num_units=hid)
assert disc_nonlinearity in ["sigmoid", "linear", "rectify", "softmax"]
discrim_net = DropoutLayer(discrim_net)
discrim_net = DenseLayer(discrim_net,
num_units=n_targets,
nonlinearity=eval(disc_nonlinearity))
print("Building and compiling training functions")
# Build and compile training functions
predictions, predictions_det = mh.define_predictions(nets, start=0)
prediction_sup, prediction_sup_det = mh.define_predictions([discrim_net])
prediction_sup = prediction_sup[0]
prediction_sup_det = prediction_sup_det[0]
# Define losses
# reconstruction losses
reconst_losses, reconst_losses_det = mh.define_reconst_losses(
predictions, predictions_det, [input_var_sup])
# supervised loss
sup_loss, sup_loss_det = mh.define_sup_loss(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
keep_labels, target_var_sup,
missing_labels_val)
inputs = [input_var_sup, target_var_sup]
params = lasagne.layers.get_all_params([discrim_net] + nets,
trainable=True)
print('Number of params: ' + str(len(params)))
# Combine losses
loss = sup_loss + gamma * reconst_losses[0]
loss_det = sup_loss_det + gamma * reconst_losses_det[0]
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
loss_det = loss_det + lmd * l2_penalty
# Compute network updates
updates = lasagne.updates.rmsprop(loss, params, learning_rate=lr)
# updates = lasagne.updates.sgd(loss,
# params,
# learning_rate=lr)
# updates = lasagne.updates.momentum(loss, params,
# learning_rate=lr, momentum=0.0)
# Apply norm constraints on the weights
for k in updates.keys():
if updates[k].ndim == 2:
updates[k] = lasagne.updates.norm_constraint(updates[k], 1.0)
# Compile training function
train_fn = theano.function(inputs,
loss,
updates=updates,
on_unused_input='ignore')
# Monitoring Labels
monitor_labels = ["reconst. loss"]
monitor_labels = [
i for i, j in zip(monitor_labels, reconst_losses) if j != 0
]
monitor_labels += ["loss. sup.", "total loss"]
# Build and compile test function
val_outputs = reconst_losses_det
val_outputs = [i for i, j in zip(val_outputs, reconst_losses) if j != 0]
val_outputs += [sup_loss_det, loss_det]
# Compute accuracy and add it to monitoring list
test_acc, test_pred = mh.define_test_functions(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
target_var_sup)
monitor_labels.append("accuracy")
val_outputs.append(test_acc)
# Compile prediction function
predict = theano.function([input_var_sup], test_pred)
# Compile validation function
val_fn = theano.function(inputs, [prediction_sup_det] + val_outputs,
on_unused_input='ignore')
# Finally, launch the training loop.
print("Starting testing...")
if not os.path.exists(save_copy + '/model_feat_sel_best.npz'):
print("No saved model to be tested and/or generate" " the embedding !")
else:
with np.load(save_copy + '/model_feat_sel_best.npz', ) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(
filter(None, nets) + [discrim_net], param_values)
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
batch_size,
shuffle=False)
test_err, pred, targets = mlh.monitoring(test_minibatches,
"test",
val_fn,
monitor_labels,
prec_recall_cutoff,
return_pred=True)
lab = targets.argmax(1)
pred_argmax = pred.argmax(1)
continent_cat = mh.create_1000_genomes_continent_labels()
lab_cont = np.zeros(lab.shape)
pred_cont = np.zeros(pred_argmax.shape)
for i, c in enumerate(continent_cat):
for el in c:
lab_cont[lab == el] = i
pred_cont[pred_argmax == el] = i
cm_e = np.zeros((26, 26))
cm_c = np.zeros((5, 5))
for i in range(26):
for j in range(26):
cm_e[i, j] = ((pred_argmax == i) * (lab == j)).sum()
for i in range(5):
for j in range(5):
cm_c[i, j] = ((pred_cont == i) * (lab_cont == j)).sum()
np.savez(os.path.join(save_copy, 'cm' + str(which_fold) + '.npz'),
cm_e=cm_e,
cm_c=cm_c)
print(os.path.join(save_copy, 'cm' + str(which_fold) + '.npz'))
def __init__(self,
W=None,
W_path=None,
K=300,
num_hidden=256,
batch_size=None,
grad_clip=100.,
max_sent_len_basic=200,
num_classes=2,
**kwargs):
W = W
V = len(W)
K = int(K)
num_hidden = int(num_hidden)
batch_size = int(batch_size)
grad_clip = int(grad_clip)
max_seq_len = int(max_sent_len_basic)
num_classes = int(num_classes)
dropout = float(kwargs["dropout"])
lambda_w = float(kwargs["lambda_w"])
index = T.lscalar()
X = T.imatrix('X')
M = T.imatrix('M')
y = T.ivector('y')
# Input Layer
l_in = lasagne.layers.InputLayer((batch_size, max_seq_len), input_var=X)
print(" l_in shape: {}\n".format(get_output_shape(l_in)))
l_mask = lasagne.layers.InputLayer((batch_size, max_seq_len), input_var=M)
#l_mask2 = lasagne.layers.InputLayer((batch_size, max_seq_len), input_var=M)
#l_mask_concat = lasagne.layers.ConcatLayer([l_mask, l_mask2])
print(" l_mask shape: {}\n".format(get_output_shape(l_mask)))
#print(" l_mask shape: {}\n".format(get_output_shape(l_mask_concat)))
# Embedding layer
l_emb = lasagne.layers.EmbeddingLayer(l_in, input_size=V, output_size=K, W=W)
# keep the embeddings static
l_emb.params[l_emb.W].remove('trainable')
print(" l_emb shape: {}\n".format(get_output_shape(l_emb)))
# add droput
#l_emb = lasagne.layers.DropoutLayer(l_emb, p=.2)
# Use orthogonal Initialization for LSTM gates
gate_params = lasagne.layers.recurrent.Gate(
W_in=lasagne.init.Orthogonal(), W_hid=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.)
)
cell_params = lasagne.layers.recurrent.Gate(
W_in=lasagne.init.Orthogonal(), W_hid=lasagne.init.Orthogonal(),
W_cell=None, b=lasagne.init.Constant(0.),
nonlinearity=lasagne.nonlinearities.tanh
)
l_fwd = lasagne.layers.LSTMLayer(
l_emb, num_units=num_hidden, grad_clipping=grad_clip,
nonlinearity=lasagne.nonlinearities.tanh, mask_input=l_mask,
ingate=gate_params, forgetgate=gate_params, cell=cell_params,
outgate=gate_params, learn_init=True
)
l_fwd = lasagne.layers.DropoutLayer(l_fwd,p=dropout)
print(" forward shape: {}\n".format(get_output_shape(l_fwd)))
if kwargs["lstm"] == "bi":
gate_params_bwd = lasagne.layers.recurrent.Gate(
W_in=lasagne.init.Orthogonal(), W_hid=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.)
)
cell_params_bwd = lasagne.layers.recurrent.Gate(
W_in=lasagne.init.Orthogonal(), W_hid=lasagne.init.Orthogonal(),
W_cell=None, b=lasagne.init.Constant(0.),
nonlinearity=lasagne.nonlinearities.tanh
)
l_bwd = lasagne.layers.LSTMLayer(
l_emb, num_units=num_hidden, grad_clipping=grad_clip,
nonlinearity=lasagne.nonlinearities.tanh, mask_input=l_mask,
ingate=gate_params_bwd, forgetgate=gate_params_bwd, cell=cell_params_bwd,
outgate=gate_params_bwd, learn_init=True,
backwards=True
)
l_bwd = lasagne.layers.DropoutLayer(l_bwd,p=dropout)
print(" backward shape: {}\n".format(get_output_shape(l_bwd)))
# concat and dropout
l_concat = lasagne.layers.ConcatLayer([l_fwd, l_bwd])
#l_concat = lasagne.layers.ElemwiseSumLayer([l_fwd, l_bwd])
l_concat_dropout = lasagne.layers.DropoutLayer(l_concat,p=dropout)
print(" concat shape: {}\n".format(get_output_shape(l_concat)))
else:
l_concat_dropout = l_fwd
network = lasagne.layers.DenseLayer(
l_concat_dropout,
num_units=num_classes,
nonlinearity=lasagne.nonlinearities.softmax
)
#print(" network shape: {}\n".format(get_output_shape(network)))
self.network = network
output = lasagne.layers.get_output(network)
# Define objective function (cost) to minimize, mean crossentropy error
cost = lasagne.objectives.categorical_crossentropy(output, y).mean()
# Compute gradient updates
params = lasagne.layers.get_all_params(network)
cost += lambda_w*apply_penalty(params, l2)
# grad_updates = lasagne.updates.nesterov_momentum(cost, params,learn_rate)
grad_updates = lasagne.updates.adam(cost, params)
#learn_rate = .01
#grad_updates = lasagne.updates.adadelta(cost, params, learn_rate)
test_output = lasagne.layers.get_output(network, deterministic=True)
val_cost_fn = lasagne.objectives.categorical_crossentropy(
test_output, y).mean()
preds = T.argmax(test_output, axis=1)
val_acc_fn = T.mean(T.eq(preds, y),
dtype=theano.config.floatX)
self.val_fn = theano.function([X, M, y], [val_cost_fn, val_acc_fn, preds],
allow_input_downcast=True)
if kwargs["lstm"] == "bi":
concat_output = lasagne.layers.get_output(l_concat)
fwd_output = lasagne.layers.get_output(l_fwd)
bwd_output = lasagne.layers.get_output(l_bwd)
mask_output = lasagne.layers.get_output(l_mask)
#mask_concat_output = lasagne.layers.get_output(l_mask_concat)
self.get_concat = theano.function([X,M], [concat_output, fwd_output, bwd_output, mask_output]) #, mask_concat_output])
#print(y_train)
# Compile train objective
print "Compiling training functions"
self.train = theano.function(inputs = [X,M,y], outputs = cost, updates = grad_updates, allow_input_downcast=True)
self.test = theano.function(inputs = [X,M,y], outputs = val_acc_fn)
self.pred = theano.function(inputs = [X,M],outputs = preds)
def execute(dataset, learning_rate=0.00001, learning_rate_annealing=1.0,
lmd=0., noise=0.0, encoder_units=[1024, 512, 256],
num_epochs=500, which_fold=1,
save_path=None, save_copy=None, dataset_path=None,
num_fully_connected=0, exp_name='', init_args=None):
# Reading dataset
print("Loading data")
if dataset == "1000_genomes" and which_fold == 1 and False:
x_unsup = mlh.load_data(dataset, dataset_path, None,
which_fold=which_fold, keep_labels=1.0,
missing_labels_val=-1.0,
embedding_input='raw', transpose=False)
import pdb; pdb.set_trace()
x_train = np.zeros((x_unsup[0].shape[0], x_unsup[0].shape[1]*2), dtype="int8")
x_train[:,::2] = (x_unsup[0] == 2)
x_train[:,1::2] = (x_unsup[0] >= 1)
x_valid = np.zeros((x_unsup[2].shape[0], x_unsup[2].shape[1]*2), dtype="int8")
x_valid[:,::2] = (x_unsup[2] == 2)
x_valid[:,1::2] = (x_unsup[2] >= 1)
else:
x_unsup = mlh.load_data(dataset, dataset_path, None,
which_fold=which_fold, keep_labels=1.0,
missing_labels_val=-1.0,
embedding_input='bin', transpose=True)
x_train = x_unsup[0][0]
x_valid = x_unsup[1][0]
print(x_train.shape, x_valid.shape)
n_features = x_train.shape[1]
exp_name += "learn_snp2vec_dae_h"
for e in encoder_units:
exp_name += ('-' + str(e))
# exp_name += '_g-' + str(gamma)
exp_name += '_l-' + str(lmd)
exp_name += '_lr-' + str(learning_rate)
exp_name += '_fold-' + str(which_fold)
save_path = os.path.join(save_path, exp_name)
save_copy = os.path.join(save_copy, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(save_copy):
os.makedirs(save_copy)
# Prepare Theano variables for inputs and targets
input_var = T.matrix('input')
target_reconst = T.matrix('target')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
batch_size = 128
# building network
encoder = InputLayer((batch_size, n_features), input_var)
# building the encoder and decoder
#import pdb; pdb.set_trace()
for i in range(len(encoder_units)):
encoder = DenseLayer(
encoder,
num_units=encoder_units[i],
W=Uniform(0.00001),
nonlinearity=leaky_rectify) # if i < len(encoder_units)-1 else linear)
embedding = lasagne.layers.get_output(encoder)
get_embedding_fn = theano.function([input_var], embedding)
params = lasagne.layers.get_all_params(encoder, trainable=True)
monitor_labels = ["embedding min", "embedding mean", "embedding max"]
val_outputs = [embedding.min(), embedding.mean(), embedding.max()]
nets = [encoder]
decoder_units = encoder_units[::-1][1:]
print(decoder_units)
decoder = encoder
for i in range(len(decoder_units)):
decoder = DenseLayer(decoder,
num_units=decoder_units[i],
W=Uniform(0.0001),
nonlinearity=leaky_rectify)
decoder = DenseLayer(decoder,
num_units=n_features,
W=convert_initialization(
init_args["decoder_init"],
nonlinearity="sigmoid"),
nonlinearity=sigmoid)
prediction_reconst = lasagne.layers.get_output(decoder)
# Reconstruction error
loss_reconst = lasagne.objectives.binary_crossentropy(prediction_reconst,
target_reconst).mean()
# loss_reconst = mh.define_sampled_mean_bincrossentropy(
# prediction_reconst, target_reconst, gamma=gamma)
#loss_reconst = mh.dice_coef_loss(
# target_reconst, prediction_reconst).mean()
accuracy = T.eq(T.gt(prediction_reconst, 0.5), target_reconst).mean()
params += lasagne.layers.get_all_params(decoder, trainable=True)
monitor_labels += ["reconst. loss", "reconst. accuracy"]
val_outputs += [loss_reconst, accuracy]
nets += [decoder]
# sparsity_reconst = gamma * l1(prediction_reconst)
# roh = input_var.mean(0)
# sparsity_reconst = ((roh * T.log(roh / (prediction_reconst.mean(0)+1e-8))) +\
# ((1 - roh) * T.log((1 - roh) / (1 - prediction_reconst + 1e-8)))).sum()
# Combine losses
loss = loss_reconst # + sparsity_reconst
# applying weight decay
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
val_outputs += [loss]
monitor_labels += ['loss']
# Some variables
max_patience = 100
patience = 0
train_monitored = []
valid_monitored = []
train_loss = []
updates = lasagne.updates.adam(loss,
params,
learning_rate=lr)
for k in updates.keys():
if updates[k].ndim == 2:
updates[k] = lasagne.updates.norm_constraint(updates[k], 1.0)
inputs = [input_var, target_reconst]
# Compile training function
print "Compiling training function"
train_fn = theano.function(inputs, loss, updates=updates,
on_unused_input='ignore')
val_fn = theano.function(inputs,
[val_outputs[0]] + val_outputs,
on_unused_input='ignore')
start_training = time.time()
print "Starting training"
for epoch in range(num_epochs):
start_time = time.time()
print("Epoch {} of {}".format(epoch+1, num_epochs))
nb_minibatches = 0
loss_epoch = 0
for x, target_reconst_val in data_generator(x_train, batch_size,
shuffle=True, noise=noise):
loss_epoch += train_fn(x, target_reconst_val)
nb_minibatches += 1
loss_epoch /= nb_minibatches
train_loss += [loss_epoch]
# Monitoring on the training set
train_minibatches = data_generator(x_train, batch_size, noise=noise)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, 0)
train_monitored += [train_err]
# Monitoring on the validation set
valid_minibatches = data_generator(x_valid, batch_size, noise=noise)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, 0)
valid_monitored += [valid_err]
early_stop_criterion = 'loss'
early_stop_val = valid_err[monitor_labels.index(early_stop_criterion)]
# Early stopping
if epoch == 0:
best_valid = early_stop_val
elif early_stop_val < best_valid and early_stop_criterion == 'loss':
best_valid = early_stop_val
patience = 0
# Save stuff
np.savez(save_path+'/model_snp2vec_best.npz',
*lasagne.layers.get_all_param_values(nets))
np.savez(save_path + "/errors_snp2vec_best.npz",
zip(*train_monitored), zip(*valid_monitored))
else:
patience += 1
np.savez(os.path.join(save_path, 'model_snp2vec_last.npz'),
*lasagne.layers.get_all_param_values(nets))
np.savez(save_path + "/errors_snp2vec_last.npz",
zip(*train_monitored), zip(*valid_monitored))
# End training
if (patience == max_patience) or (epoch == num_epochs-1):
print("Ending training")
# Load best model
if not os.path.exists(save_path + '/model_snp2vec_best.npz'):
print("No saved model to be tested and/or generate"
" the embedding !")
else:
with np.load(save_path + '/model_snp2vec_best.npz') as f:
param_values = [f['arr_%d' % i]
for i in range(len(f.files))]
lasagne.layers.set_all_param_values(nets, param_values)
# Use the saved model to generate the feature embedding
# Here the feature embedding is the different in the hidden
# representation between having that feature on and having it off
print("Generating embedding")
embedding_size = encoder_units[-1]
null_input = np.zeros((1, n_features), dtype="float32")
null_embedding = get_embedding_fn(null_input)[0]
all_embeddings = np.zeros((n_features,
embedding_size), dtype="float32")
"""
single_feat_input = null_input.copy()
for i in range(n_features):
if i % 10000 == 0:
print(i, n_features)
single_feat_input[:,i] = 1
all_embeddings[i] = (get_embedding_fn(single_feat_input)[0] -
null_embedding)
single_feat_input[:,i] = 0
result1 = all_embeddings[:1000].copy()
"""
block_size = 10
single_feat_batch = np.zeros((block_size, n_features), dtype="float32")
for i in range(0, n_features, block_size):
if i % 10000 == 0:
print(i, n_features)
for j in range(block_size):
single_feat_batch[j, i+j] = 1
all_embeddings[i:i+10] = (get_embedding_fn(single_feat_batch) -
null_embedding)
for j in range(block_size):
single_feat_batch[j, i+j] = 0
np.save("/Tmp/carriepl/feature_selection/all_embeddings_fold%i_noise%f.npy" % (which_fold, noise),
all_embeddings)
# Training set results
train_minibatches = data_generator(x_train, batch_size, noise=noise)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, 0)
# Validation set results
valid_minibatches = data_generator(x_valid, batch_size, noise=noise)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, 0)
# Stop
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() -
start_time))
break
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() - start_time))
# Anneal the learning rate
lr.set_value(float(lr.get_value() * learning_rate_annealing))
# Copy files to loadpath
if save_path != save_copy:
print('Copying model and other training files to {}'.format(save_copy))
copy_tree(save_path, save_copy)
def execute(dataset,
n_hidden_u,
n_hidden_t_enc,
n_hidden_t_dec,
n_hidden_s,
learning_rate,
learning_rate_annealing=1.,
embedding_source=None,
alpha=1,
beta=1,
gamma=1,
lmd=0,
encoder_net_init=0.001,
decoder_net_init=0.001,
disc_nonlinearity='softmax',
keep_labels=1.0,
prec_recall_cutoff=True,
missing_labels_val=-1.0,
which_fold=0,
early_stop_criterion='accuracy',
save_path='/Tmp/romerosa/DietNetworks/',
dataset_path='/Tmp/' + os.environ["USER"] + '/datasets/',
resume=False,
exp_name=''):
# Prepare embedding information
if embedding_source is None:
embedding_input = 'raw'
else:
embedding_input = embedding_source
embedding_source = os.path.join(
dataset_path, embedding_input + '_fold' + str(which_fold) + '.npy')
# Load the dataset
print("Loading data")
x_train, y_train, x_valid, y_valid, x_test, y_test, \
x_unsup, training_labels = mlh.load_data(
dataset, dataset_path, embedding_source,
which_fold=which_fold, keep_labels=keep_labels,
missing_labels_val=missing_labels_val,
embedding_input=embedding_input)
if x_unsup is not None:
n_samples_unsup = x_unsup.shape[1]
else:
n_samples_unsup = 0
# Extract required information from data
n_samples, n_feats = x_train.shape
print("Number of features : ", n_feats)
print("Glorot init : ", 2.0 / (n_feats + n_hidden_t_enc[-1]))
n_targets = y_train.shape[1]
# Set some variables
batch_size = 138
beta = gamma if (gamma == 0) else beta
# Preparing folder to save stuff
if embedding_source is None:
embedding_name = embedding_input
else:
embedding_name = embedding_source.replace("_", "").split(".")[0]
exp_name += embedding_name.rsplit('/', 1)[::-1][0] + '_'
exp_name += mlh.define_exp_name(keep_labels, alpha, beta, gamma, lmd,
n_hidden_u, n_hidden_t_enc, n_hidden_t_dec,
n_hidden_s, which_fold, learning_rate,
decoder_net_init, encoder_net_init,
early_stop_criterion,
learning_rate_annealing)
print("Experiment: " + exp_name)
save_path = os.path.join(save_path, dataset, exp_name)
print(save_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
# Prepare Theano variables for inputs and targets
input_var_sup = T.matrix('input_sup')
input_var_unsup = theano.shared(x_unsup, 'input_unsup') # x_unsup TBD
target_var_sup = T.matrix('target_sup')
# Build model
print("Building model")
# Some checkings
# assert len(n_hidden_u) > 0
assert len(n_hidden_t_enc) > 0
assert len(n_hidden_t_dec) > 0
assert n_hidden_t_dec[-1] == n_hidden_t_enc[-1]
# Build feature embedding networks (encoding and decoding if gamma > 0)
nets, embeddings, pred_feat_emb = mh.build_feat_emb_nets(
embedding_source, n_feats, n_samples_unsup, input_var_unsup,
n_hidden_u, n_hidden_t_enc, n_hidden_t_dec, gamma, encoder_net_init,
encoder_net_init, save_path)
# Build feature embedding reconstruction networks (if alpha > 0, beta > 0)
nets += mh.build_feat_emb_reconst_nets(
[alpha, beta], n_samples_unsup, n_hidden_u,
[n_hidden_t_enc, n_hidden_t_dec], nets,
[encoder_net_init, encoder_net_init])
# Supervised network
discrim_net, hidden_rep = mh.build_discrim_net(
batch_size, n_feats, input_var_sup, n_hidden_t_enc, n_hidden_s,
embeddings[0], disc_nonlinearity, n_targets)
# Reconstruct network
nets += [
mh.build_reconst_net(hidden_rep,
embeddings[1] if len(embeddings) > 1 else None,
n_feats, gamma)
]
# Load best model
with np.load(os.path.join(save_path, 'dietnets_best.npz')) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(
filter(None, nets) + [discrim_net], param_values)
print("Building and compiling training functions")
# Build functions
predictions, predictions_det = mh.define_predictions(nets, start=2)
prediction_sup, prediction_sup_det = mh.define_predictions([discrim_net])
prediction_sup = prediction_sup[0]
prediction_sup_det = prediction_sup_det[0]
# Define losses
# reconstruction losses
_, reconst_losses_det = mh.define_reconst_losses(
predictions, predictions_det,
[input_var_unsup, input_var_unsup, input_var_sup])
# supervised loss
_, sup_loss_det = mh.define_sup_loss(disc_nonlinearity, prediction_sup,
prediction_sup_det, keep_labels,
target_var_sup, missing_labels_val)
# Define inputs
inputs = [input_var_sup, target_var_sup]
# Combine losses
loss_det = sup_loss_det + alpha*reconst_losses_det[0] + \
beta*reconst_losses_det[1] + gamma*reconst_losses_det[2]
# Define parameters
params = lasagne.layers.get_all_params([discrim_net] + filter(None, nets),
trainable=True)
l2_penalty = apply_penalty(params, l2)
loss_det = loss_det + lmd * l2_penalty
# Monitoring Labels
monitor_labels = [
"reconst. feat. W_enc", "reconst. feat. W_dec", "reconst. loss"
]
monitor_labels = [
i for i, j in zip(monitor_labels, reconst_losses_det) if j != 0
]
monitor_labels += ["feat. W_enc. mean", "feat. W_enc var"]
monitor_labels += ["feat. W_dec. mean", "feat. W_dec var"] if \
(embeddings[1] is not None) else []
monitor_labels += ["loss. sup.", "total loss"]
# test function
val_outputs = reconst_losses_det
val_outputs = [
i for i, j in zip(val_outputs, reconst_losses_det) if j != 0
]
val_outputs += [embeddings[0].mean(), embeddings[0].var()]
val_outputs += [embeddings[1].mean(), embeddings[1].var()] if \
(embeddings[1] is not None) else []
val_outputs += [sup_loss_det, loss_det]
# Compute accuracy and add it to monitoring list
test_acc, test_pred = mh.define_test_functions(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
target_var_sup)
monitor_labels.append("accuracy")
val_outputs.append(test_acc)
# Compile prediction function
predict = theano.function([input_var_sup], test_pred)
# Compile validation function
val_fn = theano.function(inputs, [prediction_sup_det] + val_outputs,
on_unused_input='ignore')
# Finally, launch the training loop.
print("Starting testing...")
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
batch_size,
shuffle=False)
test_err, pred, targets = mlh.monitoring(test_minibatches,
"test",
val_fn,
monitor_labels,
prec_recall_cutoff,
return_pred=True)
lab = targets.argmax(1)
pred_argmax = pred.argmax(1)
continent_cat = mh.create_1000_genomes_continent_labels()
lab_cont = np.zeros(lab.shape)
pred_cont = np.zeros(pred_argmax.shape)
for i, c in enumerate(continent_cat):
for el in c:
lab_cont[lab == el] = i
pred_cont[pred_argmax == el] = i
cm_e = np.zeros((26, 26))
cm_c = np.zeros((5, 5))
for i in range(26):
for j in range(26):
cm_e[i, j] = ((pred_argmax == i) * (lab == j)).sum()
for i in range(5):
for j in range(5):
cm_c[i, j] = ((pred_cont == i) * (lab_cont == j)).sum()
np.savez(os.path.join(save_path, 'cm' + str(which_fold) + '.npz'),
cm_e=cm_e,
cm_c=cm_c)
print(os.path.join(save_path, 'cm' + str(which_fold) + '.npz'))
def __init__(self,
V,
d,
max_post_length,
max_sentence_length,
embeddings=None,
GRAD_CLIP=100,
num_layers=1,
learning_rate=0.01,
add_biases=False,
rd=100,
op=False,
word_attn=True,
sent_attn=True,
highway=False,
hops=3,
words=True,
frames=False,
discourse=False):
self._hyper_params = dict(V=V,
d=d,
max_post_length=max_post_length,
max_sentence_length=max_sentence_length,
GRAD_CLIP=GRAD_CLIP,
num_layers=num_layers,
learning_rate=learning_rate,
add_biases=add_biases,
rd=rd,
op=op,
word_attn=word_attn,
sent_attn=sent_attn,
highway=highway,
hops=hops)
print(V, d, max_post_length, max_sentence_length)
#S x N matrix of sentences (aka list of word indices)
#B x S x N tensor of batches of posts
idxs_rr = T.itensor3('idxs_rr')
idxs_op = T.itensor3('idxs_op')
#B x S x N matrix
mask_rr_w = T.itensor3('mask_rr_w')
mask_op_w = T.itensor3('mask_rr_w')
#B x S matrix
mask_rr_s = T.imatrix('mask_rr_s')
mask_op_s = T.imatrix('mask_rr_s')
#B-long vector
gold = T.ivector('gold')
lambda_w = T.scalar('lambda_w')
p_dropout = T.scalar('p_dropout')
biases = T.matrix('biases')
weights = T.ivector('weights')
#now use this as an input to an LSTM
l_idxs_rr = lasagne.layers.InputLayer(shape=(None, max_post_length,
max_sentence_length),
input_var=idxs_rr)
l_mask_rr_w = lasagne.layers.InputLayer(shape=(None, max_post_length,
max_sentence_length),
input_var=mask_rr_w)
l_mask_rr_s = lasagne.layers.InputLayer(shape=(None, max_post_length),
input_var=mask_rr_s)
l_idxs_op = lasagne.layers.InputLayer(shape=(None, max_post_length,
max_sentence_length),
input_var=idxs_op)
l_mask_op_w = lasagne.layers.InputLayer(shape=(None, max_post_length,
max_sentence_length),
input_var=mask_op_w)
l_mask_op_s = lasagne.layers.InputLayer(shape=(None, max_post_length),
input_var=mask_op_s)
if add_biases:
l_biases = lasagne.layers.InputLayer(shape=(None, 1),
input_var=biases)
#now B x S x N x D
if embeddings is not None:
l_emb_rr_w = lasagne.layers.EmbeddingLayer(
l_idxs_rr, V, d, W=lasagne.utils.floatX(embeddings))
else:
l_emb_rr_w = lasagne.layers.EmbeddingLayer(l_idxs_rr, V, d)
#now B x S x D
if words:
if word_attn:
l_attn_rr_w = AttentionWordLayer([l_emb_rr_w, l_mask_rr_w], d)
l_avg_rr_s = WeightedAverageWordLayer(
[l_emb_rr_w, l_attn_rr_w])
else:
l_avg_rr_s = AverageWordLayer([l_emb_rr_w, l_mask_rr_w])
concats = [l_avg_rr_s]
inputs = [idxs_rr, mask_rr_w, mask_rr_s]
else:
concats = []
inputs = [mask_rr_w, mask_rr_s]
if frames:
idxs_frames_rr = T.itensor3('idxs_frames_rr')
inputs.append(idxs_frames_rr)
l_idxs_frames_rr = lasagne.layers.InputLayer(
shape=(None, max_post_length, max_sentence_length),
input_var=idxs_frames_rr)
l_emb_frames_rr_w = lasagne.layers.EmbeddingLayer(l_idxs_frames_rr,
V,
d,
W=l_emb_rr_w.W)
if word_attn:
l_attn_rr_frames = AttentionWordLayer(
[l_emb_frames_rr_w, l_mask_rr_w], d)
l_avg_rr_s_frames = WeightedAverageWordLayer(
[l_emb_frames_rr_w, l_attn_rr_frames])
else:
l_avg_rr_s_frames = AverageWordLayer(
[l_emb_frames_rr_w, l_mask_rr_w])
concats.append(l_avg_rr_s_frames)
if discourse:
idxs_disc_rr = T.imatrix('idxs_disc_rr')
inputs.append(idxs_disc_rr)
l_emb_disc_rr = lasagne.layers.EmbeddingLayer(l_idxs_disc_rr,
V,
d,
W=l_emb_rr_w.W)
concats.append(l_emb_disc_rr)
l_avg_rr_s = lasagne.layers.ConcatLayer(concats, axis=-1)
if highway:
l_avg_rr_s = HighwayLayer(
l_avg_rr_s,
num_units=l_avg_rr_s.output_shape[-1],
nonlinearity=lasagne.nonlinearities.rectify,
num_leading_axes=2)
#separate embeddings for OP
if embeddings is not None:
l_emb_op_w = lasagne.layers.EmbeddingLayer(
l_idxs_op, V, d, W=lasagne.utils.floatX(embeddings))
else:
l_emb_op_w = lasagne.layers.EmbeddingLayer(l_idxs_op, V, d)
if op:
if words:
l_attn_op_w = AttentionWordLayer([l_emb_op_w, l_mask_op_w], d)
l_avg_op_s = WeightedAverageWordLayer(
[l_emb_op_w, l_attn_op_w])
concats = [l_avg_op_s]
inputs.extend([idxs_op, mask_op_w, mask_op_s])
else:
concats = []
inputs.extend([mask_op_w, mask_op_s])
if frames:
idxs_frames_op = T.itensor3('idxs_frames_op')
inputs.append(idxs_frames_op)
l_idxs_frames_op = lasagne.layers.InputLayer(
shape=(None, max_post_length, max_sentence_length),
input_var=idxs_frames_op)
l_emb_frames_op_w = lasagne.layers.EmbeddingLayer(
l_idxs_frames_op, V, d, W=l_emb_op_w.W)
l_attn_op_frames = AttentionWordLayer(
[l_emb_frames_op_w, l_mask_op_w], d)
l_avg_op_s_frames = WeightedAverageWordLayer(
[l_emb_frames_op_w, l_attn_op_frames])
concats.append(l_avg_op_s_frames)
if discourse:
idxs_disc_op = T.imatrix('idxs_disc_op')
inputs.append(idxs_disc_op)
l_emb_disc_op = lasagne.layers.EmbeddingLayer(l_idxs_disc_op,
V,
d,
W=l_emb_op_w.W)
concats.append(l_emb_disc_op)
l_avg_op_s = lasagne.layers.ConcatLayer(concats, axis=-1)
#bidirectional LSTM
l_lstm_op_s_fwd = lasagne.layers.LSTMLayer(
l_avg_op_s,
rd,
nonlinearity=lasagne.nonlinearities.tanh,
grad_clipping=GRAD_CLIP,
mask_input=l_mask_op_s)
l_lstm_op_s_rev = lasagne.layers.LSTMLayer(
l_avg_op_s,
rd,
nonlinearity=lasagne.nonlinearities.tanh,
grad_clipping=GRAD_CLIP,
mask_input=l_mask_op_s,
backwards=True)
l_avg_op_s = lasagne.layers.ConcatLayer(
[l_lstm_op_s_fwd, l_lstm_op_s_rev], axis=-1)
l_attn_op_s = AttentionSentenceLayer([l_avg_op_s, l_mask_op_s], d)
l_op_avg = WeightedAverageSentenceLayer([l_avg_op_s, l_attn_op_s])
#bidirectional LSTM
l_lstm_rr_s_fwd = lasagne.layers.LSTMLayer(
l_avg_rr_s,
rd,
nonlinearity=lasagne.nonlinearities.tanh,
grad_clipping=GRAD_CLIP,
mask_input=l_mask_rr_s)
l_lstm_rr_s_rev = lasagne.layers.LSTMLayer(
l_avg_rr_s,
rd,
nonlinearity=lasagne.nonlinearities.tanh,
grad_clipping=GRAD_CLIP,
mask_input=l_mask_rr_s,
backwards=True)
#for attention or avergae
l_lstm_rr_s = lasagne.layers.ConcatLayer(
[l_lstm_rr_s_fwd, l_lstm_rr_s_rev], axis=-1)
#now memory network
init_memory_response = AverageSentenceLayer([l_lstm_rr_s, l_mask_rr_s])
if op:
init_memory_response = lasagne.layers.ConcatLayer(
[init_memory_response, l_op_avg])
l_memory = MyConcatLayer([l_lstm_rr_s, init_memory_response])
if sent_attn:
l_attn_rr_s = AttentionSentenceLayer([l_lstm_rr_s, l_mask_rr_s], d)
l_rr_avg = WeightedAverageSentenceLayer([l_lstm_rr_s, l_attn_rr_s])
else:
l_rr_avg = AverageSentenceLayer([l_lstm_rr_s, l_mask_rr_s])
for i in range(hops):
l_attn_rr_s = AttentionSentenceLayer([l_memory, l_mask_rr_s], d)
l_rr_avg = WeightedAverageSentenceLayer([l_memory, l_attn_rr_s])
if op:
l_rr_avg = lasagne.layers.ConcatLayer([l_rr_avg, l_op_avg])
l_memory = MyConcatLayer([l_lstm_rr_s, l_rr_avg])
l_hid = l_rr_avg
for num_layer in range(num_layers):
l_hid = lasagne.layers.DenseLayer(
l_hid,
num_units=d,
nonlinearity=lasagne.nonlinearities.rectify)
#now B x 1
l_hid = lasagne.layers.DropoutLayer(l_hid, p_dropout)
if add_biases:
l_hid = lasagne.layers.ConcatLayer([l_hid, l_biases], axis=-1)
inputs.append(biases)
self.network = lasagne.layers.DenseLayer(l_hid,
num_units=1,
nonlinearity=T.nnet.sigmoid)
predictions = lasagne.layers.get_output(self.network).ravel()
xent = lasagne.objectives.binary_crossentropy(predictions, gold)
loss = lasagne.objectives.aggregate(xent,
weights,
mode='normalized_sum')
params = lasagne.layers.get_all_params(self.network, trainable=True)
#add regularization
loss += lambda_w * apply_penalty(params, l2)
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate=learning_rate, momentum=0.9)
print('compiling...')
train_outputs = loss
self.train = theano.function(inputs +
[gold, lambda_w, p_dropout, weights],
train_outputs,
updates=updates,
allow_input_downcast=True,
on_unused_input='warn')
print('...')
test_predictions = lasagne.layers.get_output(
self.network, deterministic=True).ravel()
self.predict = theano.function(inputs,
test_predictions,
allow_input_downcast=True,
on_unused_input='warn')
test_acc = T.mean(T.eq(test_predictions > .5, gold),
dtype=theano.config.floatX)
print('...')
test_loss = lasagne.objectives.binary_crossentropy(
test_predictions, gold).mean()
self.validate = theano.function(
inputs + [gold, lambda_w, p_dropout, weights], [loss, test_acc],
on_unused_input='warn')
print('...')
#attention for words, B x S x N
print('attention...')
word_attention = lasagne.layers.get_output(
AttentionWordLayer([l_emb_rr_w, l_mask_rr_w],
d,
W_w=l_attn_rr_w.W_w,
u_w=l_attn_rr_w.u_w,
b_w=l_attn_rr_w.b_w,
normalized=False))
self.word_attention = theano.function([idxs_rr, mask_rr_w],
word_attention,
allow_input_downcast=True,
on_unused_input='warn')
#attention for sentences, B x S
print('...')
sentence_attention = lasagne.layers.get_output(l_attn_rr_s)
if add_biases:
inputs = inputs[:-1]
self.sentence_attention = theano.function(inputs,
sentence_attention,
allow_input_downcast=True,
on_unused_input='warn')
print('finished compiling...')
def __init__(self,
W=None,
W_path=None,
K=300,
num_hidden=256,
batch_size=None,
grad_clip=100.,
max_sent_len=200,
num_classes=2,
**kwargs):
W = W
V = len(W)
K = int(K)
print("this is the value of K: {}\n".format(K))
num_hidden = int(num_hidden)
batch_size = int(batch_size)
grad_clip = int(grad_clip)
max_seq_len = int(max_sent_len)
max_post_len = int(kwargs["max_post_len"])
num_classes = int(num_classes)
dropout = float(kwargs["dropout"])
lambda_w = float(kwargs["lambda_w"])
separate_attention_context = str_to_bool(kwargs["separate_attention_context"])
separate_attention_response = str_to_bool(kwargs["separate_attention_response"])
interaction = str_to_bool(kwargs["interaction"])
separate_attention_context_words = str_to_bool(kwargs["separate_attention_context_words"])
separate_attention_response_words = str_to_bool(kwargs["separate_attention_response_words"])
print("this is the separate_attention_context: {}\n".format(separate_attention_context))
print("this is the separate_attention_response: {}\n".format(separate_attention_response))
print("this is the separate_attention_context_words: {}\n".format(separate_attention_context_words))
print("this is the separate_attention_response_words: {}\n".format(separate_attention_response_words))
print("this is the interaction: {}\n".format(interaction))
#S x N matrix of sentences (aka list of word indices)
#B x S x N tensor of batches of responses
idxs_context = T.itensor3('idxs_context') #imatrix
#B x S x N matrix
mask_context_words = T.itensor3('mask_context_words')
#B x S matrix
mask_context_sents = T.imatrix('mask_context_sents')
#B x S x N tensor of batches of responses
idxs_response = T.itensor3('idxs_response') #imatrix
#B x S x N matrix
mask_response_words = T.itensor3('mask_response_words')
#B x S matrix
mask_response_sents = T.imatrix('mask_response_sents')
#B-long vector
y = T.ivector('y')
# TODO
# Add biases, other params?
#lambda_w = T.scalar('lambda_w')
#p_dropout = T.scalar('p_dropout')
#biases = T.matrix('biases')
#weights = T.ivector('weights')
inputs = [idxs_response, mask_response_words, mask_response_sents]
# TODO
# change inputs, function calls
#idxs_context, mask_context_words, mask_context_sents
#now use this as an input to an LSTM
l_idxs_context = lasagne.layers.InputLayer(shape=(None, max_post_len, max_sent_len),
input_var=idxs_context)
l_mask_context_words = lasagne.layers.InputLayer(shape=(None, max_post_len, max_sent_len),input_var=mask_context_words)
l_mask_context_sents = lasagne.layers.InputLayer(shape=(None, max_post_len),
input_var=mask_context_sents)
#if add_biases:
# l_biases = lasagne.layers.InputLayer(shape=(None,1),
# input_var=biases)
#now B x S x N x D
#l_emb = lasagne.layers.EmbeddingLayer(l_in, input_size=V, output_size=K, W=W)
l_emb_rr_w_context = lasagne.layers.EmbeddingLayer(l_idxs_context, input_size=V, output_size=K,
W=W)
l_emb_rr_w_context.params[l_emb_rr_w_context.W].remove('trainable')
# l_hid_context = l_emb_rr_w
#CBOW w/attn
#now B x S x D
if separate_attention_context_words:
l_attention_words_context = AttentionWordLayer([l_emb_rr_w_context, l_mask_context_words], K)
#print(" attention word layer shape: {}\n".format(get_output_shape(l_attention_words_context)))
l_avg_rr_s_words_context = WeightedAverageWordLayer([l_emb_rr_w_context,l_attention_words_context])
else:
l_avg_rr_s_words_context = WeightedAverageWordLayer([l_emb_rr_w_context, l_mask_context_words])
##concats = l_avg_rr_s_words_context
##concats = [l_avg_rr_s_words_context]
l_avg_rr_s_context = l_avg_rr_s_words_context
# concats not relevant here, was just frames, sentiment etc for other task.
#l_avg_rr_s_context = lasagne.layers.ConcatLayer(concats, axis=-1)
# TODO
# add highway ?
#add MLP
#if highway:
# l_avg_rr_s_context = HighwayLayer(l_avg_rr_s_context, num_units=l_avg_rr_s_context.output_shape[-1],
# nonlinearity=lasagne.nonlinearities.rectify,
# num_leading_axes=2)
#
l_lstm_rr_s_context = lasagne.layers.LSTMLayer(l_avg_rr_s_context, num_hidden,
nonlinearity=lasagne.nonlinearities.tanh,
grad_clipping=grad_clip,
mask_input=l_mask_context_sents)
l_lstm_rr_s_context = lasagne.layers.DropoutLayer(l_lstm_rr_s_context,p=dropout)
if interaction:
#l_hid_context = l_lstm_rr_s_context
if separate_attention_context:
print("separate attention context\n")
l_attn_rr_s_context = AttentionSentenceLayer([l_lstm_rr_s_context, l_mask_context_sents], num_hidden)
l_lstm_rr_avg_context = WeightedAverageSentenceLayer([l_lstm_rr_s_context, l_attn_rr_s_context])
print(" attention weighted average sentence layer shape: {}\n".format(get_output_shape(l_lstm_rr_avg_context)))
else:
print("just averaged context without attention\n")
l_lstm_rr_avg_context = WeightedAverageSentenceLayer([l_lstm_rr_s_context, l_mask_context_sents])
print(" attention weighted average sentence layer shape: {}\n".format(get_output_shape(l_lstm_rr_avg_context)))
l_hid_context = l_lstm_rr_avg_context
print("interaction\n")
else:
print("no interaction!!! \n")
#LSTM w/ attn
#now B x D
if separate_attention_context:
print("separate attention context\n")
l_attn_rr_s_context = AttentionSentenceLayer([l_lstm_rr_s_context, l_mask_context_sents], num_hidden)
l_lstm_rr_avg_context = WeightedAverageSentenceLayer([l_lstm_rr_s_context, l_attn_rr_s_context])
print(" attention weighted average sentence layer shape: {}\n".format(get_output_shape(l_lstm_rr_avg_context)))
else:
print("just averaged context without attention\n")
l_lstm_rr_avg_context = WeightedAverageSentenceLayer([l_lstm_rr_s_context, l_mask_context_sents])
print(" attention weighted average sentence layer shape: {}\n".format(get_output_shape(l_lstm_rr_avg_context)))
l_hid_context = l_lstm_rr_avg_context
# TODO
# change inputs, function calls
#idxs_context, mask_context_words, mask_context_sents
#now use this as an input to an LSTM
l_idxs_response = lasagne.layers.InputLayer(shape=(None, max_post_len, max_sent_len),
input_var=idxs_response)
l_mask_response_words = lasagne.layers.InputLayer(shape=(None, max_post_len, max_sent_len),input_var=mask_response_words)
l_mask_response_sents = lasagne.layers.InputLayer(shape=(None, max_post_len),
input_var=mask_response_sents)
#if add_biases:
# l_biases = lasagne.layers.InputLayer(shape=(None,1),
# input_var=biases)
#now B x S x N x D
#l_emb = lasagne.layers.EmbeddingLayer(l_in, input_size=V, output_size=K, W=W)
l_emb_rr_w_response = lasagne.layers.EmbeddingLayer(l_idxs_response, input_size=V, output_size=K,
W=W)
l_emb_rr_w_response.params[l_emb_rr_w_response.W].remove('trainable')
# l_hid_response = l_emb_rr_w
#CBOW w/attn
#now B x S x D
if separate_attention_response_words:
l_attention_words_response = AttentionWordLayer([l_emb_rr_w_response, l_mask_response_words], K)
#print(" attention word layer shape: {}\n".format(get_output_shape(l_attention_words_response)))
l_avg_rr_s_words_response = WeightedAverageWordLayer([l_emb_rr_w_response,l_attention_words_response])
else:
l_avg_rr_s_words_response = WeightedAverageWordLayer([l_emb_rr_w_response, l_mask_response_words])
#l_attention_words_response = AttentionWordLayer([l_emb_rr_w_response, l_mask_response_words], K)
#print(" attention word layer shape: {}\n".format(get_output_shape(l_attention_words_response)))
#l_avg_rr_s_words_response = WeightedAverageWordLayer([l_emb_rr_w_response, l_mask_response_words])
##concats = l_avg_rr_s_words_response
##concats = [l_avg_rr_s_words_response]
l_avg_rr_s_response = l_avg_rr_s_words_response
# concats not relevant here, was just frames, sentiment etc for other task.
#l_avg_rr_s_response = lasagne.layers.ConcatLayer(concats, axis=-1)
# TODO
# add highway ?
#add MLP
#if highway:
# l_avg_rr_s_response = HighwayLayer(l_avg_rr_s_response, num_units=l_avg_rr_s_response.output_shape[-1],
# nonlinearity=lasagne.nonlinearities.rectify,
# num_leading_axes=2)
#
if interaction:
print("interaction\n")
# add some cell init
l_lstm_rr_s_response = lasagne.layers.LSTMLayer(l_avg_rr_s_response, num_hidden,
nonlinearity=lasagne.nonlinearities.tanh,
grad_clipping=grad_clip,cell_init=l_hid_context,
mask_input=l_mask_response_sents)
else:
l_lstm_rr_s_response = lasagne.layers.LSTMLayer(l_avg_rr_s_response, num_hidden,
nonlinearity=lasagne.nonlinearities.tanh,
grad_clipping=grad_clip,
mask_input=l_mask_response_sents)
l_lstm_rr_s_response = lasagne.layers.DropoutLayer(l_lstm_rr_s_response,p=dropout)
#LSTM w/ attn
#now B x D
if separate_attention_response:
print("separate attention on the response\n")
l_attn_rr_s_response = AttentionSentenceLayer([l_lstm_rr_s_response, l_mask_response_sents], num_hidden)
l_lstm_rr_avg_response = WeightedAverageSentenceLayer([l_lstm_rr_s_response, l_attn_rr_s_response])
print(" attention weighted average sentence layer shape: {}\n".format(get_output_shape(l_lstm_rr_avg_response)))
else:
print("just average response without attention\n")
l_lstm_rr_avg_response = WeightedAverageSentenceLayer([l_lstm_rr_s_response, l_mask_response_sents])
print(" attention weighted average sentence layer shape: {}\n".format(get_output_shape(l_lstm_rr_avg_response)))
l_hid_response = l_lstm_rr_avg_response
# TODO
# add more layers? biases?
#for num_layer in range(num_layers):
# l_hid_response = lasagne.layers.DenseLayer(l_hid_response, num_units=rd,
# nonlinearity=lasagne.nonlinearities.rectify)
# #now B x 1
# l_hid_response = lasagne.layers.DropoutLayer(l_hid_response, p_dropout)
#
#if add_biases:
# l_hid_response = lasagne.layers.ConcatLayer([l_hid_response, l_biases], axis=-1)
# inputs.append(biases)
#
#self.network = lasagne.layers.DenseLayer(l_hid_response, num_units=2,
# nonlinearity=T.nnet.sigmoid)
#
#predictions = lasagne.layers.get_output(self.network).ravel()
#
#xent = lasagne.objectives.binary_crossentropy(predictions, gold)
#loss = lasagne.objectives.aggregate(xent, weights, mode='normalized_sum')
#
#params = lasagne.layers.get_all_params(self.network, trainable=True)
#
# TODO
##add regularization? different gradient technique?
#loss += lambda_w*apply_penalty(params, l2)
#updates = lasagne.updates.nesterov_momentum(loss, params,
# learning_rate=learning_rate, momentum=0.9)
#print('compiling...')
#train_outputs = loss
#self.train = theano.function(inputs + [gold, lambda_w, p_dropout, weights],
# train_outputs,
# updates=updates,
# allow_input_downcast=True,
# on_unused_input='warn')
#print('...')
#test_predictions = lasagne.layers.get_output(self.network, deterministic=True).ravel()
#
#self.predict = theano.function(inputs,
# test_predictions,
# allow_input_downcast=True,
# on_unused_input='warn')
#test_acc = T.mean(T.eq(test_predictions > .5, gold),
# dtype=theano.config.floatX)
#print('...')
#test_loss = lasagne.objectives.binary_crossentropy(test_predictions,
# gold).mean()
#self.validate = theano.function(inputs + [gold, lambda_w, p_dropout, weights],
# [loss, test_acc],
# on_unused_input='warn')
print('...')
#attention for words, B x S x N
##attention for sentences, B x S
print('finished compiling...')
if interaction:
l_concat = l_hid_response
else:
l_concat = lasagne.layers.ConcatLayer([l_hid_context,l_hid_response])
network = lasagne.layers.DenseLayer(
l_concat,
num_units=num_classes,
nonlinearity=lasagne.nonlinearities.softmax
)
self.network = network
output = lasagne.layers.get_output(network)
# Define objective function (cost) to minimize, mean crossentropy error
cost = lasagne.objectives.categorical_crossentropy(output, y).mean()
# Compute gradient updates
params = lasagne.layers.get_all_params(network)
cost += lambda_w*apply_penalty(params, l2)
# grad_updates = lasagne.updates.nesterov_momentum(cost, params,learn_rate)
grad_updates = lasagne.updates.adam(cost, params)
#learn_rate = .01
#grad_updates = lasagne.updates.adadelta(cost, params, learn_rate)
test_output = lasagne.layers.get_output(network, deterministic=True)
val_cost_fn = lasagne.objectives.categorical_crossentropy(
test_output, y).mean()
preds = T.argmax(test_output, axis=1)
val_acc_fn = T.mean(T.eq(preds, y),
dtype=theano.config.floatX)
self.val_fn = theano.function([idxs_context, mask_context_words, mask_context_sents, idxs_response, mask_response_words, mask_response_sents, y], [val_cost_fn, val_acc_fn, preds],
allow_input_downcast=True,on_unused_input='warn')
# Compile train objective
print "Compiling training, testing, prediction functions"
self.train = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents,idxs_response, mask_response_words, mask_response_sents, y], outputs = cost, updates = grad_updates, allow_input_downcast=True,on_unused_input='warn')
self.test = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents,idxs_response, mask_response_words, mask_response_sents, y], outputs = val_acc_fn,allow_input_downcast=True,on_unused_input='warn')
self.pred = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents, idxs_response, mask_response_words, mask_response_sents],outputs = preds,allow_input_downcast=True,on_unused_input='warn')
if separate_attention_response:
sentence_attention = lasagne.layers.get_output(l_attn_rr_s_response, deterministic=True)
#if add_biases:
# inputs = inputs[:-1]
self.sentence_attention_response = theano.function([idxs_context, mask_context_words, mask_context_sents,idxs_response, mask_response_words, mask_response_sents],
[sentence_attention, preds],
allow_input_downcast=True,
on_unused_input='warn')
if separate_attention_context:
sentence_attention_context = lasagne.layers.get_output(l_attn_rr_s_context, deterministic=True)
#if add_biases:
# inputs = inputs[:-1]
self.sentence_attention_context = theano.function([idxs_context, mask_context_words, mask_context_sents,idxs_response, mask_response_words, mask_response_sents],
[sentence_attention_context,preds],
allow_input_downcast=True,
on_unused_input='warn')
if separate_attention_response_words:
word_attention = lasagne.layers.get_output(l_attention_words_response, deterministic=True)
self.sentence_attention_response_words = theano.function([idxs_context, mask_context_words, mask_context_sents,idxs_response, mask_response_words, mask_response_sents],[word_attention,preds],
allow_input_downcast=True,
on_unused_input='warn')
if separate_attention_context_words:
word_attention_context = lasagne.layers.get_output(l_attention_words_context, deterministic = True)
self.sentence_attention_context_words = theano.function([idxs_context, mask_context_words, mask_context_sents,idxs_response, mask_response_words, mask_response_sents],[word_attention_context,preds],
allow_input_downcast=True,
on_unused_input='warn')
def execute(dataset,
n_hidden_t_enc,
n_hidden_s,
num_epochs=500,
learning_rate=.001,
learning_rate_annealing=1.0,
gamma=1,
lmd=0.,
disc_nonlinearity="sigmoid",
keep_labels=1.0,
prec_recall_cutoff=True,
missing_labels_val=-1.0,
which_fold=1,
early_stop_criterion='loss',
save_path='/Tmp/romerosa/DietNetworks/',
save_copy='/Tmp/romerosa/DietNetworks/',
dataset_path='/Tmp/carriepl/datasets/',
resume=False):
# Load the dataset
print("Loading data")
x_train, y_train, x_valid, y_valid, x_test, y_test, \
x_unsup, training_labels = mlh.load_data(
dataset, dataset_path, None,
which_fold=which_fold, keep_labels=keep_labels,
missing_labels_val=missing_labels_val,
embedding_input='raw')
# Extract required information from data
n_samples, n_feats = x_train.shape
print("Number of features : ", n_feats)
print("Glorot init : ", 2.0 / (n_feats + n_hidden_t_enc[-1]))
n_targets = y_train.shape[1]
# Set some variables
batch_size = 128
# Preparing folder to save stuff
exp_name = 'basic_' + mlh.define_exp_name(
keep_labels, 0, 0, gamma, lmd, [], n_hidden_t_enc, [], n_hidden_s,
which_fold, learning_rate, 0, 0, early_stop_criterion,
learning_rate_annealing)
print("Experiment: " + exp_name)
save_path = os.path.join(save_path, dataset, exp_name)
save_copy = os.path.join(save_copy, dataset, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
# Prepare Theano variables for inputs and targets
input_var_sup = T.matrix('input_sup')
target_var_sup = T.matrix('target_sup')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
# Build model
print("Building model")
discrim_net = InputLayer((None, n_feats), input_var_sup)
discrim_net = DenseLayer(discrim_net,
num_units=n_hidden_t_enc[-1],
nonlinearity=rectify)
# Reconstruct the input using dec_feat_emb
if gamma > 0:
reconst_net = DenseLayer(discrim_net,
num_units=n_feats,
nonlinearity=linear)
nets = [reconst_net]
else:
nets = [None]
# Add supervised hidden layers
for hid in n_hidden_s:
discrim_net = DropoutLayer(discrim_net)
discrim_net = DenseLayer(discrim_net, num_units=hid)
assert disc_nonlinearity in ["sigmoid", "linear", "rectify", "softmax"]
discrim_net = DropoutLayer(discrim_net)
discrim_net = DenseLayer(discrim_net,
num_units=n_targets,
nonlinearity=eval(disc_nonlinearity))
print("Building and compiling training functions")
# Build and compile training functions
predictions, predictions_det = mh.define_predictions(nets, start=0)
prediction_sup, prediction_sup_det = mh.define_predictions([discrim_net])
prediction_sup = prediction_sup[0]
prediction_sup_det = prediction_sup_det[0]
# Define losses
# reconstruction losses
reconst_losses, reconst_losses_det = mh.define_reconst_losses(
predictions, predictions_det, [input_var_sup])
# supervised loss
sup_loss, sup_loss_det = mh.define_sup_loss(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
keep_labels, target_var_sup,
missing_labels_val)
inputs = [input_var_sup, target_var_sup]
params = lasagne.layers.get_all_params([discrim_net] + nets,
trainable=True)
print('Number of params: ' + str(len(params)))
# Combine losses
loss = sup_loss + gamma * reconst_losses[0]
loss_det = sup_loss_det + gamma * reconst_losses_det[0]
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
loss_det = loss_det + lmd * l2_penalty
# Compute network updates
updates = lasagne.updates.rmsprop(loss, params, learning_rate=lr)
# updates = lasagne.updates.sgd(loss,
# params,
# learning_rate=lr)
# updates = lasagne.updates.momentum(loss, params,
# learning_rate=lr, momentum=0.0)
# Apply norm constraints on the weights
for k in updates.keys():
if updates[k].ndim == 2:
updates[k] = lasagne.updates.norm_constraint(updates[k], 1.0)
# Compile training function
train_fn = theano.function(inputs,
loss,
updates=updates,
on_unused_input='ignore')
# Monitoring Labels
monitor_labels = ["reconst. loss"]
monitor_labels = [
i for i, j in zip(monitor_labels, reconst_losses) if j != 0
]
monitor_labels += ["loss. sup.", "total loss"]
# Build and compile test function
val_outputs = reconst_losses_det
val_outputs = [i for i, j in zip(val_outputs, reconst_losses) if j != 0]
val_outputs += [sup_loss_det, loss_det]
# Compute accuracy and add it to monitoring list
test_acc, test_pred = mh.define_test_functions(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
target_var_sup)
monitor_labels.append("accuracy")
val_outputs.append(test_acc)
# Compile prediction function
predict = theano.function([input_var_sup], test_pred)
# Compile validation function
val_fn = theano.function(inputs, [prediction_sup_det] + val_outputs,
on_unused_input='ignore')
# Finally, launch the training loop.
print("Starting training...")
# Some variables
max_patience = 100
patience = 0
train_monitored = []
valid_monitored = []
train_loss = []
# Pre-training monitoring
print("Epoch 0 of {}".format(num_epochs))
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
# Training loop
start_training = time.time()
for epoch in range(num_epochs):
start_time = time.time()
print("Epoch {} of {}".format(epoch + 1, num_epochs))
nb_minibatches = 0
loss_epoch = 0
# Train pass
for batch in mlh.iterate_minibatches(x_train,
training_labels,
batch_size,
shuffle=True):
loss_epoch += train_fn(*batch)
nb_minibatches += 1
loss_epoch /= nb_minibatches
train_loss += [loss_epoch]
# Monitoring on the training set
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
train_monitored += [train_err]
# Monitoring on the validation set
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
valid_monitored += [valid_err]
try:
early_stop_val = valid_err[monitor_labels.index(
early_stop_criterion)]
except:
raise ValueError("There is no monitored value by the name of %s" %
early_stop_criterion)
# Early stopping
if epoch == 0:
best_valid = early_stop_val
elif (early_stop_val > best_valid and early_stop_criterion == 'accuracy') or \
(early_stop_val < best_valid and early_stop_criterion ==
'loss. sup.'):
best_valid = early_stop_val
patience = 0
# Save stuff
np.savez(
os.path.join(save_path, 'model_best.npz'),
*lasagne.layers.get_all_param_values(
filter(None, nets) + [discrim_net]))
np.savez(save_path + "/errors_supervised_best.npz",
zip(*train_monitored), zip(*valid_monitored))
else:
patience += 1
np.savez(
os.path.join(save_path, 'model_last.npz'),
*lasagne.layers.get_all_param_values(
filter(None, nets) + [discrim_net]))
np.savez(save_path + "/errors_supervised_last.npz",
zip(*train_monitored), zip(*valid_monitored))
# End training
if patience == max_patience or epoch == num_epochs - 1:
print("Ending training")
# Load best model
if not os.path.exists(save_path + '/model_best.npz'):
print("No saved model to be tested and/or generate"
" the embedding !")
else:
with np.load(save_path + '/model_best.npz', ) as f:
param_values = [
f['arr_%d' % i] for i in range(len(f.files))
]
lasagne.layers.set_all_param_values(
filter(None, nets) + [discrim_net], param_values)
# Training set results
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
# Validation set results
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
# Test set results
if y_test is not None:
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
batch_size,
shuffle=False)
test_err = mlh.monitoring(test_minibatches, "test", val_fn,
monitor_labels, prec_recall_cutoff)
else:
for minibatch in mlh.iterate_testbatches(x_test,
batch_size,
shuffle=False):
test_predictions = []
test_predictions += [predict(minibatch)]
np.savez(os.path.join(save_path, 'test_predictions.npz'),
test_predictions)
# Stop
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() -
start_time))
break
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() - start_time))
# Anneal the learning rate
lr.set_value(float(lr.get_value() * learning_rate_annealing))
# Print all final errors for train, validation and test
print("Training time:\t\t\t{:.3f}s".format(time.time() - start_training))
# Copy files to loadpath
if save_path != save_copy:
print('Copying model and other training files to {}'.format(save_copy))
copy_tree(save_path, save_copy)
def execute(dataset,
learning_rate=0.00001,
alpha=0.,
beta=1.,
lmd=0.,
encoder_units=[1024, 512, 256],
num_epochs=500,
which_fold=1,
save_path=None,
save_copy=None,
dataset_path=None):
# Reading dataset
print("Loading data")
x_unsup = mlh.load_data(dataset,
dataset_path,
None,
which_fold=which_fold,
keep_labels=1.0,
missing_labels_val=-1.0,
embedding_input='bin',
transpose=True)
x_train = x_unsup[0][0]
x_valid = x_unsup[1][0]
n_features = x_train.shape[1]
exp_name = "learn_gene_vector_h"
for e in encoder_units:
exp_name += ('-' + str(e))
exp_name += '_a-' + str(alpha)
exp_name += '_b-' + str(beta)
exp_name += '_l-' + str(lmd)
exp_name += '_lr-' + str(learning_rate)
save_path = os.path.join(save_path, exp_name)
save_copy = os.path.join(save_copy, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(save_copy):
os.makedirs(save_copy)
# Prepare Theano variables for inputs and targets
input_var = T.matrix('input')
target_var = T.matrix('target')
target_reconst = T.matrix('target')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
lmd = 0.0001 # weight decay coeff
num_epochs = 200
# there arent really any epochs as we are using a generator with random
# sampling from dataset. This is for compat.
batches_per_epoch = 1000
batch_size = 128
# building network
encoder = InputLayer((batch_size, n_features), input_var)
# building the encoder and decoder
for i in range(len(encoder_units)):
encoder = DenseLayer(encoder,
num_units=encoder_units[i],
nonlinearity=rectify)
params = lasagne.layers.get_all_params(encoder, trainable=True)
monitor_labels = []
val_outputs = []
nets = [encoder]
if alpha > 0:
decoder_units = encoder_units[::-1][1:]
decoder = encoder
for i in range(len(decoder_units)):
decoder = DenseLayer(decoder,
num_units=decoder_units[i],
nonlinearity=rectify)
decoder = DenseLayer(decoder,
num_units=n_features,
nonlinearity=sigmoid)
prediction_reconst = lasagne.layers.get_output(decoder)
# Reconstruction error
loss_reconst = lasagne.objectives.binary_crossentropy(
prediction_reconst, target_reconst).mean()
params += lasagne.layers.get_all_params(decoder, trainable=True)
monitor_labels += ["reconst."]
val_outputs += [loss_reconst]
nets += [decoder]
else:
loss_reconst = 0
if beta > 0:
predictor_laysize = [encoder_units[-1]] * 4
predictor = encoder
for i in range(len(predictor_laysize)):
predictor = DenseLayer(predictor,
num_units=predictor_laysize[i],
nonlinearity=rectify)
predictor = DenseLayer(predictor, num_units=2, nonlinearity=sigmoid)
prediction_var = lasagne.layers.get_output(predictor)
# w2v error
loss_pred = lasagne.objectives.binary_crossentropy(
prediction_var, target_var).mean()
params += lasagne.layers.get_all_params(predictor, trainable=True)
monitor_labels += ["pred."]
val_outputs += [loss_pred]
nets += [predictor]
else:
loss_pred = 0
# Combine losses
loss = alpha * loss_reconst + beta * loss_pred
# applying weight decay
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
# loss = loss + lmd*l2_penalty
val_outputs += [loss]
monitor_labels += ['loss']
# Some variables
max_patience = 100
patience = 0
train_monitored = []
valid_monitored = []
train_loss = []
updates = lasagne.updates.rmsprop(loss, params, learning_rate=lr)
inputs = [input_var, target_var, target_reconst]
# Compile training function
print "Compiling training function"
train_fn = theano.function(inputs,
loss,
updates=updates,
on_unused_input='ignore')
val_fn = theano.function(inputs, [val_outputs[0]] + val_outputs,
on_unused_input='ignore')
start_training = time.time()
print "training start time: {}".format(start_training)
# data_gen = data_generator(x_train, batch_size)
print "Starting training"
for epoch in range(num_epochs):
start_time = time.time()
print("Epoch {} of {}".format(epoch + 1, num_epochs))
nb_minibatches = 0
loss_epoch = 0
for x, y, target_reconst_val in data_generator(x_train, batch_size):
loss_epoch += train_fn(x, y, target_reconst_val)
nb_minibatches += 1
loss_epoch /= nb_minibatches
train_loss += [loss_epoch]
# Monitoring on the training set
train_minibatches = data_generator(x_train, batch_size)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, 0)
train_monitored += [train_err]
# Monitoring on the validation set
valid_minibatches = data_generator(x_valid, batch_size)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, 0)
valid_monitored += [valid_err]
early_stop_criterion = 'loss'
early_stop_val = valid_err[monitor_labels.index(early_stop_criterion)]
# Early stopping
if epoch == 0:
best_valid = early_stop_val
elif early_stop_val < best_valid and early_stop_criterion == 'loss':
best_valid = early_stop_val
patience = 0
# Save stuff
np.savez(save_path + '/model_snp2vec_best.npz',
*lasagne.layers.get_all_param_values(nets))
np.savez(save_path + "/errors_snp2vec_best.npz",
zip(*train_monitored), zip(*valid_monitored))
else:
patience += 1
np.savez(os.path.join(save_path, 'model_snp2vec_last.npz'),
*lasagne.layers.get_all_param_values(nets))
np.savez(save_path + "/errors_snp2vec_last.npz",
zip(*train_monitored), zip(*valid_monitored))
# End training
if (patience == max_patience) or (epoch == num_epochs - 1):
print("Ending training")
# Load best model
if not os.path.exists(save_path + '/model_snp2vec_best.npz'):
print(
"No saved model to be tested and/or generate"
" the embedding !")
else:
with np.load(save_path + '/model_snp2vec_best.npz') as f:
param_values = [
f['arr_%d' % i] for i in range(len(f.files))
]
lasagne.layers.set_all_param_values(nets, param_values)
# Training set results
train_minibatches = data_generator(x_train, batch_size)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, 0)
# Validation set results
valid_minibatches = data_generator(x_valid, batch_size)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, 0)
# Stop
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() -
start_time))
break
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() - start_time))
# Copy files to loadpath
if save_path != save_copy:
print('Copying model and other training files to {}'.format(save_copy))
copy_tree(save_path, save_copy)
def execute(
dataset,
n_hidden_u,
n_hidden_t_enc,
n_hidden_t_dec,
n_hidden_s,
embedding_source=None,
num_epochs=500,
learning_rate=.001,
learning_rate_annealing=1.0,
alpha=1,
beta=1,
gamma=1,
lmd=.0001,
disc_nonlinearity="sigmoid",
encoder_net_init=0.2,
decoder_net_init=0.2,
keep_labels=1.0,
prec_recall_cutoff=True,
missing_labels_val=-1.0,
which_fold=0,
early_stop_criterion='loss_sup_det',
embedding_input='raw',
save_path='/Tmp/' + os.environ["USER"] +
'/savepath/', # a default value was needed?
save_copy='/Tmp/' + os.environ["USER"] + '/savecopy/',
dataset_path='/Tmp/' + os.environ["USER"] + '/datasets/',
resume=False,
exp_name='',
random_proj=0):
# Load the dataset
print("Loading data")
x_train, y_train, x_valid, y_valid, x_test, y_test, \
x_unsup, training_labels = mlh.load_data(
dataset, dataset_path, embedding_source,
which_fold=which_fold, keep_labels=keep_labels,
missing_labels_val=missing_labels_val,
embedding_input=embedding_input)
if x_unsup is not None:
n_samples_unsup = x_unsup.shape[1]
else:
n_samples_unsup = 0
# Extract required information from data
n_samples, n_feats = x_train.shape
print("Number of features : ", n_feats)
print("Glorot init : ", 2.0 / (n_feats + n_hidden_t_enc[-1]))
n_targets = y_train.shape[1]
# Set some variables
batch_size = 128
beta = gamma if (gamma == 0) else beta
# Preparing folder to save stuff
if embedding_source is None:
embedding_name = embedding_input
else:
embedding_name = embedding_source.replace("_", "").split(".")[0]
exp_name += embedding_name.rsplit('/', 1)[::-1][0] + '_'
exp_name += 'final_'
exp_name += mlh.define_exp_name(keep_labels, alpha, beta, gamma, lmd,
n_hidden_u, n_hidden_t_enc, n_hidden_t_dec,
n_hidden_s, which_fold, embedding_input,
learning_rate, decoder_net_init,
encoder_net_init, early_stop_criterion,
learning_rate_annealing)
print("Experiment: " + exp_name)
save_path = os.path.join(save_path, dataset, exp_name)
save_copy = os.path.join(save_copy, dataset, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(save_copy):
os.makedirs(save_copy)
# Prepare Theano variables for inputs and targets
input_var_sup = T.matrix('input_sup')
input_var_unsup = theano.shared(x_unsup, 'input_unsup') # x_unsup TBD
target_var_sup = T.matrix('target_sup')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
# Build model
print("Building model")
# Some checkings
# assert len(n_hidden_u) > 0
assert len(n_hidden_t_enc) > 0
assert len(n_hidden_t_dec) > 0
assert n_hidden_t_dec[-1] == n_hidden_t_enc[-1]
# Build feature embedding networks (encoding and decoding if gamma > 0)
nets, embeddings, pred_feat_emb = mh.build_feat_emb_nets(
embedding_source, n_feats, n_samples_unsup, input_var_unsup,
n_hidden_u, n_hidden_t_enc, n_hidden_t_dec, gamma, encoder_net_init,
decoder_net_init, save_path, random_proj)
# Build feature embedding reconstruction networks (if alpha > 0, beta > 0)
nets += mh.build_feat_emb_reconst_nets(
[alpha, beta], n_samples_unsup, n_hidden_u,
[n_hidden_t_enc, n_hidden_t_dec], nets,
[encoder_net_init, decoder_net_init])
# Supervised network
discrim_net, hidden_rep = mh.build_discrim_net(
batch_size, n_feats, input_var_sup, n_hidden_t_enc, n_hidden_s,
embeddings[0], disc_nonlinearity, n_targets)
# Reconstruct network
nets += [
mh.build_reconst_net(hidden_rep,
embeddings[1] if len(embeddings) > 1 else None,
n_feats, gamma)
]
# Load weights if we are resuming job
if resume:
# Load best model
with np.load(os.path.join(save_path, 'model_feat_sel_last.npz')) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
nlayers = len(
lasagne.layers.get_all_params(filter(None, nets) + [discrim_net]))
lasagne.layers.set_all_param_values(
filter(None, nets) + [discrim_net], param_values[:nlayers])
print("Building and compiling training functions")
# Build and compile training functions
predictions, predictions_det = mh.define_predictions(nets, start=2)
prediction_sup, prediction_sup_det = mh.define_predictions([discrim_net])
prediction_sup = prediction_sup[0]
prediction_sup_det = prediction_sup_det[0]
# Define losses
# reconstruction losses
reconst_losses, reconst_losses_det = mh.define_reconst_losses(
predictions, predictions_det,
[input_var_unsup, input_var_unsup, input_var_sup])
# supervised loss
sup_loss, sup_loss_det = mh.define_sup_loss(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
keep_labels, target_var_sup,
missing_labels_val)
# Define inputs
inputs = [input_var_sup, target_var_sup]
# Define parameters
params = lasagne.layers.get_all_params([discrim_net] + filter(None, nets),
trainable=True)
params_to_freeze= \
lasagne.layers.get_all_params(filter(None, nets), trainable=False)
print('Number of params discrim: ' + str(len(params)))
print('Number of params to freeze: ' + str(len(params_to_freeze)))
for p in params_to_freeze:
new_params = [el for el in params if el != p]
params = new_params
print('Number of params to update: ' + str(len(params)))
# Combine losses
loss = sup_loss + alpha*reconst_losses[0] + beta*reconst_losses[1] + \
gamma*reconst_losses[2]
loss_det = sup_loss_det + alpha*reconst_losses_det[0] + \
beta*reconst_losses_det[1] + gamma*reconst_losses_det[2]
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
loss_det = loss_det + lmd * l2_penalty
# Compute network updates
updates = lasagne.updates.rmsprop(loss, params, learning_rate=lr)
# updates = lasagne.updates.sgd(loss,
# params,
# learning_rate=lr)
# updates = lasagne.updates.momentum(loss, params,
# learning_rate=lr, momentum=0.0)
# Apply norm constraints on the weights
for k in updates.keys():
if updates[k].ndim == 2:
updates[k] = lasagne.updates.norm_constraint(updates[k], 1.0)
# Compile training function
train_fn = theano.function(inputs,
loss,
updates=updates,
on_unused_input='ignore')
# Monitoring Labels
monitor_labels = [
"reconst. feat. W_enc", "reconst. feat. W_dec", "reconst. loss"
]
monitor_labels = [
i for i, j in zip(monitor_labels, reconst_losses) if j != 0
]
monitor_labels += ["feat. W_enc. mean", "feat. W_enc var"]
monitor_labels += ["feat. W_dec. mean", "feat. W_dec var"] if \
(embeddings[1] is not None) else []
monitor_labels += ["loss. sup.", "total loss"]
# Build and compile test function
val_outputs = reconst_losses_det
val_outputs = [i for i, j in zip(val_outputs, reconst_losses) if j != 0]
val_outputs += [embeddings[0].mean(), embeddings[0].var()]
val_outputs += [embeddings[1].mean(), embeddings[1].var()] if \
(embeddings[1] is not None) else []
val_outputs += [sup_loss_det, loss_det]
# Compute accuracy and add it to monitoring list
test_acc, test_pred = mh.define_test_functions(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
target_var_sup)
monitor_labels.append("accuracy")
val_outputs.append(test_acc)
# Compile prediction function
predict = theano.function([input_var_sup], test_pred)
# Compile validation function
val_fn = theano.function(inputs, [prediction_sup_det] + val_outputs,
on_unused_input='ignore')
# Finally, launch the training loop.
print("Starting training...")
# Some variables
max_patience = 100
patience = 0
train_monitored = []
valid_monitored = []
train_loss = []
# Pre-training monitoring
print("Epoch 0 of {}".format(num_epochs))
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
# Training loop
start_training = time.time()
for epoch in range(num_epochs):
start_time = time.time()
print("Epoch {} of {}".format(epoch + 1, num_epochs))
nb_minibatches = 0
loss_epoch = 0
# Train pass
for batch in mlh.iterate_minibatches(x_train,
training_labels,
batch_size,
shuffle=True):
loss_epoch += train_fn(*batch)
nb_minibatches += 1
loss_epoch /= nb_minibatches
train_loss += [loss_epoch]
# Monitoring on the training set
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
train_monitored += [train_err]
# Monitoring on the validation set
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
valid_monitored += [valid_err]
try:
early_stop_val = valid_err[monitor_labels.index(
early_stop_criterion)]
except:
raise ValueError("There is no monitored value by the name of %s" %
early_stop_criterion)
# Early stopping
if epoch == 0:
best_valid = early_stop_val
elif (early_stop_val > best_valid and early_stop_criterion == 'accuracy') or \
(early_stop_val < best_valid and early_stop_criterion == 'loss. sup.'):
best_valid = early_stop_val
patience = 0
# Save stuff
np.savez(
os.path.join(save_path, 'model_feat_sel_best.npz'),
*lasagne.layers.get_all_param_values(
filter(None, nets) + [discrim_net]))
np.savez(save_path + "/errors_supervised_best.npz",
zip(*train_monitored), zip(*valid_monitored))
# Monitor on the test set now because sometimes the saving doesn't
# go well and there isn't a model to load at the end of training
if y_test is not None:
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
138,
shuffle=False)
test_err = mlh.monitoring(test_minibatches, "test", val_fn,
monitor_labels, prec_recall_cutoff)
else:
patience += 1
# Save stuff
np.savez(
os.path.join(save_path, 'model_feat_sel_last.npz'),
*lasagne.layers.get_all_param_values(
filter(None, nets) + [discrim_net]))
np.savez(save_path + "/errors_supervised_last.npz",
zip(*train_monitored), zip(*valid_monitored))
# End training
if patience == max_patience or epoch == num_epochs - 1:
print("Ending training")
# Load best model
with np.load(os.path.join(save_path,
'model_feat_sel_best.npz')) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
nlayers = len(
lasagne.layers.get_all_params(
filter(None, nets) + [discrim_net]))
lasagne.layers.set_all_param_values(
filter(None, nets) + [discrim_net], param_values[:nlayers])
if embedding_source is None:
# Save embedding
pred = pred_feat_emb()
np.savez(os.path.join(save_path, 'feature_embedding.npz'),
pred)
# Training set results
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
# Validation set results
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
# Test set results
if y_test is not None:
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
138,
shuffle=False)
test_err = mlh.monitoring(test_minibatches, "test", val_fn,
monitor_labels, prec_recall_cutoff)
np.savez(os.path.join(save_path, 'final_errors.npz'), test_err)
else:
for minibatch in mlh.iterate_testbatches(x_test,
138,
shuffle=False):
test_predictions = []
test_predictions += [predict(minibatch)]
np.savez(os.path.join(save_path, 'test_predictions.npz'),
test_predictions)
# Stop
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() -
start_time))
break
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() - start_time))
# Anneal the learning rate
lr.set_value(float(lr.get_value() * learning_rate_annealing))
# Print and save all final errors for train, validation and test
print("Training time:\t\t\t{:.3f}s".format(time.time() - start_training))
print("test_err:", test_err)
# Copy files to loadpath
if save_path != save_copy:
print('Copying model and other training files to {}'.format(save_copy))
copy_tree(save_path, save_copy)
def execute(dataset,
learning_rate=0.00001,
learning_rate_annealing=1.0,
alpha=0.,
beta=1.,
lmd=0.,
encoder_units=[1024, 512, 256],
num_epochs=500,
which_fold=1,
save_path=None,
save_copy=None,
dataset_path=None,
num_fully_connected=0,
exp_name='',
init_args=None):
# Reading dataset
print("Loading data")
x_unsup = mlh.load_data(dataset,
dataset_path,
None,
which_fold=which_fold,
keep_labels=1.0,
missing_labels_val=-1.0,
embedding_input='bin',
transpose=True)
x_train = x_unsup[0][0]
x_valid = x_unsup[1][0]
n_features = x_train.shape[1]
exp_name += "learn_gene_vector_h"
for e in encoder_units:
exp_name += ('-' + str(e))
exp_name += '_a-' + str(alpha)
exp_name += '_b-' + str(beta)
# exp_name += '_g-' + str(gamma)
exp_name += '_l-' + str(lmd)
exp_name += '_lr-' + str(learning_rate)
save_path = os.path.join(save_path, exp_name)
save_copy = os.path.join(save_copy, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(save_copy):
os.makedirs(save_copy)
# Prepare Theano variables for inputs and targets
input_var = T.matrix('input')
target_var = T.matrix('target')
target_reconst = T.matrix('target')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
batch_size = 128
# building network
encoder = InputLayer((batch_size, n_features), input_var)
# building the encoder and decoder
#import pdb; pdb.set_trace()
for i in range(len(encoder_units)):
encoder = DenseLayer(
encoder,
num_units=encoder_units[i],
W=HeNormal('relu'),
nonlinearity=rectify) # if i < len(encoder_units)-1 else linear)
embedding = lasagne.layers.get_output(encoder)
params = lasagne.layers.get_all_params(encoder, trainable=True)
monitor_labels = ["embedding min", "embedding max"]
val_outputs = [embedding.min(), embedding.max()]
nets = [encoder]
if alpha > 0:
decoder_units = encoder_units[::-1][1:]
print(decoder_units)
decoder = encoder
for i in range(len(decoder_units)):
decoder = DenseLayer(decoder,
num_units=decoder_units[i],
W=HeNormal('relu'),
nonlinearity=rectify)
decoder = DenseLayer(decoder,
num_units=n_features,
W=convert_initialization(
init_args["decoder_init"],
nonlinearity="sigmoid"),
nonlinearity=sigmoid)
prediction_reconst = lasagne.layers.get_output(decoder)
# Reconstruction error
loss_reconst = lasagne.objectives.binary_crossentropy(
prediction_reconst, target_reconst).mean()
# loss_reconst = mh.define_sampled_mean_bincrossentropy(
# prediction_reconst, target_reconst, gamma=gamma)
#loss_reconst = mh.dice_coef_loss(
# target_reconst, prediction_reconst).mean()
accuracy = T.eq(T.gt(prediction_reconst, 0.5), target_reconst).mean()
params += lasagne.layers.get_all_params(decoder, trainable=True)
monitor_labels += ["reconst. loss", "reconst. accuracy"]
val_outputs += [loss_reconst, accuracy]
nets += [decoder]
# sparsity_reconst = gamma * l1(prediction_reconst)
# roh = input_var.mean(0)
# sparsity_reconst = ((roh * T.log(roh / (prediction_reconst.mean(0)+1e-8))) +\
# ((1 - roh) * T.log((1 - roh) / (1 - prediction_reconst + 1e-8)))).sum()
else:
loss_reconst = 0
# sparsity_reconst = 0
if beta > 0:
predictor_laysize = [encoder_units[-1]] * num_fully_connected
predictor = encoder
for i in range(len(predictor_laysize)):
predictor = DenseLayer(predictor,
num_units=predictor_laysize[i],
nonlinearity=rectify,
W=convert_initialization(
init_args["predictor_init"],
nonlinearity="relu"))
predictor = DenseLayer(predictor,
num_units=2,
nonlinearity=sigmoid,
W=convert_initialization(
init_args["predictor_init"],
nonlinearity="sigmoid"))
prediction_var = lasagne.layers.get_output(predictor)
# w2v error
# loss_pred = lasagne.objectives.binary_crossentropy(
# prediction_var, target_var
# ).mean()
loss_pred = mh.dice_coef_loss(target_var, prediction_var).mean()
accuracy = T.eq(T.gt(prediction_var, 0.5), target_var).mean()
params += lasagne.layers.get_all_params(predictor, trainable=True)
monitor_labels += ["pred. loss", "pred. accuracy"]
val_outputs += [loss_pred, accuracy]
nets += [predictor]
# sparsity_pred = gamma * l1(prediction_var)
# roh = 0.05
# sparsity_pred = ((roh * T.log(roh / prediction_pred.mean(0))) +\
# ((1 - roh) * T.log((1 - roh) / (1 - prediction_pred)))).sum()
else:
loss_pred = 0
# sparsity_pred = 0
# Combine losses
loss = alpha * loss_reconst + beta * loss_pred # sparsity_pred # + sparsity_reconst
# applying weight decay
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
# loss = loss + lmd*l2_penalty
val_outputs += [loss]
monitor_labels += ['loss']
# Some variables
max_patience = 100
patience = 0
train_monitored = []
valid_monitored = []
train_loss = []
updates = lasagne.updates.adam(loss, params, learning_rate=lr)
for k in updates.keys():
if updates[k].ndim == 2:
updates[k] = lasagne.updates.norm_constraint(updates[k], 1.0)
inputs = [input_var, target_var, target_reconst]
# Compile training function
print "Compiling training function"
train_fn = theano.function(inputs,
loss,
updates=updates,
on_unused_input='ignore')
val_fn = theano.function(inputs, [val_outputs[0]] + val_outputs,
on_unused_input='ignore')
if alpha > 0:
pred_fn = theano.function([input_var], prediction_reconst)
start_training = time.time()
# data_gen = data_generator(x_train, batch_size)
print "Starting training"
for epoch in range(num_epochs):
start_time = time.time()
print("Epoch {} of {}".format(epoch + 1, num_epochs))
nb_minibatches = 0
loss_epoch = 0
for x, y, target_reconst_val in data_generator(x_train,
batch_size,
shuffle=True):
loss_epoch += train_fn(x, y, target_reconst_val)
nb_minibatches += 1
if alpha > 0:
pr = pred_fn(x)
print('min pr:' + str(pr.min()))
print('max pr:' + str(pr.max()))
print('mean pr:' + str(pr.mean()))
loss_epoch /= nb_minibatches
train_loss += [loss_epoch]
# Monitoring on the training set
train_minibatches = data_generator(x_train, batch_size)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, 0)
train_monitored += [train_err]
# Monitoring on the validation set
valid_minibatches = data_generator(x_valid, batch_size)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, 0)
valid_monitored += [valid_err]
early_stop_criterion = 'loss'
early_stop_val = valid_err[monitor_labels.index(early_stop_criterion)]
# Early stopping
if epoch == 0:
best_valid = early_stop_val
elif early_stop_val < best_valid and early_stop_criterion == 'loss':
best_valid = early_stop_val
patience = 0
# Save stuff
np.savez(save_path + '/model_snp2vec_best.npz',
*lasagne.layers.get_all_param_values(nets))
np.savez(save_path + "/errors_snp2vec_best.npz",
zip(*train_monitored), zip(*valid_monitored))
else:
patience += 1
np.savez(os.path.join(save_path, 'model_snp2vec_last.npz'),
*lasagne.layers.get_all_param_values(nets))
np.savez(save_path + "/errors_snp2vec_last.npz",
zip(*train_monitored), zip(*valid_monitored))
# End training
if (patience == max_patience) or (epoch == num_epochs - 1):
print("Ending training")
# Load best model
if not os.path.exists(save_path + '/model_snp2vec_best.npz'):
print(
"No saved model to be tested and/or generate"
" the embedding !")
else:
with np.load(save_path + '/model_snp2vec_best.npz') as f:
param_values = [
f['arr_%d' % i] for i in range(len(f.files))
]
lasagne.layers.set_all_param_values(nets, param_values)
# Training set results
train_minibatches = data_generator(x_train, batch_size)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, 0)
# Validation set results
valid_minibatches = data_generator(x_valid, batch_size)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, 0)
# Stop
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() -
start_time))
break
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() - start_time))
# Anneal the learning rate
lr.set_value(float(lr.get_value() * learning_rate_annealing))
# Copy files to loadpath
if save_path != save_copy:
print('Copying model and other training files to {}'.format(save_copy))
copy_tree(save_path, save_copy)
def reset():
if any(np.isnan(scale.get_value()) for scale in scales):
for scale in scales:
scale.set_value(1.)
for l in l_hiddens:
l.b.set_value(Constant()(l.b.get_value().shape))
l.W.set_value(Orthogonal()(l.W.get_value().shape))
l_out.b.set_value(Constant()(l_out.b.get_value().shape))
l_out.W.set_value(Orthogonal()(l_out.W.get_value().shape))
for p in (p for u in (updates_ada, updates_other, updates_scal) for p in u
if p not in get_all_params(l_out)):
p.set_value(Constant()(p.get_value().shape))
chunky_l2 = apply_penalty(get_all_params(l_out, regularizable=True), l2) - l2(
l_hiddens[0].W) + l2(l_hiddens[0].W / T.reshape(vscale, (206279, 1)))
chunky_l1 = apply_penalty(get_all_params(l_out, regularizable=True), l1) - l1(
l_hiddens[0].W) + l1(l_hiddens[0].W / T.reshape(vscale, (206279, 1)))
simple_l2 = apply_penalty(get_all_params(l_out, regularizable=True), l2)
#l_out2 = DenseLayer(dropout(l_hiddens2[-1]), num_units=y.shape[1])
#l_out = lasagne.layers.NonlinearityLayer(lasagne.layers.ElemwiseSumLayer((l_out1,l_out2),.5), softmax)
#categorical_crossentropy(get_output(l_out)[train_indice])
target = T.fmatrix(name="target")
#f=theano.function([l_in.input_var],get_output(l_out),allow_input_downcast=True)
#f(X[0,:].toarray())
loss = categorical_crossentropy(get_output(l_out), target).mean()
# train_loss_smoo=categorical_crossentropy(get_output(l_out,deterministic=True)[train_indices,],target[train_indices,]).mean()
def __init__(self,
W=None,
W_path=None,
K=300,
num_hidden=256,
batch_size=None,
grad_clip=100.,
max_sent_len=200,
num_classes=2,
**kwargs):
W = W
V = len(W)
K = int(K)
num_hidden = int(num_hidden)
batch_size = int(batch_size)
grad_clip = int(grad_clip)
max_seq_len = int(max_sent_len)
max_post_len = int(kwargs["max_post_len"])
max_len = max(max_seq_len, max_post_len)
max_seq_len = max_len
max_post_len = max_len
num_classes = int(num_classes)
''' Boolean on and/or sentence and word level attention'''
''' to use context or not'''
separate_attention_context_sents = str_to_bool(
kwargs["separate_attention_context"])
separate_attention_response_sents = str_to_bool(
kwargs["separate_attention_response"])
separate_attention_context_words = str_to_bool(
kwargs["separate_attention_context_words"])
separate_attention_response_words = str_to_bool(
kwargs["separate_attention_response_words"])
print("separate_attention_context_sentence is : {}\n".format(
separate_attention_context_sents))
print("separate_attention_response_sentence is : {}\n".format(
separate_attention_response_sents))
print("separate_attention_context_words is : {}\n".format(
separate_attention_context_words))
print("separate_attention_response_words is : {}\n".format(
separate_attention_response_words))
#B x S x N tensor of batches of context
idxs_context = T.itensor3('idxs_context') #imatrix, i = int
#B x S x N matrix
mask_context_words = T.itensor3('mask_context_words')
#B x S matrix
mask_context_sents = T.imatrix('mask_context_sents')
#B x S x N tensor of batches of responses
idxs_response = T.itensor3('idxs_response') #imatrix, i = int
#B x S X N matrix for words
mask_response_words = T.itensor3('mask_response_words')
#B x S matrix for sentences
mask_response_sents = T.imatrix('mask_response_sents')
#B-long vector
gold = T.ivector('y')
# dropout
dropout_val = T.scalar('p_dropout')
#lambda, cost
lambda_cost = T.scalar('lambda_w')
#biases
biases_cost = T.matrix('biases')
#weights
weights = T.ivector('weights')
''' check biases'''
biases_present = False
if biases_present:
lstm_biases = lasagne.layers.InputLayer(shape=(None, 1),
input_var=biases_cost)
''' building the context layer via function'''
if separate_attention_context_sents:
lstm_hidden_context,lstm_attn_words_context,lstm_attn_sents_context = self.buildThePostLayer(idxs_context,mask_context_words,\
mask_context_sents,\
separate_attention_context_words,\
separate_attention_context_sents,num_hidden,grad_clip,V,K,W,max_post_len,max_sent_len)
''' do the same for response layer'''
if separate_attention_response_sents:
lstm_hidden_response,lstm_attn_words_response,lstm_attn_sents_response = self.buildThePostLayer(idxs_response,mask_response_words,\
mask_context_sents,\
separate_attention_context_words,\
separate_attention_context_sents,num_hidden,grad_clip,V,K,W,max_post_len,max_sent_len)
print('...')
print('finished compiling...')
''' prepare the final network of connections now'''
if separate_attention_response_sents and separate_attention_context_sents:
output, network = self.buildNetwork(lstm_hidden_context,
lstm_hidden_response,
num_classes)
elif separate_attention_context_sents:
output, network = self.buildNetworkOnlyContext(
lstm_hidden_context, num_classes)
'''Define objective function (cost) to minimize mean cross-entropy error'''
params = lasagne.layers.get_all_params(network)
cost = lasagne.objectives.categorical_crossentropy(output, gold).mean()
lambda_w = .000001
cost += lambda_w * apply_penalty(params, l2)
grad_updates = lasagne.updates.adam(cost, params)
test_output = lasagne.layers.get_output(network, deterministic=True)
val_cost_fn = lasagne.objectives.categorical_crossentropy(
test_output, gold).mean()
preds = T.argmax(test_output, axis=1)
val_acc_fn = T.mean(T.eq(preds, gold), dtype=theano.config.floatX)
if separate_attention_context_sents and separate_attention_response_sents:
self.val_fn = theano.function([idxs_context, mask_context_words, mask_context_sents, idxs_response, \
mask_response_words, mask_response_sents, gold], [val_cost_fn, val_acc_fn, preds],
allow_input_downcast=True,on_unused_input='warn')
# Compile train objective
print "Compiling training, testing, prediction functions"
self.train = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents,\
idxs_response, mask_response_words, mask_response_sents, gold],\
outputs = cost, updates = grad_updates, allow_input_downcast=True,on_unused_input='warn')
self.test = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents,idxs_response,\
mask_response_words, mask_response_sents, gold],\
outputs = val_acc_fn,allow_input_downcast=True,on_unused_input='warn')
self.pred = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents, \
idxs_response, mask_response_words, mask_response_sents],\
outputs = preds,allow_input_downcast=True,on_unused_input='warn')
elif separate_attention_context_sents:
self.val_fn = theano.function([idxs_context, mask_context_words, mask_context_sents, \
gold], [val_cost_fn, val_acc_fn, preds],
allow_input_downcast=True,on_unused_input='warn')
print "Compiling training, testing, prediction functions"
self.train = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents,\
gold],\
outputs = cost, updates = grad_updates, allow_input_downcast=True,on_unused_input='warn')
self.test = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents,\
gold],\
outputs = val_acc_fn,allow_input_downcast=True,on_unused_input='warn')
self.pred = theano.function(inputs = [idxs_context, mask_context_words, mask_context_sents \
],\
outputs = preds,allow_input_downcast=True,on_unused_input='warn')
if separate_attention_response_sents:
sentence_attention = lasagne.layers.get_output(
lstm_attn_sents_response)
#if add_biases:
# inputs = inputs[:-1]
self.sentence_attention_response = theano.function([idxs_context, mask_context_words,\
mask_context_sents,idxs_response, mask_response_words, mask_response_sents],
sentence_attention,
allow_input_downcast=True,
on_unused_input='warn')
if separate_attention_context_sents:
sentence_attention_context = lasagne.layers.get_output(
lstm_attn_sents_context)
#if add_biases:
# inputs = inputs[:-1]
self.sentence_attention_context = theano.function([idxs_context, mask_context_words,\
mask_context_sents,idxs_response, mask_response_words, mask_response_sents],
[sentence_attention_context, preds],
allow_input_downcast=True,
on_unused_input='warn')
if separate_attention_response_words:
sentence_attention_words = lasagne.layers.get_output(
lstm_attn_words_response)
#if add_biases:
# inputs = inputs[:-1]
self.sentence_attention_response_words = theano.function(
[
idxs_context, mask_context_words, mask_context_sents,
idxs_response, mask_response_words, mask_response_sents
],
sentence_attention_words,
allow_input_downcast=True,
on_unused_input='warn')
if separate_attention_context_words:
sentence_attention_context_words = lasagne.layers.get_output(
lstm_attn_words_context)
#if add_biases:
# inputs = inputs[:-1]
self.sentence_attention_context_words = theano.function(
[
idxs_context, mask_context_words, mask_context_sents,
idxs_response, mask_response_words, mask_response_sents
],
sentence_attention_context_words,
allow_input_downcast=True,
on_unused_input='warn')
'''compare the results with regular code and then add the bias etc. '''
# Get regularizable params
regularization_params = layers.get_all_params(unsupervised_graph, regularizable=True) + \
layers.get_all_params(supervised_graph, regularizable=True)
regularization_params = utils.unique(regularization_params)
# Creating loss functions
# Train loss has to take into account of labeled image or not
if run_parameters.unsupervised_cost_fun == 'squared_error':
loss1 = objectives.squared_error(reconstruction, input_var)
elif run_parameters.unsupervised_cost_fun == 'categorical_crossentropy':
loss1 = objectives.categorical_crossentropy(reconstruction, input_var)
if supervised_cost_fun == 'squared_error':
loss2 = objectives.squared_error(prediction, target_var) * repeat_col(labeled_var, 10)
elif supervised_cost_fun == 'categorical_crossentropy':
loss2 = objectives.categorical_crossentropy(prediction, target_var) * labeled_var.T
l2_penalties = regularization.apply_penalty(regularization_params, regularization.l2)
sparse_layers = get_all_sparse_layers(unsupervised_graph)
sparse_layers_output = layers.get_output(sparse_layers, deterministic=True)
if run_parameters.sparse_regularizer_type == 0:
sparse_regularizer = reduce(lambda x, y: x + T.clip((T.mean(abs(y)) - run_parameters.sparse_regularize_factor) *
y.size, 0, float('inf')),
sparse_layers_output, 0)
elif run_parameters.sparse_regularizer_type == 1:
sparse_regularizer = reduce(
lambda x, y: x + T.clip(T.mean(abs(y), axis=1) - run_parameters.sparse_regularize_factor,
0, float('inf')).sum() * y.shape[1],
sparse_layers_output, 0)
loss = losses_ratio[0] * loss1.mean() + \
losses_ratio[1] * loss2.mean() + \
losses_ratio[2] * l2_penalties.mean() + \
def execute(dataset,
n_hidden_u,
num_epochs=500,
learning_rate=.001,
learning_rate_annealing=1.0,
lmd=.0001,
embedding_input='raw',
which_fold=0,
save_path='/Tmp/$USER/feature_selection/newmodel/',
save_copy='/Tmp/$USER/feature_selection/newmodel/',
dataset_path='/Tmp/$USER/feature_selection/newmodel/'):
# Load the dataset
print("Loading data")
x_unsup = mlh.load_data(dataset,
dataset_path,
None,
which_fold=which_fold,
keep_labels=1.0,
missing_labels_val=-1.0,
embedding_input=embedding_input,
transpose=True)
x_train = x_unsup[0][0]
x_valid = x_unsup[1][0]
# Extract required information from data
n_row, n_col = x_train.shape
print('Data size ' + str(n_row) + 'x' + str(n_col))
# Set some variables
batch_size = 256
# Define experiment name
exp_name = 'pretrain_' + mlh.define_exp_name(
1., 0, 0, 0, lmd, n_hidden_u, [], [], [], which_fold, embedding_input,
learning_rate, 0, 0, 'reconst_loss', learning_rate_annealing)
print('Experiment: ' + exp_name)
# Preparing folder to save stuff
save_path = os.path.join(save_path, dataset, exp_name)
save_copy = os.path.join(save_copy, dataset, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
# Prepare Theano variables for inputs and targets
input_var = T.matrix('input_unsup')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
# Build model
print("Building model")
# Some checkings
assert len(n_hidden_u) > 0
# Build unsupervised network
encoder_net = InputLayer((None, n_col), input_var)
for out in n_hidden_u:
encoder_net = DenseLayer(encoder_net, num_units=out, nonlinearity=tanh)
encoder_net = DropoutLayer(encoder_net)
decoder_net = encoder_net
for i in range(len(n_hidden_u) - 2, -1, -1):
decoder_net = DenseLayer(decoder_net,
num_units=n_hidden_u[i],
nonlinearity=linear)
decoder_net = DropoutLayer(decoder_net)
decoder_net = DenseLayer(decoder_net, num_units=n_col, nonlinearity=linear)
if embedding_input == 'raw' or embedding_input == 'w2v':
final_nonlin = linear
elif embedding_input == 'bin':
final_nonlin = sigmoid
elif 'histo' in embedding_input:
final_nonlin = softmax
if embedding_input == 'histo3x26':
laySize = lasagne.layers.get_output(decoder_net).shape
decoder_net = ReshapeLayer(decoder_net, (laySize[0] * 26, 3))
decoder_net = NonlinearityLayer(decoder_net, nonlinearity=final_nonlin)
if embedding_input == 'histo3x26':
decoder_net = ReshapeLayer(decoder_net, (laySize[0], laySize[1]))
print("Building and compiling training functions")
# Build and compile training functions
predictions, predictions_det = mh.define_predictions(
[encoder_net, decoder_net], start=0)
prediction_sup, prediction_sup_det = mh.define_predictions(
[encoder_net, decoder_net], start=0)
# Define losses
# reconstruction losses
loss, loss_det = mh.define_loss(predictions[1], predictions_det[1],
input_var, embedding_input)
# Define parameters
params = lasagne.layers.get_all_params(decoder_net, trainable=True)
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
loss_det = loss_det + lmd * l2_penalty
# Compute network updates
updates = lasagne.updates.adam(loss, params, learning_rate=lr)
# updates = lasagne.updates.sgd(loss,
# params,
# learning_rate=lr)
# updates = lasagne.updates.momentum(loss, params,
# learning_rate=lr, momentum=0.0)
# Apply norm constraints on the weights
for k in updates.keys():
if updates[k].ndim == 2:
updates[k] = lasagne.updates.norm_constraint(updates[k], 1.0)
# Compile training function
train_fn = theano.function([input_var],
loss,
updates=updates,
on_unused_input='ignore')
# Expressions required for test
monitor_labels = ['loss']
val_outputs = [loss_det]
# Add some monitoring on the learned feature embedding
val_outputs += [
predictions[0].min(), predictions[0].mean(), predictions[0].max(),
predictions[0].var()
]
monitor_labels += [
"feat. emb. min", "feat. emb. mean", "feat. emb. max", "feat. emb. var"
]
# Compile validation function
val_fn = theano.function([input_var], val_outputs)
pred_feat_emb = theano.function([input_var], predictions_det[0])
# Finally, launch the training loop.
print("Starting training...")
# Some variables
max_patience = 100
patience = 0
train_monitored = []
valid_monitored = []
train_loss = []
nb_minibatches = n_row / batch_size
print("Nb of minibatches: " + str(nb_minibatches))
start_training = time.time()
for epoch in range(num_epochs):
start_time = time.time()
print("Epoch {} of {}".format(epoch + 1, num_epochs))
loss_epoch = 0
# Train pass
for batch in mlh.iterate_minibatches_unsup(x_train,
batch_size,
shuffle=True):
loss_epoch += train_fn(batch)
loss_epoch /= nb_minibatches
train_loss += [loss_epoch]
train_minibatches = mlh.iterate_minibatches_unsup(x_train,
batch_size,
shuffle=True)
train_err = mlh.monitoring(train_minibatches,
"train",
val_fn,
monitor_labels,
start=0)
train_monitored += [train_err]
# Validation pass
valid_minibatches = mlh.iterate_minibatches_unsup(x_valid,
batch_size,
shuffle=True)
valid_err = mlh.monitoring(valid_minibatches,
"valid",
val_fn,
monitor_labels,
start=0)
valid_monitored += [valid_err]
try:
early_stop_val = valid_err[monitor_labels.index('loss')]
except:
raise ValueError("There is no monitored value by the name of %s" %
early_stop_criterion)
# Eearly stopping
if epoch == 0:
best_valid = early_stop_val
elif early_stop_val < best_valid:
best_valid = early_stop_val
patience = 0
# Save stuff
np.savez(
os.path.join(save_path, 'model_enc_unsupervised_best.npz'),
*lasagne.layers.get_all_param_values(encoder_net))
np.savez(os.path.join(save_path, 'model_ae_unsupervised_best.npz'),
*lasagne.layers.get_all_param_values(encoder_net))
np.savez(os.path.join(save_path, "errors_unsupervised_best.npz"),
zip(*train_monitored), zip(*valid_monitored))
else:
patience += 1
# Save stuff
np.savez(
os.path.join(save_path, 'model_enc_unsupervised_last.npz'),
*lasagne.layers.get_all_param_values(encoder_net))
np.savez(os.path.join(save_path, 'model_ae_unsupervised_last.npz'),
*lasagne.layers.get_all_param_values(encoder_net))
np.savez(os.path.join(save_path, "errors_unsupervised_last.npz"),
zip(*train_monitored), zip(*valid_monitored))
# End training
if patience == max_patience or epoch == num_epochs - 1:
print(" Ending training")
# Load unsupervised best model
if not os.path.exists(save_path +
'/model_enc_unsupervised_best.npz'):
print("No saved model to be tested and/or generate"
" the embedding !")
else:
with np.load(save_path +
'/model_enc_unsupervised_best.npz', ) as f:
param_values = [
f['arr_%d' % i] for i in range(len(f.files))
]
lasagne.layers.set_all_param_values(
encoder_net, param_values)
# Save embedding
preds = []
for batch in mlh.iterate_minibatches_unsup(x_train,
1,
shuffle=False):
preds.append(pred_feat_emb(batch))
for batch in mlh.iterate_minibatches_unsup(x_valid,
1,
shuffle=False):
preds.append(pred_feat_emb(batch))
preds = np.vstack(preds)
np.savez(os.path.join(save_path, 'feature_embedding.npz'),
preds)
# Stop
print(" epoch time:\t\t\t{:.3f}s".format(time.time() - start_time))
break
print(" epoch time:\t\t\t{:.3f}s".format(time.time() - start_time))
# Anneal the learning rate
lr.set_value(float(lr.get_value() * learning_rate_annealing))
# Print all final errors for train, validation and test
print("Training time:\t\t\t{:.3f}s".format(time.time() - start_training))
# Copy files to loadpath
if save_path != save_copy:
print('Copying model and other training files to {}'.format(save_copy))
copy_tree(save_path, save_copy)
def execute(dataset,
n_hidden_u,
n_hidden_t_enc,
n_hidden_t_dec,
n_hidden_s,
embedding_source=histo_GenotypicFrequency_perclass,
additional_unsup_input=None,
num_epochs=500,
learning_rate=.001,
learning_rate_annealing=1.0,
alpha=1,
beta=1,
delta=1,
gamma=1,
lmd=.0001,
disc_nonlinearity="sigmoid",
encoder_net_init=0.2,
decoder_net_init=0.2,
optimizer="rmsprop",
max_patience=100,
batchnorm=0,
input_dropout=1.0,
embedding_noise=0.0,
keep_labels=1.0,
prec_recall_cutoff=True,
missing_labels_val=-1.0,
which_fold=0,
early_stop_criterion='loss_sup_det',
input_decoder_mode="regression",
save_path='/Users/Marie-Elyse/Downloads/embedding2',
save_copy='/Users/Marie-Elyse/Downloads/embedding2',
dataset_path='/Users/Marie-Elyse/Downloads/embedding2',
resume=False,
exp_name='',
random_proj=0,
bootstrap_snp_embeddings=0,
bootstrap_cutoff=0.9):
# Prepare embedding information :
# - If no embedding is specified, use the transposed input matrix
# - If a file is specified, use it's content as feature embeddings
# - Else (a embedding category like 'histo3x26' is provided), load a
# pregenerated embedding of the specified category
if embedding_source is None or embedding_source == "raw":
embedding_source = None
embedding_input = 'raw'
elif os.path.exists(embedding_source):
embedding_input = embedding_source
else:
embedding_input = embedding_source
embedding_source = os.path.join(
dataset_path, embedding_input + '_fold' + str(which_fold) + '.npy')
# Load the dataset
print("Loading data")
(x_train, y_train, exmpl_ids_train, x_valid, y_valid, exmpl_ids_valid,
x_test, y_test, exmpl_ids_test, x_unsup, training_labels, feature_names,
label_names) = mlh.load_data(dataset,
dataset_path,
embedding_source,
which_fold=which_fold,
keep_labels=keep_labels,
missing_labels_val=missing_labels_val,
embedding_input=embedding_input,
norm=False)
# Load the additional unsupervised data, if some is specified
if additional_unsup_input is not None:
print("Adding additional data to the model's unsupervised inputs")
paths = additional_unsup_input.split(";")
additional_unsup_data = [np.load(p) for p in paths]
print(x_unsup.shape)
x_unsup = np.hstack(additional_unsup_data + [x_unsup])
print(x_unsup.shape)
if x_unsup is not None:
n_samples_unsup = x_unsup.shape[1]
else:
n_samples_unsup = 0
original_x_train = x_train.copy()
original_x_valid = x_valid.copy()
original_x_test = x_test.copy()
# Change how the missing data values are encoded. Right now they are
# encoded as being the mean of the corresponding feature so that, after
# feature normalization, they will be 0s. However, this prevents us from
# transfering the minibatch data as int8 so we replace those values with -1s.
for i in range(x_train.shape[1]):
feature_mean = x_train[:, i].mean()
x_train[:, i] = mh.replace_arr_value(x_train[:, i], feature_mean, -1)
x_valid[:, i] = mh.replace_arr_value(x_valid[:, i], feature_mean, -1)
x_test[:, i] = mh.replace_arr_value(x_test[:, i], feature_mean, -1)
x_train = x_train.astype("int8")
x_valid = x_valid.astype("int8")
x_test = x_test.astype("int8")
# Normalize the input data. The mlh.load_data() function already offers
# this feature but we need to do it here so that we will have access to
# both the normalized and unnormalized input data.
norm_mus = original_x_train.mean(axis=0)
norm_sigmas = original_x_train.std(axis=0) + 1e-6
#x_train = (x_train - norm_mus[None, :]) / norm_sigmas[None, :]
#x_valid = (x_valid - norm_mus[None, :]) / norm_sigmas[None, :]
#x_test = (x_test - norm_mus[None, :]) / norm_sigmas[None, :]
#x_train *= (315345. / 553107)
#x_valid *= (315345. / 553107)
#x_test *= (315345. / 553107)
# Setup variables to build the right type of decoder bases on the value of
# `input_decoder_mode`
assert input_decoder_mode in ["regression", "classification"]
if input_decoder_mode == "regression":
# The size of the input reconstruction will be the same as the number
# of inputs
decoder_encoder_unit_ratio = 1
elif input_decoder_mode == "classification":
# # The size of the input reconstruction will be the N times larger as
# the number of inputs where N is the number of distinct discrete
# values that each input can take. For SNP input data with an additive
# coding scheme, N=3 because the 3 possible values are : {0, 1, 2}.
nb_discrete_vals_by_input = int(original_x_train.max() + 1)
decoder_encoder_unit_ratio = nb_discrete_vals_by_input
# Print baseline accuracy for the imputation of genes
print("Distribution of input values in valid: %f %f %f" %
((original_x_train == 0).mean(), (original_x_train == 1).mean(),
(original_x_train == 2).mean()))
print("Distribution of input values in test: %f %f %f" %
((original_x_test == 0).mean(), (original_x_test == 1).mean(),
(original_x_test == 2).mean()))
# Extract required information from data
n_samples, n_feats = x_train.shape
print("Number of features : ", n_feats)
print("Glorot init : ", 2.0 / (n_feats + n_hidden_t_enc[-1]))
n_targets = y_train.shape[1] if y_train.ndim == 2 else y_train.max() + 1
# Set some variables
batch_size = 138
beta = gamma if (gamma == 0) else beta
# Generate an name for the experiment based on the hyperparameters used
if embedding_source is None:
embedding_name = embedding_input
else:
embedding_name = embedding_source.replace("_", "").split(".")[0]
exp_name += embedding_name.rsplit('/', 1)[::-1][0] + '_'
exp_name += mlh.define_exp_name(
keep_labels, alpha, beta, gamma, lmd, n_hidden_u, n_hidden_t_enc,
n_hidden_t_dec, n_hidden_s, which_fold, learning_rate,
decoder_net_init, encoder_net_init, batchnorm, input_dropout,
embedding_noise, early_stop_criterion, learning_rate_annealing,
input_decoder_mode)
print("Experiment: " + exp_name)
# Ensure that the folders where the results of the experiment will be
# saved do exist. Create them if they don't.
save_path = os.path.join(save_path, dataset, exp_name)
save_copy = os.path.join(save_copy, dataset, exp_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(save_copy):
os.makedirs(save_copy)
# Prepare Theano variables for inputs and targets
input_var_sup = T.bmatrix('input_sup')
input_var_unsup = theano.shared(x_unsup, 'input_unsup') # x_unsup TBD
target_var_sup = T.matrix('target_sup')
lr = theano.shared(np.float32(learning_rate), 'learning_rate')
# Use the provided mus and sigmas to process the missing values and
# normalize the inputs
b_input_var_sup = input_var_sup.astype("float32")
normed_input_sup = (T.eq(b_input_var_sup, -1) * norm_mus +
T.neq(b_input_var_sup, -1) * b_input_var_sup)
normed_input_sup = (normed_input_sup - norm_mus) / norm_sigmas
reconst_target_sup = T.cast(input_var_sup, "int32")
# Build model
print("Building model")
# Some checkings
# assert len(n_hidden_u) > 0
assert len(n_hidden_t_enc) > 0
assert len(n_hidden_t_dec) > 0
assert n_hidden_t_dec[-1] == n_hidden_t_enc[-1]
# Build feature embedding networks (encoding and decoding if gamma > 0)
nets, embeddings, pred_feat_emb = mh.build_feat_emb_nets(
embedding_source, n_feats, n_samples_unsup, input_var_unsup,
n_hidden_u, n_hidden_t_enc, n_hidden_t_dec, gamma, encoder_net_init,
decoder_net_init, save_path, random_proj, decoder_encoder_unit_ratio,
embedding_noise)
# Build feature embedding reconstruction networks (if alpha > 0, beta > 0)
nets += mh.build_feat_emb_reconst_nets(
[alpha, beta], n_samples_unsup, n_hidden_u,
[n_hidden_t_enc, n_hidden_t_dec], nets,
[encoder_net_init, decoder_net_init])
# Supervised network
discrim_net, hidden_rep = mh.build_discrim_net(
batch_size, n_feats, normed_input_sup, n_hidden_t_enc, n_hidden_s,
embeddings[0], disc_nonlinearity, n_targets, batchnorm, input_dropout)
# Reconstruct network
nets += [
mh.build_reconst_net(hidden_rep,
embeddings[1] if len(embeddings) > 1 else None,
n_feats * decoder_encoder_unit_ratio, gamma,
decoder_encoder_unit_ratio)
]
# Load weights if we are resuming job
if resume:
# Load best model
with np.load(os.path.join(save_copy, 'dietnet_best.npz')) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
nlayers = len(
lasagne.layers.get_all_params(filter(None, nets) + [discrim_net]))
#lasagne.layers.set_all_param_values(filter(None, nets) +
# [discrim_net],
# param_values[:nlayers])
params = lasagne.layers.get_all_params(
filter(None, nets) + [discrim_net])
for p, v in zip(params, param_values[:nlayers]):
# Do not overwrite embedding value with old embedding. Removing
# the following condition will prevent a trained model from being
# tested on a different dataset
if p.name != "feat_emb":
p.set_value(v)
print("Building and compiling training functions")
# Build and compile training functions
predictions, predictions_det = mh.define_predictions(nets, start=2)
prediction_sup, prediction_sup_det = mh.define_predictions([discrim_net])
prediction_sup = prediction_sup[0]
prediction_sup_det = prediction_sup_det[0]
# Define losses
# reconstruction losses
if input_decoder_mode == "regression":
reconst_losses, reconst_losses_det = mh.define_reconst_losses(
predictions, predictions_det,
[input_var_unsup, input_var_unsup, normed_input_sup])
elif input_decoder_mode == "classification":
# Obtain regular reconstruction losses for every reconstruction
# but the reconstruction of the supervised input data
reconst_losses1, reconst_losses_det1 = mh.define_reconst_losses(
predictions[:-1], predictions_det[:-1],
[input_var_unsup, input_var_unsup])
# Obtain a "classification" reconstruction loss for the reconstruction
# of the supervised input data. This classification loss will be
# performed on the input data without normalization
reconst_losses2, reconst_losses_det2 = mh.define_classif_reconst_losses(
predictions[-1:], predictions_det[-1:], [reconst_target_sup],
[decoder_encoder_unit_ratio])
reconst_losses = reconst_losses1 + reconst_losses2
reconst_losses_det = reconst_losses_det1 + reconst_losses_det2
# supervised loss
sup_loss, sup_loss_det = mh.define_sup_loss(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
keep_labels, target_var_sup,
missing_labels_val)
# Define inputs
inputs = [input_var_sup, target_var_sup]
# Define parameters
params = lasagne.layers.get_all_params([discrim_net] + filter(None, nets),
trainable=True,
unwrap_shared=False)
params_to_freeze= \
lasagne.layers.get_all_params(filter(None, nets), trainable=False,
unwrap_shared=False)
# Remove unshared variables from params and params_to_freeze
params = [
p for p in params
if isinstance(p, theano.compile.sharedvalue.SharedVariable)
]
params_to_freeze = [
p for p in params_to_freeze
if isinstance(p, theano.compile.sharedvalue.SharedVariable)
]
print("Params : ", params)
feat_emb_var = next(p for p in lasagne.layers.get_all_params([discrim_net])
if p.name == 'input_unsup' or p.name == 'feat_emb')
# feat_emb_var = lasagne.layers.get_all_params([discrim_net])[0]
print(feat_emb_var)
feat_emb_val = feat_emb_var.get_value()
feat_emb_norms = (feat_emb_val**2).sum(0)**0.5
feat_emb_var.set_value(feat_emb_val / feat_emb_norms)
print('Number of params discrim: ' + str(len(params)))
print('Number of params to freeze: ' + str(len(params_to_freeze)))
for p in params_to_freeze:
new_params = [el for el in params if el != p]
params = new_params
print('Number of params to update: ' + str(len(params)))
# Combine losses
loss = delta*sup_loss + alpha*reconst_losses[0] + beta*reconst_losses[1] + \
gamma*reconst_losses[2]
loss_det = delta*sup_loss_det + alpha*reconst_losses_det[0] + \
beta*reconst_losses_det[1] + gamma*reconst_losses_det[2]
l2_penalty = apply_penalty(params, l2)
loss = loss + lmd * l2_penalty
loss_det = loss_det + lmd * l2_penalty
# Compute network updates
assert optimizer in ["rmsprop", "adam", "amsgrad"]
if optimizer == "rmsprop":
updates = lasagne.updates.rmsprop(loss, params, learning_rate=lr)
elif optimizer == "adam":
updates = lasagne.updates.adam(loss, params, learning_rate=lr)
elif optimizer == "amsgrad":
updates = lasagne.updates.amsgrad(loss, params, learning_rate=lr)
#updates = lasagne.updates.sgd(loss,
# params,
# learning_rate=lr)
# updates = lasagne.updates.momentum(loss, params,
# learning_rate=lr, momentum=0.0)
# Apply norm constraints on the weights
for k in updates.keys():
if updates[k].ndim == 2:
updates[k] = lasagne.updates.norm_constraint(updates[k], 1.0)
# Compile training function
train_fn = theano.function(inputs,
loss,
updates=updates,
on_unused_input='ignore')
# Monitoring Labels
monitor_labels = [
"reconst. feat. W_enc", "reconst. feat. W_dec", "reconst. loss"
]
monitor_labels = [
i for i, j in zip(monitor_labels, reconst_losses) if j != 0
]
monitor_labels += ["feat. W_enc. mean", "feat. W_enc var"]
monitor_labels += ["feat. W_dec. mean", "feat. W_dec var"] if \
(embeddings[1] is not None) else []
monitor_labels += ["loss. sup.", "total loss"]
# Build and compile test function
val_outputs = reconst_losses_det
val_outputs = [i for i, j in zip(val_outputs, reconst_losses) if j != 0]
val_outputs += [embeddings[0].mean(), embeddings[0].var()]
val_outputs += [embeddings[1].mean(), embeddings[1].var()] if \
(embeddings[1] is not None) else []
val_outputs += [sup_loss_det, loss_det]
# Compute supervised accuracy and add it to monitoring list
test_acc, test_pred = mh.define_test_functions(disc_nonlinearity,
prediction_sup,
prediction_sup_det,
target_var_sup)
monitor_labels.append("accuracy")
val_outputs.append(test_acc)
# If appropriate, compute the input reconstruction accuracy and add it to
# the monitoring list
if input_decoder_mode == "classification":
input_reconst_acc = mh.define_classif_reconst_acc(
predictions_det[-1], reconst_target_sup,
decoder_encoder_unit_ratio)
#import pdb; pdb.set_trace()
monitor_labels.append("input_reconst_acc")
val_outputs.append(input_reconst_acc)
# Compile prediction function
predict = theano.function([input_var_sup], test_pred)
predict_from_normed_inps = theano.function([normed_input_sup], test_pred)
predict_scores = theano.function([input_var_sup], prediction_sup_det)
predict_scores_from_normed_inps = theano.function([input_var_sup],
prediction_sup_det)
# Compile validation function
val_fn = theano.function(inputs, [prediction_sup_det] + val_outputs,
on_unused_input='ignore')
# Finally, launch the training loop.
print("Starting training...")
# Some variables
patience = 0
train_monitored = []
valid_monitored = []
train_loss = []
# Pre-training monitoring
print("Epoch 0 of {}".format(num_epochs))
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
# Before starting training, save a copy of the model in case
np.savez(
os.path.join(save_path, 'dietnet_best.npz'),
*lasagne.layers.get_all_param_values(
filter(None, nets) + [discrim_net]))
# Training loop
start_training = time.time()
for epoch in range(num_epochs):
start_time = time.time()
print("Epoch {} of {}".format(epoch + 1, num_epochs))
nb_minibatches = 0
loss_epoch = 0
# Train pass
for batch in mlh.iterate_minibatches(x_train,
training_labels,
batch_size,
shuffle=True):
loss_epoch += train_fn(*batch)
nb_minibatches += 1
loss_epoch /= nb_minibatches
train_loss += [loss_epoch]
# Monitoring on the training set
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
train_monitored += [train_err]
# Monitoring on the validation set
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
valid_monitored += [valid_err]
try:
early_stop_val = valid_err[monitor_labels.index(
early_stop_criterion)]
except:
raise ValueError("There is no monitored value by the name of %s" %
early_stop_criterion)
valid_loss_sup_hist = [
v[monitor_labels.index("loss. sup.")] for v in valid_monitored
]
valid_loss_sup = valid_loss_sup_hist[-1]
# Early stopping
if epoch == 0:
best_valid = early_stop_val
elif ((early_stop_val > best_valid
and early_stop_criterion == 'input_reconst_acc')
or (early_stop_val > best_valid
and early_stop_criterion == 'accuracy')
or (early_stop_val >= best_valid
and early_stop_criterion == 'accuracy'
and valid_loss_sup == min(valid_loss_sup_hist))
or (early_stop_val < best_valid
and early_stop_criterion == 'loss. sup.')):
best_valid = early_stop_val
patience = 0
# Save stuff
np.savez(
os.path.join(save_path, 'dietnet_best.npz'),
*lasagne.layers.get_all_param_values(
filter(None, nets) + [discrim_net]))
np.savez(save_path + "/errors_supervised_best.npz",
zip(*train_monitored), zip(*valid_monitored))
# Monitor on the test set now because sometimes the saving doesn't
# go well and there isn't a model to load at the end of training
if y_test is not None:
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
138,
shuffle=False)
test_err = mlh.monitoring(test_minibatches, "test", val_fn,
monitor_labels, prec_recall_cutoff)
else:
patience += 1
# Save stuff
np.savez(
os.path.join(save_path, 'dietnet_last.npz'),
*lasagne.layers.get_all_param_values(
filter(None, nets) + [discrim_net]))
np.savez(save_path + "/errors_supervised_last.npz",
zip(*train_monitored), zip(*valid_monitored))
print(" epoch time:\t\t\t{:.3f}s \n".format(time.time() - start_time))
# End training if needed
if patience == max_patience or epoch == num_epochs - 1:
break
# Anneal the learning rate
lr.set_value(
np.array(lr.get_value() * learning_rate_annealing,
dtype="float32"))
# End training with a final monitoring step on the best model
print("Ending training")
# Load best model
with np.load(os.path.join(save_path, 'dietnet_best.npz')) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
nlayers = len(
lasagne.layers.get_all_params(filter(None, nets) + [discrim_net]))
#lasagne.layers.set_all_param_values(filter(None, nets) +
# [discrim_net],
# param_values[:nlayers])
params = lasagne.layers.get_all_params(
filter(None, nets) + [discrim_net])
for p, v in zip(params, param_values[:nlayers]):
# Do not overwrite embedding value with old embedding. Removing
# the following condition will prevent a trained model from being
# tested on a different dataset
if p.name != "feat_emb":
p.set_value(v)
if embedding_source is None:
# Save embedding
pred = pred_feat_emb()
np.savez(os.path.join(save_path, 'feature_embedding.npz'), pred)
# Training set results
train_minibatches = mlh.iterate_minibatches(x_train,
y_train,
batch_size,
shuffle=False)
train_err = mlh.monitoring(train_minibatches, "train", val_fn,
monitor_labels, prec_recall_cutoff)
# Validation set results
valid_minibatches = mlh.iterate_minibatches(x_valid,
y_valid,
batch_size,
shuffle=False)
valid_err = mlh.monitoring(valid_minibatches, "valid", val_fn,
monitor_labels, prec_recall_cutoff)
# Test set results
if y_test is not None:
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
138,
shuffle=False)
test_err = mlh.monitoring(test_minibatches, "test", val_fn,
monitor_labels, prec_recall_cutoff)
# Test the model's accuracy with varying levels of provided SNPs
test_minibatches = mlh.iterate_minibatches(x_test,
y_test,
138,
shuffle=False)
mlh.eval_prediction(test_minibatches,
"test (rescaled)",
predict_from_normed_inps,
norm_mus,
norm_sigmas,
nb_evals=1,
rescale_inputs=True)
# Save the model's test predictions to file
print(x_test.shape)
test_predictions = []
for minibatch in mlh.iterate_testbatches(x_test, 1, shuffle=False):
test_predictions += [predict(minibatch)]
print(len(test_predictions))
print(sum([t.shape[0] for t in test_predictions]))
np.savez(os.path.join(save_path, 'test_predictions.npz'),
test_predictions)
# Get the scores assigned by the model to each class for each test sample
test_scores = []
for minibatch in mlh.iterate_testbatches(x_test, 1, shuffle=False):
test_scores += [predict_scores(minibatch)]
np.savez(os.path.join(save_path, 'test_scores.npz'), test_scores)
# Generate new SNP embeddings using test examples labeled according
# to the model's predictions
if bootstrap_snp_embeddings:
if bootstrap_cutoff == "soft":
bootstrap_snp_data = np.hstack(
(x_train.transpose(), x_valid.transpose(),
x_test.transpose()))
bootstrap_labels = np.vstack(
(y_train, y_valid, np.array(test_scores)[:, 0, :]))
filename_genotypic = 'bootstrap_gen_snp_embeddings_softlabels.npy'
filename_allelic = 'bootstrap_all_snp_embeddings_softlabels.npy'
else: # Hard cutoff
sure_test_idxs = np.argwhere(
(np.array(test_scores)[:, 0, :] >
bootstrap_cutoff).sum(1)).flatten()
sure_test_inputs = x_test[sure_test_idxs]
sure_test_preds = np.array(test_scores)[sure_test_idxs,
0].argmax(1)
bootstrap_snp_data = np.hstack(
(x_train.transpose(), x_valid.transpose(),
sure_test_inputs.transpose()))
bootstrap_labels = np.hstack(
(y_train.argmax(1), y_valid.argmax(1), sure_test_preds))
filename_genotypic = 'bootstrap_gen_snp_embeddings_cutoff%f.npy' % bootstrap_cutoff
filename_allelic = 'bootstrap_all_snp_embeddings_cutoff%f.npy' % bootstrap_cutoff
utils_helpers.generate_snp_hist(
bootstrap_snp_data,
bootstrap_labels,
label_names=label_names,
perclass=True,
sum_to_one=True,
filename_genotypic=os.path.join(save_path, filename_genotypic),
filename_allelic=os.path.join(save_path, filename_allelic))
# Print all final errors for train, validation and test
print("Training time:\t\t\t{:.3f}s".format(time.time() - start_training))
# Analyse the model gradients to determine the influence of each SNP on
# each of the model's prediction
print(label_names)
class_idx = T.iscalar("class index")
grad_fn = theano.function([input_var_sup, class_idx],
T.grad(prediction_sup_det[:, class_idx].mean(),
input_var_sup).mean(0))
grads_wrt_inputs = mlh.get_grads_wrt_inputs(x_test, grad_fn, feature_names,
label_names)
# Obtain function that takes as inputs normed inputs and returns the
# gradient of a class score wrt the normed inputs themselves (this is
# requird because computing the integrated gradients requires to be able
# to interpolate between an example where all features are missing and an
# example where any number of features are provided)
grad_from_normed_fn = theano.function(
[normed_input_sup, class_idx],
T.grad(prediction_sup_det[:, class_idx].sum(),
normed_input_sup).mean(0))
# Collect integrated gradients over the whole test set. Obtain, for each
# SNP, for each possible value (0, 1 or 2), the average contribution of that
# value for what SNP to the score of each class.
avg_int_grads = np.zeros((x_test.shape[1], 3, len(label_names)),
dtype="float32")
counts_int_grads = np.zeros((x_test.shape[1], 3), dtype="int32")
for test_idx in range(x_test.shape[0]):
int_grads = mlh.get_integrated_gradients(x_test[test_idx],
grad_from_normed_fn,
feature_names,
label_names,
norm_mus,
norm_sigmas,
m=100)
snp_value_mask = np.arange(3) == x_test[test_idx][:, None]
avg_int_grads += snp_value_mask[:, :,
None] * int_grads.transpose()[:,
None, :]
counts_int_grads += snp_value_mask
avg_int_grads = avg_int_grads / counts_int_grads[:, :, None]
# Save all the additional information required for model analysis :
# - Test predictions
# - SNP IDs
# - Subject IDs
# - Normalization parameters for the input minibatches
np.savez(os.path.join(save_path, 'additional_data.npz'),
test_labels=y_test,
test_scores=np.array(test_scores)[:, 0],
test_predictions=np.array(test_predictions)[:, 0],
norm_mus=norm_mus,
norm_sigmas=norm_sigmas,
grads_wrt_inputs=grads_wrt_inputs,
exmpl_ids_train=exmpl_ids_train,
exmpl_ids_valid=exmpl_ids_valid,
exmpl_ids_test=exmpl_ids_test,
feature_names=feature_names,
label_names=label_names,
avg_int_grads=avg_int_grads)
# Copy files to loadpath (only if some training has beeen done so there
# is a local saved version)
if save_path != save_copy and num_epochs > 0:
print('Copying model and other training files to {}'.format(save_copy))
copy_tree(save_path, save_copy)
for i in xrange(0):
l_hiddens.append(DenseLayer(dropout(l_hiddens[-1]), num_units=100, nonlinearity=rectify))
l_out = DenseLayer(dropout(l_hiddens[-1]), num_units=y.shape[1], nonlinearity=softmax, W=Orthogonal())
def reset():
if any(np.isnan(scale.get_value()) for scale in scales):
for scale in scales:
scale.set_value(1.)
for l in l_hiddens:
l.b.set_value(Constant()(l.b.get_value().shape))
l.W.set_value(Orthogonal()(l.W.get_value().shape))
l_out.b.set_value(Constant()(l_out.b.get_value().shape))
l_out.W.set_value(Orthogonal()(l_out.W.get_value().shape))
for p in (p for u in (updates_ada,updates_other,updates_scal) for p in u if p not in get_all_params(l_out)):
p.set_value(Constant()(p.get_value().shape))
chunky_l2 = apply_penalty(get_all_params(l_out,regularizable=True),l2)-l2(l_hiddens[0].W)+l2(l_hiddens[0].W/T.reshape(vscale,(206279,1)))
chunky_l1 = apply_penalty(get_all_params(l_out,regularizable=True),l1)-l1(l_hiddens[0].W)+l1(l_hiddens[0].W/T.reshape(vscale,(206279,1)))
simple_l2 = apply_penalty(get_all_params(l_out,regularizable=True),l2)
#l_out2 = DenseLayer(dropout(l_hiddens2[-1]), num_units=y.shape[1])
#l_out = lasagne.layers.NonlinearityLayer(lasagne.layers.ElemwiseSumLayer((l_out1,l_out2),.5), softmax)
#categorical_crossentropy(get_output(l_out)[train_indice])
target=T.fmatrix(name="target")
#f=theano.function([l_in.input_var],get_output(l_out),allow_input_downcast=True)
#f(X[0,:].toarray())
loss=categorical_crossentropy(get_output(l_out),target).mean()
# train_loss_smoo=categorical_crossentropy(get_output(l_out,deterministic=True)[train_indices,],target[train_indices,]).mean()
# valid_loss=categorical_crossentropy(get_output(l_out)[valid_indices,],target[valid_indices,]).mean()
# valid_loss_smoo=categorical_crossentropy(get_output(l_out,deterministic=True)[valid_indices,],target[valid_indices,]).mean()
