def train(**params):
print("** Loading training images")
start = time.time()
lr_hr_ds, n_data = data.load_train_dataset(params['lr_dir'],
params['hr_dir'], params['ext'],
params['batch_size'])
val_lr_hr_ds, n_val_data = data.load_test_dataset(params['val_lr_dir'],
params['val_hr_dir'],
params['val_ext'],
params['val_batch_size'])
print("Finish loading images in %.2fs" % (time.time() - start))
one_gpu_model, gpu_model = prepare_model(**params)
exp_folder = make_exp_folder(params['exp_dir'], params['arc'])
save_params(exp_folder, **params)
tb_callback = make_tb_callback(exp_folder)
lr_callback = make_lr_callback(params['lr_init'], params['lr_decay'],
params['lr_decay_at_steps'])
cp_callback = make_cp_callback(exp_folder, one_gpu_model)
gpu_model.fit(lr_hr_ds,
epochs=params['epochs'],
steps_per_epoch=num_iter_per_epoch(n_data,
params['batch_size']),
callbacks=[tb_callback, cp_callback, lr_callback],
initial_epoch=params['init_epoch'],
validation_data=val_lr_hr_ds,
validation_steps=n_val_data)
one_gpu_model.save_weights(os.path.join(exp_folder, 'final_model.h5'))
K.clear_session()
def train(self, fname, dataset, sess_info, epochs):
(sess, saver) = sess_info
f = open_file(fname)
iterep = 500
for i in range(iterep * epochs):
batch = dataset.train.next_batch(100)
sess.run(self.train_step, feed_dict={'x:0': batch})
progbar(i, iterep)
if (i + 1) % iterep == 0:
a, b = sess.run(
[self.nent, self.loss],
feed_dict={
'x:0':
dataset.train.data[np.random.choice(
len(dataset.train.data), 200)]
})
c, d = sess.run([self.nent, self.loss],
feed_dict={'x:0': dataset.test.data})
a, b, c, d = -a.mean(), b.mean(), -c.mean(), d.mean()
e = test_acc(dataset, sess, self.qy_logit)
string = (
'{:>10s},{:>10s},{:>10s},{:>10s},{:>10s},{:>10s}'.format(
'tr_ent', 'tr_loss', 't_ent', 't_loss', 't_acc',
'epoch'))
stream_print(f, string, i <= iterep)
string = ('{:10.2e},{:10.2e},{:10.2e},{:10.2e},{:10.2e},{:10d}'
.format(a, b, c, d, e, int((i + 1) / iterep)))
stream_print(f, string)
# Saves parameters every 10 epochs
if (i + 1) % (10 * iterep) == 0:
print('saving')
save_params(saver, sess, (i + 1) // iterep)
if f is not None: f.close()
def run(self):
""" Script for initializing training/testing process """
global_step = tf.Variable(0, trainable=False)
optimizer = tf.train.AdamOptimizer(self.learning_rate)
deconv_mult = lambda grads: list(map(lambda x: (x[0] * 1.0, x[1]) if 'deconv' in x[1].name else x, grads))
grads = deconv_mult(optimizer.compute_gradients(self.loss))
self.train_op = optimizer.apply_gradients(grads, global_step=global_step)
tf.global_variables_initializer().run()
# Load previous model checkpoints if exists
if self.load():
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if self.params:
save_params(self.sess, self.model.model_params)
elif self.train:
# Train and test run sequentially
self.run_train()
self.run_test()
else:
self.run_test()
def main(num_epochs=20):
print("Building model and compiling functions...")
input_var = T.tensor4('inputs')
fcae = build_fcae(input_var)
output = nn.layers.get_output(fcae['output'])
output_det = nn.layers.get_output(fcae['output'], deterministic=True)
loss = nn.objectives.binary_crossentropy(output, input_var).mean()
test_loss = nn.objectives.binary_crossentropy(output_det, input_var).mean()
# ADAM updates
params = nn.layers.get_all_params(fcae['output'], trainable=True)
updates = nn.updates.adam(loss, params, learning_rate=1e-3)
train_fn = theano.function([input_var], loss, updates=updates)
val_fn = theano.function([input_var], test_loss)
ae_fn = theano.function([input_var], nn.layers.get_output(fcae['output']))
data = u.DataH5PyStreamer(os.path.join(c.external_data, 'mnist.hdf5'),
batch_size=128)
hist = u.train_with_hdf5(
data,
num_epochs=num_epochs,
train_fn=train_fn,
test_fn=val_fn,
max_per_epoch=40,
tr_transform=lambda x: u.raw_to_floatX(x[0], pixel_shift=0.),
te_transform=lambda x: u.raw_to_floatX(x[0], pixel_shift=0.))
u.save_params(fcae['output'],
'fcae_params_{}.npz'.format(np.asarray(hist)[-1, -1]))
from PIL import Image
from matplotlib import pyplot as plt
streamer = data.streamer()
imb = next(streamer.get_epoch_iterator())
batch = u.raw_to_floatX(imb[0], pixel_shift=0.).transpose((0, 1, 3, 2))
orig_dim = 28
im = Image.new("RGB", (orig_dim * 20, orig_dim * 20))
for j in xrange(10):
dim = orig_dim
orig_im = Image.fromarray(
u.get_picture_array(batch,
np.random.randint(batch.shape[0]),
shift=0.0))
im.paste(orig_im.resize((2 * orig_dim, 2 * orig_dim), Image.ANTIALIAS),
box=(0, j * orig_dim * 2))
new_im = {}
for i in xrange(9):
new_im = orig_im.resize((dim, dim), Image.ANTIALIAS)
new_im = ae_fn(
u.arr_from_img(new_im, shift=0.).reshape(1, -1, dim, dim))
new_im = Image.fromarray(u.get_picture_array(new_im, 0, shift=0.))\
.resize((orig_dim*2, orig_dim*2), Image.ANTIALIAS)
im.paste(new_im, box=((i + 1) * orig_dim * 2, j * orig_dim * 2))
dim = int(dim * 1.2)
im.save('increasing_size_autoencoded.jpg')
def train(self, params, train, dev, test):
start_time = time.time()
counter = 0
try:
for eidx in xrange(params.epochs):
kf = utils.get_minibatches_idx(len(train), params.batchsize, shuffle=True)
uidx = 0
for _, train_index in kf:
uidx += 1
batch = [train[t] for t in train_index]
vocab = self.get_word_arr(batch)
y = self.get_y(batch)
x, xmask = self.prepare_data(self.populate_embeddings_words(batch, vocab))
idxs = self.get_idxs(xmask)
if params.nntype == "charlstm" or params.nntype == "charcnn":
char_indices = self.populate_embeddings_characters(vocab)
if params.nntype == "charagram":
char_hash = self.populate_embeddings_characters_charagram(vocab)
if params.nntype == "charlstm":
c, cmask = self.prepare_data(char_indices)
if params.nntype == "charcnn":
c = self.prepare_data_conv(char_indices)
if params.nntype == "charlstm":
cost = self.train_function(c, cmask, x, xmask, idxs, y)
if params.nntype == "charcnn":
cost = self.train_function(c, x, xmask, idxs, y)
if params.nntype == "charagram":
cost = self.train_function(char_hash, x, xmask, idxs, y)
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
#print 'Epoch ', (eidx+1), 'Update ', (uidx+1), 'Cost ', cost
if(params.save):
counter += 1
utils.save_params(self, params.outfile+str(counter)+'.pickle')
if(params.evaluate):
devscore = self.evaluate(dev, params)
testscore = self.evaluate(test, params)
trainscore = self.evaluate(train, params)
print "accuracy: ", devscore, testscore, trainscore
print 'Epoch ', (eidx+1), 'Cost ', cost
except KeyboardInterrupt:
print "Training interrupted"
end_time = time.time()
print "total time:", (end_time - start_time)
def save_params_to_file(self, fname: str):
"""
Saves model parameters to file.
:param fname: Path to save parameters to.
"""
assert self._is_built
utils.save_params(self.params.copy(), fname)
logging.info('Saved params to "%s"', fname)
def main(num_epochs = 20):
print("Building model and compiling functions...")
input_var = T.tensor4('inputs')
fcae = build_fcae(input_var)
output = nn.layers.get_output(fcae['output'])
output_det = nn.layers.get_output(fcae['output'], deterministic=True)
loss = nn.objectives.binary_crossentropy(output, input_var).mean()
test_loss = nn.objectives.binary_crossentropy(output_det, input_var).mean()
# ADAM updates
params = nn.layers.get_all_params(fcae['output'], trainable=True)
updates = nn.updates.adam(loss, params, learning_rate=1e-3)
train_fn = theano.function([input_var], loss, updates=updates)
val_fn = theano.function([input_var], test_loss)
ae_fn = theano.function([input_var], nn.layers.get_output(fcae['output']))
data = u.DataH5PyStreamer(os.path.join(c.external_data, 'mnist.hdf5'), batch_size=128)
hist = u.train_with_hdf5(data, num_epochs=num_epochs,
train_fn = train_fn, test_fn = val_fn,
max_per_epoch=40,
tr_transform = lambda x: u.raw_to_floatX(x[0], pixel_shift=0.),
te_transform = lambda x: u.raw_to_floatX(x[0], pixel_shift=0.))
u.save_params(fcae['output'], 'fcae_params_{}.npz'.format(np.asarray(hist)[-1,-1]))
from PIL import Image
from matplotlib import pyplot as plt
streamer = data.streamer()
imb = next(streamer.get_epoch_iterator())
batch = u.raw_to_floatX(imb[0], pixel_shift=0.).transpose((0,1,3,2))
orig_dim = 28
im = Image.new("RGB", (orig_dim*20, orig_dim*20))
for j in xrange(10):
dim = orig_dim
orig_im = Image.fromarray(u.get_picture_array(batch,
np.random.randint(batch.shape[0]), shift=0.0))
im.paste(orig_im.resize((2*orig_dim, 2*orig_dim), Image.ANTIALIAS),
box=(0,j*orig_dim*2))
new_im = {}
for i in xrange(9):
new_im = orig_im.resize((dim, dim), Image.ANTIALIAS)
new_im = ae_fn(u.arr_from_img(new_im, shift=0.).reshape(1,-1,dim,dim))
new_im = Image.fromarray(u.get_picture_array(new_im, 0, shift=0.))\
.resize((orig_dim*2, orig_dim*2), Image.ANTIALIAS)
im.paste(new_im, box=((i+1)*orig_dim*2, j*orig_dim*2))
dim = int(dim * 1.2)
im.save('increasing_size_autoencoded.jpg')
def main(specstr=default_specstr, z_dim=256, num_epochs=10, ch=3, init_from='',
img_size=64, pxsh=0.5, data_file='', batch_size=8, save_to='params'):
# build expressions for the output, loss, gradient
input_var = T.tensor4('inputs')
print('building specstr {} - zdim {}'.format(specstr, z_dim))
cae = m.build_cae_nopoolinv(input_var, shape=(img_size,img_size), channels=ch,
specstr=specstr.format(z_dim))
l_list = nn.layers.get_all_layers(cae)
pred = nn.layers.get_output(cae)
loss = nn.objectives.squared_error(pred, input_var.flatten(2)).mean()
params = nn.layers.get_all_params(cae, trainable=True)
grads = nn.updates.total_norm_constraint(T.grad(loss, params), 10)
updates = nn.updates.adam(grads, params, learning_rate=1e-3)
te_pred = nn.layers.get_output(cae, deterministic=True)
te_loss = nn.objectives.squared_error(te_pred, input_var.flatten(2)).mean()
# training functions
print('compiling functions')
train_fn = theano.function([input_var], loss, updates=updates)
val_fn = theano.function([input_var], te_loss)
# compile functions for encode/decode to test later
enc_layer = l_list[next(i for i in xrange(len(l_list)) if l_list[i].name=='encode')]
enc_fn = theano.function([input_var], nn.layers.get_output(enc_layer, deterministic=True))
dec_fn = lambda z: nn.layers.get_output(cae, deterministic=True,
inputs={l_list[0]:np.zeros((z.shape[0],ch,img_size,img_size),dtype=theano.config.floatX),
enc_layer:z}).eval().reshape(-1,ch,img_size,img_size)
# load params if requested, run training
if len(init_from) > 0:
print('loading params from {}'.format(init_from))
load_params(cae, init_from)
data = u.DataH5PyStreamer(data_file, batch_size=batch_size)
print('training for {} epochs'.format(num_epochs))
hist = u.train_with_hdf5(data, num_epochs=num_epochs,
train_fn = train_fn,
test_fn = val_fn,
tr_transform=lambda x: u.raw_to_floatX(x[0], pixel_shift=pxsh, center=False),
te_transform=lambda x: u.raw_to_floatX(x[0], pixel_shift=pxsh, center=True))
# generate examples, save training history
te_stream = data.streamer(shuffled=True)
imb, = next(te_stream.get_epoch_iterator())
tg = u.raw_to_floatX(imb, pixel_shift=pxsh, square=True, center=True)
pr = dec_fn(enc_fn(tg))
for i in range(pr.shape[0]):
u.get_image_pair(tg, pr,index=i,shift=pxsh).save('output_{}.jpg'.format(i))
hist = np.asarray(hist)
np.savetxt('cae_train_hist.csv', np.asarray(hist), delimiter=',', fmt='%.5f')
u.save_params(cae, os.path.join(save_to, 'cae_{}.npz'.format(hist[-1,-1])))
def callback_fn(self, params):
print("Function value: ", end='')
loss = objective_function(params, self.X, self.y, self.lambd)
print(loss)
print("Average gradient: ", end='')
avg_grad = np.mean(
d_optimization_function(params, self.X, self.y, self.lambd)**2)
print(avg_grad)
print()
self.iters += 1
save_params(params, self.filename, self.iters)
save_losses(loss, self.loss_filename, self.iters)
def run(self):
self.train_op = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
tf.global_variables_initializer().run()
if self.load():
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if self.params:
save_params(self.sess, self.model.weights, self.model.biases, self.model.alphas, self.model.model_params)
elif self.train:
self.run_train()
else:
self.run_test()
def evaluate(eval_dir, method, train_features, train_labels, test_features,
test_labels, **kwargs):
if method == 'svm':
acc_train, acc_test = svm(train_features, train_labels, test_features,
test_labels)
elif method == 'knn':
acc_train, acc_test = knn(train_features, train_labels, test_features,
test_labels, **kwargs)
elif method == 'nearsub':
acc_train, acc_test = nearsub(train_features, train_labels,
test_features, test_labels, **kwargs)
elif method == 'nearsub_pca':
acc_train, acc_test = knn(train_features, train_labels, test_features,
test_labels, **kwargs)
acc_dict = {'train': acc_train, 'test': acc_test}
utils.save_params(eval_dir, acc_dict, name=f'acc_{method}')
def run(self):
# SGD with momentum
# self.train_op = tf.train.MomentumOptimizer(self.learning_rate, self.momentum).minimize(self.loss)
self.train_op = tf.train.AdadeltaOptimizer(learning_rate=self.learning_rate).minimize(self.loss)
tf.initialize_all_variables().run()
if self.load(self.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if self.params:
save_params(self.sess, self.weights, self.biases)
elif self.train:
self.run_train()
else:
self.run_test()
def run(self):
self.train_op = tf.train.AdamOptimizer().minimize(self.loss)
tf.initialize_all_variables().run()
if self.load(self.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if self.params:
s, d, m = self.model_params
save_params(self.sess, self.weights, self.biases, self.alphas, s,
d, m)
elif self.train:
self.run_train()
else:
self.run_test()
def main(L=2, img_size=64, pxsh=0., z_dim=32, n_hid=1024, num_epochs=12, binary='True',
init_from='', data_file='', batch_size=128, save_to='params', max_per_epoch=-1):
binary = binary.lower()=='true'
# Create VAE model
input_var = T.tensor4('inputs')
print("Building model and compiling functions...")
print("L = {}, z_dim = {}, n_hid = {}, binary={}".format(L, z_dim, n_hid, binary))
l_tup = l_z_mu, l_z_ls, l_x_mu_list, l_x_ls_list, l_x_list, l_x = \
m.build_vcae(input_var, L=L, binary=binary, z_dim=z_dim, n_hid=n_hid)
if len(init_from) > 0:
print('loading from {}'.format(init_from))
load_params(l_x, init_from)
# compile functions
loss, _ = u.build_vae_loss(input_var, *l_tup, deterministic=False, binary=binary, L=L)
test_loss, test_prediction = u.build_vae_loss(input_var, *l_tup, deterministic=True,
binary=binary, L=L)
params = nn.layers.get_all_params(l_x, trainable=True)
updates = nn.updates.adam(loss, params, learning_rate=3e-5)
train_fn = theano.function([input_var], loss, updates=updates)
val_fn = theano.function([input_var], test_loss)
ae_fn = theano.function([input_var], test_prediction)
# run training loop
print('training for {} epochs'.format(num_epochs))
data = u.DataH5PyStreamer(data_file, batch_size=batch_size)
hist = u.train_with_hdf5(data, num_epochs=num_epochs, train_fn=train_fn, test_fn=val_fn,
max_per_epoch=max_per_epoch,
tr_transform=lambda x: u.raw_to_floatX(x[0], pixel_shift=pxsh, center=False),
te_transform=lambda x: u.raw_to_floatX(x[0], pixel_shift=pxsh, center=True))
# generate examples, save training history
te_stream = data.streamer(shuffled=True)
imb, = next(te_stream.get_epoch_iterator())
orig_images = u.raw_to_floatX(imb, pixel_shift=pxsh)
autoencoded_images = ae_fn(orig_images)
for i in range(autoencoded_images.shape[0]):
u.get_image_pair(orig_images, autoencoded_images, index=i, shift=pxsh) \
.save('output_{}.jpg'.format(i))
hist = np.asarray(hist)
np.savetxt('vcae_train_hist.csv', np.asarray(hist), delimiter=',', fmt='%.5f')
u.save_params(l_x, os.path.join(save_to, 'vcae_{}.npz'.format(hist[-1,-1])))
def train(params=None):
os.makedirs(params['ckpt_path'], exist_ok=True)
device = torch.device("cuda")
train_dataset = HDRDataset(params['dataset'],
params=params,
suffix=params['dataset_suffix'])
train_loader = DataLoader(train_dataset,
batch_size=params['batch_size'],
shuffle=True)
model = HDRPointwiseNN(params=params)
ckpt = get_latest_ckpt(params['ckpt_path'])
if ckpt:
print('Loading previous state:', ckpt)
state_dict = torch.load(ckpt)
state_dict, _ = load_params(state_dict)
model.load_state_dict(state_dict)
model.to(device)
mseloss = torch.nn.MSELoss()
optimizer = Adam(model.parameters(), params['lr'])
count = 0
for e in range(params['epochs']):
model.train()
for i, (low, full, target) in enumerate(train_loader):
optimizer.zero_grad()
low = low.to(device)
full = full.to(device)
t = target.to(device)
res = model(low, full)
total_loss = mseloss(res, t)
total_loss.backward()
if (count + 1) % params['log_interval'] == 0:
_psnr = psnr(res, t).item()
loss = total_loss.item()
print(e, count, loss, _psnr)
optimizer.step()
if (count + 1) % params['ckpt_interval'] == 0:
print('@@ MIN:', torch.min(res), 'MAX:', torch.max(res))
model.eval().cpu()
ckpt_model_filename = "ckpt_" + str(e) + '_' + str(
count) + ".pth"
ckpt_model_path = os.path.join(params['ckpt_path'],
ckpt_model_filename)
state = save_params(model.state_dict(), params)
torch.save(state, ckpt_model_path)
test(ckpt_model_path)
model.to(device).train()
count += 1
def run(self):
# SGD with momentum
# self.train_op = tf.train.MomentumOptimizer(self.learning_rate, self.momentum).minimize(self.loss)
# Now we use the Adam-Optimizer instead of SGD (Statistical Gradient Decent)
self.train_op = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).minimize(self.loss)
tf.initialize_all_variables().run()
if self.load(self.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if self.params:
save_params(self.sess, self.weights, self.biases)
elif self.train:
self.run_train()
else:
self.run_test()
def run(self):
global_step = tf.Variable(0, trainable=False)
optimizer = tf.train.AdamOptimizer(self.learning_rate)
deconv_mult = lambda grads: list(map(lambda x: (x[0] * 1.0, x[1]) if 'deconv' in x[1].name else x, grads))
grads = deconv_mult(optimizer.compute_gradients(self.loss))
self.train_op = optimizer.apply_gradients(grads, global_step=global_step)
tf.global_variables_initializer().run()
if self.load():
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if self.params:
save_params(self.sess, self.model.model_params)
elif self.train:
self.run_train()
else:
self.run_test()
def train(self):
num_minibatches = len(self.mnist.train.x) / self.minibatch_size
for epoch in xrange(self.num_epochs):
for mb_index in xrange(num_minibatches):
mb_x = self.mnist.train.x \
[mb_index : mb_index+self.minibatch_size]
mb_x = mb_x.reshape((self.minibatch_size, 1, 28, 28))
#E_h = crbm.E_h_given_x_func(mb_x)
#print "Shape of E_h", E_h.shape
cd_return = self.crbm.CD_step(mb_x)
sp_return = self.crbm.sparsity_step(mb_x)
self.series['cd'].append( \
(epoch, mb_index), cd_return)
self.series['sparsity'].append( \
(epoch, mb_index), sp_return)
total_idx = epoch*num_minibatches + mb_index
if (total_idx+1) % REDUCE_EVERY == 0:
self.series['params'].append( \
(epoch, mb_index), self.crbm.params)
if total_idx % VISUALIZE_EVERY == 0:
self.visualize_gibbs_result(\
mb_x, GIBBS_STEPS_IN_VIZ_CHAIN,
"gibbs_chain_"+str(epoch)+"_"+str(mb_index))
self.visualize_gibbs_result(mb_x, 1,
"gibbs_1_"+str(epoch)+"_"+str(mb_index))
self.visualize_filters(
"filters_"+str(epoch)+"_"+str(mb_index))
if TEST_CONFIG:
# do a single epoch for cluster tests config
break
if SAVE_PARAMS:
utils.save_params(self.crbm.params, "params.pkl")
def save_params(self, dir_path="", epoch=None):
param_saving_path = save_params(dir_path=dir_path,
name=self.name,
epoch=epoch,
params=self.params,
aux_states=self.aux_states)
misc_saving_path = save_misc(
dir_path=dir_path,
epoch=epoch,
name=self.name,
content={
'data_shapes':
{k: list(map(int, v))
for k, v in self.data_shapes.items()}
})
logging.info('Saving %s, params: \"%s\", misc: \"%s\"', self.name,
param_saving_path, misc_saving_path)
def index(request):
db = Database('banco-notes')
if request.startswith('POST'):
params = save_params(
request
) #params = {'titulo':algm coisa, 'detalhes':outra coisa, 'id':outra coisa}
#add_to_jsonfile(params, 'notes.json')
if 'deletar' in params.keys():
db.delete(params['deletar']) #id
elif params['id'] == 'None':
db.add(
Note(title=params['titulo'],
content=params['detalhes'],
id=params['id']))
else:
db.update(
Note(title=params['titulo'],
content=params['detalhes'],
id=params['id']))
return build_response(code=303,
reason='See Other',
headers='Location: /')
else:
# Cria uma lista de <li>'s para cada anotação
print('notas do banco: ', db.get_all())
note_template = load_template('components/note.html')
notes_li = [
note_template.format(id=note.id,
title=note.title,
details=note.content)
for note in db.get_all()
]
notes = '\n'.join(notes_li)
print('----------------------------------------')
return build_response(load_template('index.html').format(notes=notes))
def main(data_name, vae_type, dimZ, dimH, n_iter, batch_size, K, checkpoint):
dimY = 10
if vae_type == 'A':
from conv_generator_mnist_A import generator
if vae_type == 'B':
from conv_generator_mnist_B import generator
if vae_type == 'C':
from conv_generator_mnist_C import generator
if vae_type == 'D':
from conv_generator_mnist_D import generator
if vae_type == 'E':
from conv_generator_mnist_E import generator
if vae_type == 'F':
from conv_generator_mnist_F import generator
if vae_type == 'G':
from conv_generator_mnist_G import generator
from conv_encoder_mnist import encoder_gaussian as encoder
shape_high = (28, 28)
input_shape = (28, 28, 1)
n_channel = 64
# then define model
dec = generator(input_shape, dimH, dimZ, dimY, n_channel, 'sigmoid', 'gen')
enc, enc_conv, enc_mlp = encoder(input_shape, dimH, dimZ, dimY, n_channel,
'enc')
# define optimisers
X_ph = tf.placeholder(tf.float32, shape=(batch_size, ) + input_shape)
Y_ph = tf.placeholder(tf.float32, shape=(batch_size, dimY))
ll = 'l2'
fit, eval_acc = construct_optimizer(X_ph, Y_ph, [enc_conv, enc_mlp], dec,
ll, K, vae_type)
# load data
from utils_mnist import data_mnist
X_train, Y_train, X_test, Y_test = data_mnist(train_start=0,
train_end=60000,
test_start=0,
test_end=10000)
# initialise sessions
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
if not os.path.isdir('save/'):
os.mkdir('save/')
print('create path save/')
path_name = data_name + '_conv_vae_%s/' % (vae_type + '_' + str(dimZ))
if not os.path.isdir('save/' + path_name):
os.mkdir('save/' + path_name)
print('create path save/' + path_name)
filename = 'save/' + path_name + 'checkpoint'
if checkpoint < 0:
print('training from scratch')
init_variables(sess)
else:
load_params(sess, filename, checkpoint)
checkpoint += 1
# now start fitting
n_iter_ = min(n_iter, 20)
beta = 1.0
for i in range(int(n_iter / n_iter_)):
fit(sess, X_train, Y_train, n_iter_, lr, beta)
# print training and test accuracy
eval_acc(sess, X_test, Y_test, 'test')
# save param values
save_params(sess, filename, checkpoint)
checkpoint += 1
type=str,
default='',
help='extra information to add to folder name')
parser.add_argument(
'--save_dir',
type=str,
default='./saved_models/',
help='base directory for saving PyTorch model. (default: ./saved_models/)')
args = parser.parse_args()
# pipeline setup
model_dir = os.path.join(
args.save_dir, "iris", "layers{}_eps{}_eta{}"
"".format(args.layers, args.eps, args.eta))
os.makedirs(model_dir, exist_ok=True)
utils.save_params(model_dir, vars(args))
# data setup
X_train, y_train, X_test, y_test, num_classes = dataset.load_Iris(0.3)
# model setup
layers = [Vector(args.layers, eta=args.eta, eps=args.eps)]
model = Architecture(layers, model_dir, num_classes)
# train/test pass
print("Forward pass - train features")
Z_train = model(X_train, y_train)
utils.save_loss(model.loss_dict, model_dir, "train")
print("Forward pass - test features")
Z_test = model(X_test)
utils.save_loss(model.loss_dict, model_dir, "test")
def main(args):
logging.info('-' * 50)
logging.info('Load data files..')
question_belong = []
if args.debug:
logging.info('*' * 10 + ' Train')
train_examples = utils.load_data(args.train_file,
100,
relabeling=args.relabeling)
logging.info('*' * 10 + ' Dev')
dev_examples = utils.load_data(args.dev_file,
100,
relabeling=args.relabeling,
question_belong=question_belong)
else:
logging.info('*' * 10 + ' Train')
train_examples = utils.load_data(args.train_file,
relabeling=args.relabeling)
logging.info('*' * 10 + ' Dev')
dev_examples = utils.load_data(args.dev_file,
args.max_dev,
relabeling=args.relabeling,
question_belong=question_belong)
args.num_train = len(train_examples[0])
args.num_dev = len(dev_examples[0])
logging.info('-' * 50)
logging.info('Build dictionary..')
#word_dict = utils.build_dict(train_examples[0] + train_examples[1] + train_examples[2], args.max_vocab_size)
word_dict = pickle.load(open("../obj/dict.pkl", "rb"))
logging.info('-' * 50)
embeddings = utils.gen_embeddings(word_dict, args.embedding_size,
args.embedding_file) # EMBEDDING
(args.vocab_size, args.embedding_size) = embeddings.shape
logging.info('Compile functions..')
train_fn, test_fn, params, all_params = build_fn(args, embeddings)
logging.info('Done.')
logging.info('-' * 50)
logging.info(args)
logging.info('-' * 50)
logging.info('Intial test..')
dev_x1, dev_x2, dev_x3, dev_y = utils.vectorize(
dev_examples,
word_dict,
sort_by_len=not args.test_only,
concat=args.concat)
word_dict_r = {}
word_dict_r[0] = "unk"
assert len(dev_x1) == args.num_dev
all_dev = gen_examples(dev_x1, dev_x2, dev_x3, dev_y, args.batch_size,
args.concat)
dev_acc, pred = eval_acc(test_fn, all_dev)
logging.info('Dev accuracy: %.2f %%' % dev_acc)
best_acc = dev_acc
if args.test_only:
return
utils.save_params(args.model_file, all_params, epoch=0, n_updates=0)
# Training
logging.info('-' * 50)
logging.info('Start training..')
train_x1, train_x2, train_x3, train_y = utils.vectorize(train_examples,
word_dict,
concat=args.concat)
assert len(train_x1) == args.num_train
start_time = time.time()
n_updates = 0
all_train = gen_examples(train_x1, train_x2, train_x3, train_y,
args.batch_size, args.concat)
for epoch in range(args.num_epoches):
np.random.shuffle(all_train)
for idx, (mb_x1, mb_mask1, mb_x2, mb_mask2, mb_x3, mb_mask3,
mb_y) in enumerate(all_train):
train_loss = train_fn(mb_x1, mb_mask1, mb_x2, mb_mask2, mb_x3,
mb_mask3, mb_y)
if idx % 100 == 0:
logging.info('#Examples = %d, max_len = %d' %
(len(mb_x1), mb_x1.shape[1]))
logging.info(
'Epoch = %d, iter = %d (max = %d), loss = %.2f, elapsed time = %.2f (s)'
% (epoch, idx, len(all_train), train_loss,
time.time() - start_time))
n_updates += 1
if n_updates % args.eval_iter == 0:
samples = sorted(
np.random.choice(args.num_train,
min(args.num_train, args.num_dev),
replace=False))
sample_train = gen_examples(
[train_x1[k]
for k in samples], [train_x2[k] for k in samples],
[train_x3[k * 4 + o] for k in samples
for o in range(4)], [train_y[k] for k in samples],
args.batch_size, args.concat)
acc, pred = eval_acc(test_fn, sample_train)
logging.info('Train accuracy: %.2f %%' % acc)
dev_acc, pred = eval_acc(test_fn, all_dev)
logging.info('Dev accuracy: %.2f %%' % dev_acc)
if dev_acc > best_acc:
best_acc = dev_acc
logging.info(
'Best dev accuracy: epoch = %d, n_udpates = %d, acc = %.2f %%'
% (epoch, n_updates, dev_acc))
utils.save_params(args.model_file,
all_params,
epoch=epoch,
n_updates=n_updates)
help='load pretrained checkpoint for assigning labels')
parser.add_argument('--pretrain_epo',
type=int,
default=None,
help='load pretrained epoch for assigning labels')
args = parser.parse_args()
## Pipelines Setup
model_dir = os.path.join(
args.save_dir,
'seqsupce_{}+{}_cpb{}_epo{}_bs{}_lr{}_mom{}_wd{}_lcr{}{}'.format(
args.arch, args.data, args.cpb, args.epo, args.bs, args.lr, args.mom,
args.wd, args.lcr, args.tail))
headers = ["label_batch_id", "epoch", "step", "loss"]
utils.init_pipeline(model_dir, headers)
utils.save_params(model_dir, vars(args))
## per model functions
def lr_schedule(epoch, optimizer):
"""decrease the learning rate"""
lr = args.lr
if epoch >= 400:
lr = args.lr * 0.01
elif epoch >= 200:
lr = args.lr * 0.1
for param_group in optimizer.param_groups:
param_group['lr'] = lr
## Prepare for Training
learning_rate = 0.01
n_train = len(y_train)
n_iters = args.epochs * n_train
print("Training:", args.epochs, "epochs of", n_train, "iterations")
train_loss = numpy.zeros(n_iters)
start_time = timeit.default_timer()
for epoch in range(args.epochs):
for i in range(n_train):
iteration = i + n_train * epoch
train_loss[iteration] = train_model(
numpy.asarray(X_train[i], dtype='int32'),
numpy.asarray(y_train[i], dtype='int32'), learning_rate)
if (len(train_loss) > 1 and train_loss[-1] > train_loss[-2]):
learning_rate = learning_rate * 0.5
print("Setting learning rate to {}".format(learning_rate))
if iteration % args.print_interval == 0:
print('epoch {}, minibatch {}/{}, train loss {}'.format(
epoch, i, n_train, train_loss[iteration]))
numpy.save(
"train_loss_{}_{}_h{}_e{}".format(args.mode, args.model, args.hidden,
args.epochs), train_loss)
numpy.save("index_{}".format(args.mode), index_)
print(
"Saved index to index_{}.npy. Saved train loss to train_loss_{}_{}_h{}_e{}.npy"
.format(args.mode, args.mode, args.model, args.hidden, args.epochs))
save_params(
"params_{}_{}_h{}_e{}".format(args.mode, args.model, args.hidden,
args.epochs), params)
def train(self,
fname,
dataset,
sess_info,
epochs,
save_parameters=True,
is_labeled=False):
history = initialize_history()
(sess, saver) = sess_info
f = open_file(fname)
iterep = 500
for i in range(iterep * epochs):
batch = dataset.train.next_batch(100)
sess.run(self.train_step,
feed_dict={
'x:0': batch,
'phase:0': True
})
progbar(i, iterep)
if (i + 1) % iterep == 0:
a, b = sess.run(
[self.nent, self.loss],
feed_dict={
'x:0':
dataset.train.data[np.random.choice(
len(dataset.train.data), 200)],
'phase:0':
False
})
c, d = sess.run([self.nent, self.loss],
feed_dict={
'x:0': dataset.test.data,
'phase:0': False
})
a, b, c, d = -a.mean(), b.mean(), -c.mean(), d.mean()
e = (0, test_acc(dataset, sess, self.qy_logit))[is_labeled]
string = (
'{:>10s},{:>10s},{:>10s},{:>10s},{:>10s},{:>10s}'.format(
'tr_ent', 'tr_loss', 't_ent', 't_loss', 't_acc',
'epoch'))
stream_print(f, string, i <= iterep)
string = ('{:10.2e},{:10.2e},{:10.2e},{:10.2e},{:10.2e},{:10d}'
.format(a, b, c, d, e, int((i + 1) / iterep)))
stream_print(f, string)
qy = sess.run(self.qy,
feed_dict={
'x:0': dataset.test.data,
'phase:0': False
})
print('Sample of qy')
print(qy[:5])
history['iters'].append(int((i + 1) / iterep))
history['ent'].append(a)
history['val_ent'].append(c)
history['loss'].append(b)
history['val_loss'].append(d)
history['val_acc'].append(e)
# Saves parameters every 10 epochs
if (i + 1) % (10 * iterep) == 0 and save_parameters:
print('saving')
save_params(saver, sess, (i + 1) // iterep)
if f is not None: f.close()
return history
}
param_set_2 = {
'sr': 44100,
'window_size': 50,
'hop_length': 2205,
'server_subpaths': 'All'
}
param_set_3 = {
'sr': 44100,
'window_size': 50,
'hop_length': 2205,
'server_subpaths': 'FINAL_East African Popular Music Archive'
}
# Set the hop length; at 22050 Hz, 512 samples ~= 23ms # at 44100Hz, for 50ms use 2205 as hop_length
save_params(preproc_path, **param_set_1)
save_params(preproc_path, **param_set_2)
save_params(preproc_path, **param_set_3)
# ----- Feature extraction ----- #
user_confirmation()
# Define possible parameters for feature extraction
for method in ['mfcc', 'spectrogram']:
for duration in [5, 10]:
save_params(feature_ext_path, method=method, duration=duration)
# Define a set of preprocessing parameters and a set of feature extraction parameters to use
preproc_params = 3
feature_ext_params = 1
params_list = [preproc_params, feature_ext_params]
def main(n_hid=256, lstm_layers=2, num_epochs=100,
batch_size=32, save_to='output', max_per_epoch=-1):
# load current set of words used
words = open(c.words_used_file, 'r').readlines()
idx_to_words = dict((i+1,w.strip()) for i,w in enumerate(words))
idx_to_words[0] = '<e>'
word_dim=len(words)+1
# normalization expected by vgg-net
mean_values = np.array([104, 117, 123]).reshape((3,1,1)).astype(theano.config.floatX)
# build function for extraction convolutional features
img_var = T.tensor4('images')
net = m.build_vgg(shape=(c.img_size, c.img_size), input_var=img_var)
values = pickle.load(open(c.vgg_weights))['param values']
nn.layers.set_all_param_values(net['pool5'], values)
conv_feats = theano.function([img_var], nn.layers.get_output(net['pool5']))
conv_shape = nn.layers.get_output_shape(net['pool5'])
# helper function for converting word vector to one-hot
raw_word_var = T.matrix('seq_raw')
one_hot = theano.function([raw_word_var], nn.utils.one_hot(raw_word_var, m=word_dim))
# build expressions for lstm
conv_feats_var = T.tensor4('conv')
seq_var = T.tensor3('seq')
lstm = m.build_rnn(conv_feats_var, seq_var, conv_shape, word_dim, n_hid, lstm_layers)
output = nn.layers.get_output(lstm['output'])
output_det = nn.layers.get_output(lstm['output'], deterministic=True)
loss = m.categorical_crossentropy_logdomain(output, seq_var).mean()
te_loss = m.categorical_crossentropy_logdomain(output_det, seq_var).mean()
# compile training functions
params = nn.layers.get_all_params(lstm['output'], trainable=True)
lr = theano.shared(nn.utils.floatX(1e-3))
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([conv_feats_var, seq_var], loss, updates=updates)
test_fn = theano.function([conv_feats_var, seq_var], te_loss)
predict_fn = theano.function([conv_feats_var, seq_var], T.exp(output_det[:,-1:]))
zeros = np.zeros((batch_size, 1, word_dim), dtype=theano.config.floatX)
def transform_data(imb):
y,x = imb
# data augmentation: flip = -1 if we do flip over y-axis, 1 if not
flip = -2*np.random.binomial(1, p=0.5) + 1
# this vgg-net expects image values that are normalized by mean but not magnitude
x = (u.raw_to_floatX(x[:,:,::flip], pixel_shift=0.)\
.transpose(0,1,3,2)[:,::-1] * 255. - mean_values)
return conv_feats(x), np.concatenate([zeros, one_hot(y)], axis=1)
data = u.DataH5PyStreamer(c.twimg_hdf5_file, batch_size=batch_size)
hist = u.train_with_hdf5(data, num_epochs=num_epochs, train_fn=train_fn, test_fn=test_fn,
max_per_epoch=max_per_epoch,
tr_transform=transform_data,
te_transform=transform_data)
np.savetxt('lstm_train_hist.csv', np.asarray(hist), delimiter=',', fmt='%.5f')
u.save_params(lstm['output'], os.path.join(save_to,
'lstm_{}.npz'.format(np.asarray(hist)[-1, -1])))
# generate some example captions for one batch of images
streamer = data.streamer(training=False, shuffled=True)
y_raw, x_raw = next(streamer.get_epoch_iterator())
x, _ = transform_data((y_raw, x_raw))
y = zeros
captions = []
for idx in xrange(y.shape[0]):
captions.append([])
idx_to_words[0] = '<e>'
for sample_num in xrange(c.max_caption_len):
pred = predict_fn(x, y)
new_y = []
for idx in xrange(pred.shape[0]):
# reduce size by a small factor to prevent numerical imprecision from
# making it sum to > 1.
# reverse it so that <e> gets the additional probability, not a word
sample = np.random.multinomial(1, pred[idx,0,::-1]*.999999)[::-1]
captions[idx].append(idx_to_words[np.argmax(sample)])
new_y.append(sample)
new_y = np.vstack(new_y).reshape(-1,1,word_dim).astype(theano.config.floatX)
y = np.concatenate([y, new_y], axis=1)
captions = ['{},{}\n'.format(i, ' '.join(cap)) for i,cap in enumerate(captions)]
with open(os.path.join(save_to, 'captions_sample.csv'), 'w') as wr:
wr.writelines(captions)
for idx in xrange(x_raw.shape[0]):
Image.fromarray(x_raw[idx].transpose(2,1,0)).save(os.path.join(save_to,
'ex_{}.jpg'.format(idx)))
def main(data_file = '', num_epochs=10, batch_size = 128, L=2, z_dim=256,
n_hid=1500, binary='false', img_size = 64, init_from = '', save_to='params',
split_layer='conv7', pxsh = 0.5, specstr = c.pf_cae_specstr,
cae_weights=c.pf_cae_params, deconv_weights = c.pf_deconv_params):
binary = binary.lower() == 'true'
# pre-trained function for extracting convolutional features from images
cae = m.build_cae(input_var=None, specstr=specstr, shape=(img_size,img_size))
laydict = dict((l.name, l) for l in nn.layers.get_all_layers(cae))
convshape = nn.layers.get_output_shape(laydict[split_layer])
convs_from_img, _ = m.encoder_decoder(cae_weights, specstr=specstr, layersplit=split_layer,
shape=(img_size, img_size))
# pre-trained function for returning to images from convolutional features
img_from_convs = m.deconvoluter(deconv_weights, specstr=specstr, shape=convshape)
# Create VAE model
print("Building model and compiling functions...")
print("L = {}, z_dim = {}, n_hid = {}, binary={}".format(L, z_dim, n_hid, binary))
input_var = T.tensor4('inputs')
c,w,h = convshape[1], convshape[2], convshape[3]
l_tup = l_z_mu, l_z_ls, l_x_mu_list, l_x_ls_list, l_x_list, l_x = \
m.build_vae(input_var, L=L, binary=binary, z_dim=z_dim, n_hid=n_hid,
shape=(w,h), channels=c)
if len(init_from) > 0:
print("loading from {}".format(init_from))
u.load_params(l_x, init_from)
# build loss, updates, training, prediction functions
loss,_ = u.build_vae_loss(input_var, *l_tup, deterministic=False, binary=binary, L=L)
test_loss, test_prediction = u.build_vae_loss(input_var, *l_tup, deterministic=True,
binary=binary, L=L)
lr = theano.shared(nn.utils.floatX(1e-5))
params = nn.layers.get_all_params(l_x, trainable=True)
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([input_var], loss, updates=updates)
val_fn = theano.function([input_var], test_loss)
ae_fn = theano.function([input_var], test_prediction)
# run training loop
def data_transform(x, do_center):
floatx_ims = u.raw_to_floatX(x, pixel_shift=pxsh, square=True, center=do_center)
return convs_from_img(floatx_ims)
print("training for {} epochs".format(num_epochs))
data = u.DataH5PyStreamer(data_file, batch_size=batch_size)
hist = u.train_with_hdf5(data, num_epochs=num_epochs, train_fn=train_fn, test_fn=val_fn,
tr_transform=lambda x: data_transform(x[0], do_center=False),
te_transform=lambda x: data_transform(x[0], do_center=True))
# generate examples, save training history
te_stream = data.streamer(shuffled=True)
imb, = next(te_stream.get_epoch_iterator())
orig_feats = data_transform(imb, do_center=True)
reconstructed_feats = ae_fn(orig_feats).reshape(orig_feats.shape)
orig_feats_deconv = img_from_convs(orig_feats)
reconstructed_feats_deconv = img_from_convs(reconstructed_feats)
for i in range(reconstructed_feats_deconv.shape[0]):
u.get_image_pair(orig_feats_deconv, reconstructed_feats_deconv, index=i, shift=pxsh)\
.save('output_{}.jpg'.format(i))
hist = np.asarray(hist)
np.savetxt('vae_convs_train_hist.csv', np.asarray(hist), delimiter=',', fmt='%.5f')
u.save_params(l_x, os.path.join(save_to, 'vae_convs_{}.npz'.format(hist[-1,-1])))
def main(data_name, vae_type, fea_layer, n_iter, batch_size, K, checkpoint,
data_path):
# load data
from import_data_cifar10 import load_data_cifar10
X_train, X_test, Y_train, Y_test = load_data_cifar10(data_path, conv=True)
dimY = Y_train.shape[1]
if vae_type == 'E':
from mlp_generator_cifar10_E import generator
if vae_type == 'F':
from mlp_generator_cifar10_F import generator
if vae_type == 'G':
from mlp_generator_cifar10_G import generator
from mlp_encoder_cifar10 import encoder_gaussian as encoder
#first build the feature extractor
input_shape = X_train[0].shape
sys.path.append('test_attacks/load/')
from vgg_cifar10 import cifar10vgg
cnn = cifar10vgg(path='test_attacks/load/vgg_model/', train=False)
if fea_layer == 'low':
N_layer = 16
if fea_layer == 'mid':
N_layer = 36
if fea_layer == 'high':
N_layer = len(cnn.model.layers) - 5
for layer in cnn.model.layers:
print(layer.__class__.__name__)
def feature_extractor(x):
out = cnn.normalize_production(x * 255.0)
for i in range(N_layer):
out = cnn.model.layers[i](out)
return out
print(fea_layer, N_layer, cnn.model.layers[N_layer-1].__class__.__name__, \
cnn.model.layers[N_layer-1].get_config())
# then define model
X_ph = tf.placeholder(tf.float32, shape=(batch_size, ) + input_shape)
Y_ph = tf.placeholder(tf.float32, shape=(batch_size, dimY))
dimZ = 128 #32
dimH = 1000
fea_op = feature_extractor(X_ph)
if len(fea_op.get_shape().as_list()) == 4:
fea_op = tf.reshape(fea_op, [batch_size, -1])
dimF = fea_op.get_shape().as_list()[-1]
dec = generator(dimF, dimH, dimZ, dimY, 'linear', 'gen')
n_layers_enc = 2
enc = encoder(dimF, dimH, dimZ, dimY, n_layers_enc, 'enc')
ll = 'l2'
identity = lambda x: x
fea_ph = tf.placeholder(tf.float32, shape=(batch_size, dimF))
fit, eval_acc = construct_optimizer(fea_ph, Y_ph, [identity, enc], dec, ll,
K, vae_type)
# initialise sessions
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
path_name = data_name + '_conv_vae_fea_%s_%s/' % (vae_type, fea_layer)
if not os.path.isdir('save/' + path_name):
os.mkdir('save/' + path_name)
print('create path save/' + path_name)
filename = 'save/' + path_name + 'checkpoint'
if checkpoint < 0:
print('training from scratch')
init_variables(sess)
else:
load_params(sess, filename, checkpoint)
checkpoint += 1
# set test phase
import keras.backend
keras.backend.set_session(sess)
cnnfile = 'test_attacks/load/vgg_model/cifar10vgg.h5'
cnn.model.load_weights(cnnfile)
print('load weight from', cnnfile)
keras.backend.set_learning_phase(0)
# extract features
def gen_feature(X):
F = []
for i in range(int(X.shape[0] / batch_size)):
batch = X[i * batch_size:(i + 1) * batch_size]
F.append(sess.run(fea_op, feed_dict={X_ph: batch}))
return np.concatenate(F, axis=0)
F_train = gen_feature(X_train)
F_test = gen_feature(X_test)
# now start fitting
beta = 1.0
n_iter_ = 10
for i in range(int(n_iter / n_iter_)):
fit(sess, F_train, Y_train, n_iter_, lr, beta)
# print training and test accuracy
eval_acc(sess, F_test, Y_test, 'test', beta)
# save param values
save_params(sess, filename, checkpoint, scope='vae')
checkpoint += 1
def main(data_file='',
img_size=64,
num_epochs=10,
batch_size=128,
pxsh=0.5,
split_layer='conv7',
specstr=c.pf_cae_specstr,
cae_params=c.pf_cae_params,
save_to='params'):
# transform function to go from images -> conv feats
conv_feats, _ = m.encoder_decoder(cae_params,
specstr=specstr,
layersplit=split_layer,
shape=(img_size, img_size))
# build pretrained net for images -> convfeats in order to get the input shape
# for the reverse function
print('compiling functions')
conv_net = m.build_cae(input_var=None,
specstr=specstr,
shape=(img_size, img_size))
cae_layer_dict = dict(
(l.name, l) for l in nn.layers.get_all_layers(conv_net))
shape = nn.layers.get_output_shape(cae_layer_dict[split_layer])
# build net for convfeats -> images
imgs_var = T.tensor4('images')
convs_var = T.tensor4('conv_features')
deconv_net = m.build_deconv_net(input_var=convs_var,
shape=shape,
specstr=specstr)
loss = nn.objectives.squared_error(
imgs_var, nn.layers.get_output(deconv_net)).mean()
te_loss = nn.objectives.squared_error(
imgs_var, nn.layers.get_output(deconv_net, deterministic=True)).mean()
params = nn.layers.get_all_params(deconv_net, trainable=True)
lr = theano.shared(nn.utils.floatX(3e-3))
updates = nn.updates.adam(loss, params, learning_rate=lr)
# compile functions
train_fn = theano.function([convs_var, imgs_var], loss, updates=updates)
val_fn = theano.function([convs_var, imgs_var], te_loss)
deconv_fn = theano.function([convs_var],
nn.layers.get_output(deconv_net,
deterministic=True))
# run training loop
print("training for {} epochs".format(num_epochs))
def data_transform(x, do_center):
floatx_ims = u.raw_to_floatX(x,
pixel_shift=pxsh,
square=True,
center=do_center)
return (conv_feats(floatx_ims), floatx_ims)
data = u.DataH5PyStreamer(data_file, batch_size=batch_size)
hist = u.train_with_hdf5(
data,
num_epochs=num_epochs,
train_fn=train_fn,
test_fn=val_fn,
tr_transform=lambda x: data_transform(x[0], do_center=False),
te_transform=lambda x: data_transform(x[0], do_center=True))
# generate examples, save training history and params
te_stream = data.streamer(shuffled=True)
imb, = next(te_stream.get_epoch_iterator())
imb = data_transform(imb, True)[0]
result = deconv_fn(imb)
for i in range(result.shape[0]):
Image.fromarray(u.get_picture_array(result, index=i, shift=pxsh)) \
.save('output_{}.jpg'.format(i))
hist = np.asarray(hist)
np.savetxt('deconv_train_hist.csv',
np.asarray(hist),
delimiter=',',
fmt='%.5f')
u.save_params(deconv_net,
os.path.join(save_to, 'deconv_{}.npz'.format(hist[-1, -1])))
def main(args):
logging.info('-' * 50)
logging.info('Load data files..')
if args.debug:
logging.info('*' * 10 + ' Train')
train_examples = utils.load_data(args.train_file,
100,
relabeling=args.relabeling)
logging.info('*' * 10 + ' Dev')
dev_examples = utils.load_data(args.dev_file,
100,
relabeling=args.relabeling)
else:
logging.info('*' * 10 + ' Train')
train_examples = utils.load_data(args.train_file,
relabeling=args.relabeling)
logging.info('*' * 10 + ' Dev')
dev_examples = utils.load_data(args.dev_file,
args.max_dev,
relabeling=args.relabeling)
args.num_train = len(train_examples[0])
args.num_dev = len(dev_examples[0])
logging.info('-' * 50)
logging.info('Build dictionary..')
word_dict = utils.build_dict(train_examples[0] + train_examples[1])
entity_markers = list(
set([w for w in word_dict.keys() if w.startswith('@entity')] +
train_examples[2]))
entity_markers = ['<unk_entity>'] + entity_markers
entity_dict = {w: index for (index, w) in enumerate(entity_markers)}
logging.info('Entity markers: %d' % len(entity_dict))
args.num_labels = len(entity_dict)
logging.info('-' * 50)
# Load embedding file
embeddings = utils.gen_embeddings(word_dict, args.embedding_size,
args.embedding_file)
(args.vocab_size, args.embedding_size) = embeddings.shape
logging.info('Compile functions..')
train_fn, test_fn, params = build_fn(args, embeddings)
logging.info('Done.')
if args.prepare_model:
return train_fn, test_fn, params
logging.info('-' * 50)
logging.info(args)
logging.info('-' * 50)
logging.info('Intial test..')
dev_x1, dev_x2, dev_l, dev_y = utils.vectorize(dev_examples, word_dict,
entity_dict)
assert len(dev_x1) == args.num_dev
all_dev = gen_examples(dev_x1, dev_x2, dev_l, dev_y, args.batch_size)
dev_acc = eval_acc(test_fn, all_dev)
logging.info('Dev accuracy: %.2f %%' % dev_acc)
best_acc = dev_acc
if args.test_only:
return
utils.save_params(args.model_file, params, epoch=0, n_updates=0)
# Training
logging.info('-' * 50)
logging.info('Start training..')
train_x1, train_x2, train_l, train_y = utils.vectorize(
train_examples, word_dict, entity_dict)
assert len(train_x1) == args.num_train
start_time = time.time()
n_updates = 0
all_train = gen_examples(train_x1, train_x2, train_l, train_y,
args.batch_size)
for epoch in range(args.num_epoches):
np.random.shuffle(all_train)
for idx, (mb_x1, mb_mask1, mb_x2, mb_mask2, mb_l,
mb_y) in enumerate(all_train):
logging.info('#Examples = %d, max_len = %d' %
(len(mb_x1), mb_x1.shape[1]))
train_loss = train_fn(mb_x1, mb_mask1, mb_x2, mb_mask2, mb_l, mb_y)
logging.info(
'Epoch = %d, iter = %d (max = %d), loss = %.2f, elapsed time = %.2f (s)'
% (epoch, idx, len(all_train), train_loss,
time.time() - start_time))
n_updates += 1
if n_updates % args.eval_iter == 0:
samples = sorted(
np.random.choice(args.num_train,
min(args.num_train, args.num_dev),
replace=False))
sample_train = gen_examples([train_x1[k] for k in samples],
[train_x2[k] for k in samples],
train_l[samples],
[train_y[k] for k in samples],
args.batch_size)
logging.info('Train accuracy: %.2f %%' %
eval_acc(test_fn, sample_train))
dev_acc = eval_acc(test_fn, all_dev)
logging.info('Dev accuracy: %.2f %%' % dev_acc)
if dev_acc > best_acc:
best_acc = dev_acc
logging.info(
'Best dev accuracy: epoch = %d, n_udpates = %d, acc = %.2f %%'
% (epoch, n_updates, dev_acc))
utils.save_params(args.model_file,
params,
epoch=epoch,
n_updates=n_updates)
def main(args):
logging.info('-' * 50)
logging.info('Load data files..')
if not (args.test_only):
logging.info('*' * 10 + ' All')
all_examples = utils.load_data(args.all_file,
100,
relabeling=args.relabeling)
dev_ratio = args.dev_ratio
sample_index = np.arange(len(all_examples[0]))
random.seed(1000)
dev_index = random.sample(sample_index,
int(dev_ratio * len(sample_index)))
train_index = np.setdiff1d(sample_index, dev_index)
dev_examples = tuple_part(all_examples, dev_index)
train_examples = tuple_part(all_examples, train_index)
#feature preprocessing
train_fea_flat_np = FeaExtract(train_examples[-1])
dev_fea_flat_np = FeaExtract(dev_examples[-1])
train_fea_flat_np2, dev_fea_flat_np2 = Prepocessing_func(
train_fea_flat_np,
dev_fea_flat_np,
varian_ratio_tol=args.pca_ratio)
train_fea_merge = FeaMerge(train_fea_flat_np2, train_examples[-1])
dev_fea_merge = FeaMerge(dev_fea_flat_np2, dev_examples[-1])
train_examples = train_examples[:-1] + (train_fea_merge, )
dev_examples = dev_examples[:-1] + (dev_fea_merge, )
args.num_train = len(train_examples[0])
else:
# logging.info('*' * 10 + ' Train')
# train_examples = utils.load_data(args.train_file, relabeling=args.relabeling)
logging.info('*' * 10 + ' Dev')
dev_examples = utils.load_data(args.dev_file,
args.max_dev,
relabeling=args.relabeling)
dev_fea_flat_np = FeaExtract(dev_examples[-1])
dev_fea_flat_np2 = PrepocessingApply_func(dev_fea_flat_np)
dev_fea_merge = FeaMerge(dev_fea_flat_np2, dev_examples[-1])
dev_examples = dev_examples[:-1] + (dev_fea_merge, )
args.num_dev = len(dev_examples[0])
args.mea_num = dev_examples[4][0].shape[-1]
logging.info('-' * 50)
logging.info('Build dictionary..')
word_dict = pickle.load(open("../../obj/dict.pkl", "rb"))
logging.info('-' * 50)
embeddings = utils.gen_embeddings(word_dict, args.embedding_size,
args.embedding_file)
(args.vocab_size, args.embedding_size) = embeddings.shape
logging.info('Compile functions..')
train_fn, test_fn, params, all_params = build_fn(args, embeddings)
logging.info('Done.')
logging.info('-' * 50)
logging.info(args)
logging.info('-' * 50)
logging.info('Intial test..')
dev_x1, dev_x2, dev_x3, dev_y, dev_x4 = utils.vectorize(
dev_examples,
word_dict,
sort_by_len=not args.test_only,
concat=args.concat)
word_dict_r = {}
word_dict_r[0] = "unk"
assert len(dev_x1) == args.num_dev
all_dev = gen_examples(dev_x1, dev_x2, dev_x3, dev_y, dev_x4,
args.batch_size, args.concat)
dev_acc, rediction = eval_acc(test_fn, all_dev)
logging.info('Dev accuracy: %.2f %%' % dev_acc.mean())
print(dev_acc.mean())
best_dev_acc = dev_acc
best_train_acc = 0
if args.test_only:
return dev_acc, best_train_acc
utils.save_params(args.model_file, all_params, epoch=0, n_updates=0)
# Training
logging.info('-' * 50)
logging.info('Start training..')
train_x1, train_x2, train_x3, train_y, train_x4 = utils.vectorize(
train_examples, word_dict, concat=args.concat)
assert len(train_x1) == args.num_train
start_time = time.time()
n_updates = 0
all_train = gen_examples(train_x1, train_x2, train_x3, train_y, train_x4,
args.batch_size, args.concat)
for epoch in range(args.num_epoches):
np.random.shuffle(all_train)
for idx, (mb_x1, mb_mask1, mb_x2, mb_mask2, mb_x3, mb_mask3, mb_y,
mb_x4, mb_mask4) in enumerate(all_train):
train_loss = train_fn(mb_x1, mb_mask1, mb_x3, mb_mask3, mb_y,
mb_x4)
# if idx % 100 == 0:
# if epoch % 100 == 0:
# logging.info('#Examples = %d, max_len = %d' % (len(mb_x1), mb_x1.shape[1]))
# logging.info('Epoch = %d, iter = %d (max = %d), loss = %.2f, elapsed time = %.2f (s)' % (epoch, idx, len(all_train), train_loss, time.time() - start_time))
n_updates += 1
if n_updates % args.eval_iter == 0:
print([x.get_value() for x in params])
print([x.get_value() for x in all_params])
samples = sorted(
np.random.choice(args.num_train,
min(args.num_train, args.num_dev),
replace=False))
sample_train = gen_examples(
[train_x1[k]
for k in samples], [train_x2[k] for k in samples],
[train_x3[k * 4 + o] for k in samples
for o in range(4)], [train_y[k] for k in samples],
[train_x4[k]
for k in samples], args.batch_size, args.concat)
acc, pred = eval_acc(test_fn, sample_train)
logging.info('Train accuracy: %.2f %%' % acc)
train_acc, pred = eval_acc(test_fn, all_train)
logging.info('train accuracy: %.2f %%' % train_acc)
dev_acc, pred = eval_acc(test_fn, all_dev)
logging.info('Dev accuracy: %.2f %%' % dev_acc)
if dev_acc > best_dev_acc:
best_dev_acc = dev_acc
logging.info(
'Best dev accuracy: epoch = %d, n_udpates = %d, acc = %.2f %%'
% (epoch, n_updates, best_dev_acc))
best_train_acc = acc
logging.info(
'Best train accuracy: epoch = %d, n_udpates = %d, acc = %.2f %%'
% (epoch, n_updates, best_train_acc))
utils.save_params(
args.model_file,
all_params,
epoch=epoch,
n_updates=n_updates,
)
return best_dev_acc, best_train_acc
def main(specstr=default_specstr,
z_dim=256,
num_epochs=10,
ch=3,
init_from='',
img_size=64,
pxsh=0.5,
data_file='',
batch_size=8,
save_to='params'):
# build expressions for the output, loss, gradient
input_var = T.tensor4('inputs')
print('building specstr {} - zdim {}'.format(specstr, z_dim))
cae = m.build_cae_nopoolinv(input_var,
shape=(img_size, img_size),
channels=ch,
specstr=specstr.format(z_dim))
l_list = nn.layers.get_all_layers(cae)
pred = nn.layers.get_output(cae)
loss = nn.objectives.squared_error(pred, input_var.flatten(2)).mean()
params = nn.layers.get_all_params(cae, trainable=True)
grads = nn.updates.total_norm_constraint(T.grad(loss, params), 10)
updates = nn.updates.adam(grads, params, learning_rate=1e-3)
te_pred = nn.layers.get_output(cae, deterministic=True)
te_loss = nn.objectives.squared_error(te_pred, input_var.flatten(2)).mean()
# training functions
print('compiling functions')
train_fn = theano.function([input_var], loss, updates=updates)
val_fn = theano.function([input_var], te_loss)
# compile functions for encode/decode to test later
enc_layer = l_list[next(i for i in xrange(len(l_list))
if l_list[i].name == 'encode')]
enc_fn = theano.function([input_var],
nn.layers.get_output(enc_layer,
deterministic=True))
dec_fn = lambda z: nn.layers.get_output(
cae,
deterministic=True,
inputs={
l_list[0]:
np.zeros((z.shape[0], ch, img_size, img_size),
dtype=theano.config.floatX),
enc_layer:
z
}).eval().reshape(-1, ch, img_size, img_size)
# load params if requested, run training
if len(init_from) > 0:
print('loading params from {}'.format(init_from))
load_params(cae, init_from)
data = u.DataH5PyStreamer(data_file, batch_size=batch_size)
print('training for {} epochs'.format(num_epochs))
hist = u.train_with_hdf5(data,
num_epochs=num_epochs,
train_fn=train_fn,
test_fn=val_fn,
tr_transform=lambda x: u.raw_to_floatX(
x[0], pixel_shift=pxsh, center=False),
te_transform=lambda x: u.raw_to_floatX(
x[0], pixel_shift=pxsh, center=True))
# generate examples, save training history
te_stream = data.streamer(shuffled=True)
imb, = next(te_stream.get_epoch_iterator())
tg = u.raw_to_floatX(imb, pixel_shift=pxsh, square=True, center=True)
pr = dec_fn(enc_fn(tg))
for i in range(pr.shape[0]):
u.get_image_pair(tg, pr, index=i,
shift=pxsh).save('output_{}.jpg'.format(i))
hist = np.asarray(hist)
np.savetxt('cae_train_hist.csv',
np.asarray(hist),
delimiter=',',
fmt='%.5f')
u.save_params(cae, os.path.join(save_to, 'cae_{}.npz'.format(hist[-1,
-1])))
weight_decay=optim_params["weight_decay"])
elif "SGD" == optim_params["name"]:
optimizer = optim.SGD(net.get_params_lr(
lr_not_pretrained=optim_params["lr_not_pretrained"],
lr_pretrained=optim_params["lr_pretrained"]),
momentum=optim_params["momentum"],
weight_decay=optim_params["weight_decay"])
# 学習
train_net(net,
train_loader,
test_loader,
optimizer=optimizer,
loss_fn=loss_fn,
epochs=params["epochs"],
device=device)
# 推論
y, ypred = eval_net(net, test_loader, probability=True, device=device)
# 正答率とネットワークの重みをリストに追加
ys.append(y.cpu().numpy())
ypreds.append(ypred.cpu().numpy())
recall = recall_score(
ys[-1], ypreds[-1].argmax(1), average=None, zero_division=0) * 100
print("テストの各クラスrecall：\n{}\n平均：{}".format(
np.round(recall, decimals=1), np.round(recall.mean(), decimals=1)))
net_weights.append(net.cpu().state_dict())
utils.print_result(params, ys, ypreds)
utils.save_params(params, net_weights)
def main(args):
logging.info('-' * 50)
logging.info('Load data files..')
if args.debug:
logging.info('*' * 10 + ' Train')
train_examples = utils.load_data(args.train_file, 100, relabeling=args.relabeling)
logging.info('*' * 10 + ' Dev')
dev_examples = utils.load_data(args.dev_file, 100, relabeling=args.relabeling)
else:
logging.info('*' * 10 + ' Train')
train_examples = utils.load_data(args.train_file, relabeling=args.relabeling)
logging.info('*' * 10 + ' Dev')
dev_examples = utils.load_data(args.dev_file, args.max_dev, relabeling=args.relabeling)
args.num_train = len(train_examples[0])
args.num_dev = len(dev_examples[0])
logging.info('-' * 50)
logging.info('Build dictionary..')
word_dict = utils.build_dict(train_examples[0] + train_examples[1])
entity_markers = list(set([w for w in word_dict.keys()
if w.startswith('@entity')] + train_examples[2]))
entity_markers = ['<unk_entity>'] + entity_markers
entity_dict = {w: index for (index, w) in enumerate(entity_markers)}
logging.info('Entity markers: %d' % len(entity_dict))
args.num_labels = len(entity_dict)
logging.info('-' * 50)
# Load embedding file
embeddings = utils.gen_embeddings(word_dict, args.embedding_size, args.embedding_file)
(args.vocab_size, args.embedding_size) = embeddings.shape
logging.info('Compile functions..')
train_fn, test_fn, params = build_fn(args, embeddings)
logging.info('Done.')
logging.info('-' * 50)
logging.info(args)
logging.info('-' * 50)
logging.info('Intial test..')
dev_x1, dev_x2, dev_l, dev_y = utils.vectorize(dev_examples, word_dict, entity_dict)
assert len(dev_x1) == args.num_dev
all_dev = gen_examples(dev_x1, dev_x2, dev_l, dev_y, args.batch_size)
dev_acc = eval_acc(test_fn, all_dev)
logging.info('Dev accuracy: %.2f %%' % dev_acc)
best_acc = dev_acc
if args.test_only:
return
utils.save_params(args.model_file, params, epoch=0, n_updates=0)
# Training
logging.info('-' * 50)
logging.info('Start training..')
train_x1, train_x2, train_l, train_y = utils.vectorize(train_examples, word_dict, entity_dict)
assert len(train_x1) == args.num_train
start_time = time.time()
n_updates = 0
all_train = gen_examples(train_x1, train_x2, train_l, train_y, args.batch_size)
for epoch in range(args.num_epoches):
np.random.shuffle(all_train)
for idx, (mb_x1, mb_mask1, mb_x2, mb_mask2, mb_l, mb_y) in enumerate(all_train):
logging.info('#Examples = %d, max_len = %d' % (len(mb_x1), mb_x1.shape[1]))
train_loss = train_fn(mb_x1, mb_mask1, mb_x2, mb_mask2, mb_l, mb_y)
logging.info('Epoch = %d, iter = %d (max = %d), loss = %.2f, elapsed time = %.2f (s)' %
(epoch, idx, len(all_train), train_loss, time.time() - start_time))
n_updates += 1
if n_updates % args.eval_iter == 0:
samples = sorted(np.random.choice(args.num_train, min(args.num_train, args.num_dev),
replace=False))
sample_train = gen_examples([train_x1[k] for k in samples],
[train_x2[k] for k in samples],
train_l[samples],
[train_y[k] for k in samples],
args.batch_size)
logging.info('Train accuracy: %.2f %%' % eval_acc(test_fn, sample_train))
logging.info('Dev accuracy: %.2f %%' % eval_acc(test_fn, all_dev))
if dev_acc > best_acc:
best_acc = dev_acc
logging.info('Best dev accuracy: epoch = %d, n_udpates = %d, acc = %.2f %%'
% (epoch, n_updates, dev_acc))
utils.save_params(args.model_file, params, epoch=epoch, n_updates=n_updates)
def main(data_name, method, dimZ, dimH, n_channel, batch_size, K_mc,
checkpoint, lbd):
# set up dataset specific stuff
from config import config
labels, n_iter, dimX, shape_high, ll = config(data_name, n_channel)
if data_name == 'mnist':
from mnist import load_mnist
if data_name == 'notmnist':
from notmnist import load_notmnist
# import functionalities
if method == 'onlinevi':
from bayesian_generator import generator_head, generator_shared, \
generator, construct_gen
from onlinevi import construct_optimizer, init_shared_prior, \
update_shared_prior, update_q_sigma
if method in ['ewc', 'noreg', 'laplace', 'si']:
from generator import generator_head, generator_shared, generator, construct_gen
if method in ['ewc', 'noreg']:
from vae_ewc import construct_optimizer, lowerbound
if method == 'ewc': from vae_ewc import update_ewc_loss, compute_fisher
if method == 'laplace':
from vae_laplace import construct_optimizer, lowerbound
from vae_laplace import update_laplace_loss, compute_fisher, init_fisher_accum
if method == 'si':
from vae_si import construct_optimizer, lowerbound, update_si_reg
# then define model
n_layers_shared = 2
batch_size_ph = tf.placeholder(tf.int32, shape=(), name='batch_size')
dec_shared = generator_shared(dimX, dimH, n_layers_shared, 'sigmoid',
'gen')
# initialise sessions
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
string = method
if method in ['ewc', 'laplace', 'si']:
string = string + '_lbd%.1f' % lbd
if method == 'onlinevi' and K_mc > 1:
string = string + '_K%d' % K_mc
path_name = data_name + '_%s/' % string
if not os.path.isdir('save/'):
os.mkdir('save/')
if not os.path.isdir('save/' + path_name):
os.mkdir('save/' + path_name)
print 'create path save/' + path_name
filename = 'save/' + path_name + 'checkpoint'
if checkpoint < 0:
print 'training from scratch'
old_var_list = init_variables(sess)
else:
load_params(sess, filename, checkpoint)
checkpoint += 1
# visualise the samples
N_gen = 10**2
path = 'figs/' + path_name
if not os.path.isdir('figs/'):
os.mkdir('figs/')
if not os.path.isdir(path):
os.mkdir(path)
print 'create path ' + path
X_ph = tf.placeholder(tf.float32, shape=(batch_size, dimX), name='x_ph')
# now start fitting
N_task = len(labels)
gen_ops = []
X_valid_list = []
X_test_list = []
eval_func_list = []
result_list = []
if method == 'onlinevi':
shared_prior_params = init_shared_prior()
if method in ['ewc', 'noreg']:
ewc_loss = 0.0
if method == 'laplace':
F_accum = init_fisher_accum()
laplace_loss = 0.0
if method == 'si':
old_params_shared = None
si_reg = None
n_layers_head = 2
n_layers_enc = n_layers_shared + n_layers_head - 1
for task in xrange(1, N_task + 1):
# first load data
if data_name == 'mnist':
X_train, X_test, _, _ = load_mnist(digits=labels[task - 1],
conv=False)
if data_name == 'notmnist':
X_train, X_test, _, _ = load_notmnist(data_path,
digits=labels[task - 1],
conv=False)
N_train = int(X_train.shape[0] * 0.9)
X_valid_list.append(X_train[N_train:])
X_train = X_train[:N_train]
X_test_list.append(X_test)
# define the head net and the generator ops
dec = generator(
generator_head(dimZ, dimH, n_layers_head, 'gen_%d' % task),
dec_shared)
enc = encoder(dimX, dimH, dimZ, n_layers_enc, 'enc_%d' % task)
gen_ops.append(construct_gen(dec, dimZ, sampling=False)(N_gen))
print 'construct eval function...'
eval_func_list.append(construct_eval_func(X_ph, enc, dec, ll, \
batch_size_ph, K=100, sample_W=False))
# then construct loss func and fit func
print 'construct fit function...'
if method == 'onlinevi':
fit = construct_optimizer(X_ph, enc, dec, ll, X_train.shape[0], batch_size_ph, \
shared_prior_params, task, K_mc)
if method in ['ewc', 'noreg']:
bound = lowerbound(X_ph, enc, dec, ll)
fit = construct_optimizer(X_ph, batch_size_ph, bound,
X_train.shape[0], ewc_loss)
if method == 'ewc':
fisher, var_list = compute_fisher(X_ph, batch_size_ph, bound,
X_train.shape[0])
if method == 'laplace':
bound = lowerbound(X_ph, enc, dec, ll)
fit = construct_optimizer(X_ph, batch_size_ph, bound,
X_train.shape[0], laplace_loss)
fisher, var_list = compute_fisher(X_ph, batch_size_ph, bound,
X_train.shape[0])
if method == 'si':
bound = lowerbound(X_ph, enc, dec, ll)
fit, shared_var_list = construct_optimizer(X_ph, batch_size_ph,
bound, X_train.shape[0],
si_reg,
old_params_shared, lbd)
if old_params_shared is None:
old_params_shared = sess.run(shared_var_list)
# initialise all the uninitialised stuff
old_var_list = init_variables(sess, old_var_list)
# start training for each task
if method == 'si':
new_params_shared, w_params_shared = fit(sess, X_train, n_iter, lr)
else:
fit(sess, X_train, n_iter, lr)
# plot samples
x_gen_list = sess.run(gen_ops, feed_dict={batch_size_ph: N_gen})
for i in xrange(len(x_gen_list)):
plot_images(x_gen_list[i], shape_high, path, \
data_name + '_gen_task%d_%d' % (task, i + 1))
x_list = [x_gen_list[i][:1] for i in xrange(len(x_gen_list))]
x_list = np.concatenate(x_list, 0)
tmp = np.zeros([10, dimX])
tmp[:task] = x_list
if task == 1:
x_gen_all = tmp
else:
x_gen_all = np.concatenate([x_gen_all, tmp], 0)
# print test-ll on all tasks
tmp_list = []
for i in xrange(len(eval_func_list)):
print 'task %d' % (i + 1),
test_ll = eval_func_list[i](sess, X_valid_list[i])
tmp_list.append(test_ll)
result_list.append(tmp_list)
# save param values
save_params(sess, filename, checkpoint)
checkpoint += 1
# update regularisers/priors
if method == 'ewc':
# update EWC loss
print 'update ewc loss...'
X_batch = X_train[np.random.permutation(range(
X_train.shape[0]))[:batch_size]]
ewc_loss = update_ewc_loss(sess, ewc_loss, var_list, fisher, lbd,
X_batch)
if method == 'laplace':
# update EWC loss
print 'update laplace loss...'
X_batch = X_train[np.random.permutation(range(
X_train.shape[0]))[:batch_size]]
laplace_loss, F_accum = update_laplace_loss(
sess, F_accum, var_list, fisher, lbd, X_batch)
if method == 'onlinevi':
# update prior
print 'update prior...'
shared_prior_params = update_shared_prior(sess,
shared_prior_params)
# reset the variance of q
update_q_sigma(sess)
if method == 'si':
# update regularisers/priors
print 'update SI big omega matrices...'
si_reg, _ = update_si_reg(sess, si_reg, new_params_shared,
old_params_shared, w_params_shared)
old_params_shared = new_params_shared
plot_images(x_gen_all, shape_high, path, data_name + '_gen_all')
for i in xrange(len(result_list)):
print result_list[i]
# save results
fname = 'results/' + data_name + '_%s.pkl' % string
import pickle
pickle.dump(result_list, open(fname, 'wb'))
print 'test-ll results saved in', fname
def main(save_to='params',
dataset = 'mm',
kl_loss='true', # use kl-div in z-space instead of mse
diffs = 'false',
seq_length = 30,
num_epochs=1,
lstm_n_hid=1024,
max_per_epoch=-1
):
kl_loss = kl_loss.lower() == 'true'
diffs = diffs.lower() == 'true'
# set up functions for data pre-processing and model training
input_var = T.tensor4('inputs')
# different experimental setup for moving mnist vs pulp fiction dataests
if dataset == 'pf':
img_size = 64
cae_weights = c.pf_cae_params
cae_specstr = c.pf_cae_specstr
split_layer = 'conv7'
inpvar = T.tensor4('input')
net = m.build_cae(inpvar, specstr=cae_specstr, shape=(img_size, img_size))
convs_from_img,_ = m.encoder_decoder(cae_weights, specstr=cae_specstr,
layersplit=split_layer, shape=(img_size, img_size), poolinv=True)
laydict = dict((l.name, l) for l in nn.layers.get_all_layers(net))
zdec_in_shape = nn.layers.get_output_shape(laydict[split_layer])
deconv_weights = c.pf_deconv_params
vae_weights = c.pf_vae_params
img_from_convs = m.deconvoluter(deconv_weights, specstr=cae_specstr, shape=zdec_in_shape)
L=2
vae_n_hid = 1500
binary = False
z_dim = 256
l_tup = l_z_mu, l_z_ls, l_x_mu_list, l_x_ls_list, l_x_list, l_x = \
m.build_vae(input_var, L=L, binary=binary, z_dim=z_dim, n_hid=vae_n_hid,
shape=(zdec_in_shape[2], zdec_in_shape[3]), channels=zdec_in_shape[1])
u.load_params(l_x, vae_weights)
datafile = 'data/pf.hdf5'
frame_skip=3 # every 3rd frame in sequence
z_decode_layer = l_x_mu_list[0]
pixel_shift = 0.5
samples_per_image = 4
tr_batch_size = 16 # must be a multiple of samples_per_image
elif dataset == 'mm':
img_size = 64
cvae_weights = c.mm_cvae_params
L=2
vae_n_hid = 1024
binary = True
z_dim = 32
zdec_in_shape = (None, 1, img_size, img_size)
l_tup = l_z_mu, l_z_ls, l_x_mu_list, l_x_ls_list, l_x_list, l_x = \
m.build_vcae(input_var, L=L, z_dim=z_dim, n_hid=vae_n_hid, binary=binary,
shape=(zdec_in_shape[2], zdec_in_shape[3]), channels=zdec_in_shape[1])
u.load_params(l_x, cvae_weights)
datafile = 'data/moving_mnist.hdf5'
frame_skip=1
w,h=img_size,img_size # of raw input image in the hdf5 file
z_decode_layer = l_x_list[0]
pixel_shift = 0
samples_per_image = 1
tr_batch_size = 128 # must be a multiple of samples_per_image
# functions for moving to/from image or conv-space, and z-space
z_mat = T.matrix('z')
zenc = theano.function([input_var], nn.layers.get_output(l_z_mu, deterministic=True))
zdec = theano.function([z_mat], nn.layers.get_output(z_decode_layer, {l_z_mu:z_mat},
deterministic=True).reshape((-1, zdec_in_shape[1]) + zdec_in_shape[2:]))
zenc_ls = theano.function([input_var], nn.layers.get_output(l_z_ls, deterministic=True))
# functions for encoding sequences of z's
print 'compiling functions'
z_var = T.tensor3('z_in')
z_ls_var = T.tensor3('z_ls_in')
tgt_mu_var = T.tensor3('z_tgt')
tgt_ls_var = T.tensor3('z_ls_tgt')
learning_rate = theano.shared(nn.utils.floatX(1e-4))
# separate function definitions if we are using MSE and predicting only z, or KL divergence
# and predicting both mean and sigma of z
if kl_loss:
def kl(p_mu, p_sigma, q_mu, q_sigma):
return 0.5 * T.sum(T.sqr(p_sigma)/T.sqr(q_sigma) + T.sqr(q_mu - p_mu)/T.sqr(q_sigma)
- 1 + 2*T.log(q_sigma) - 2*T.log(p_sigma))
lstm, _ = m.Z_VLSTM(z_var, z_ls_var, z_dim=z_dim, nhid=lstm_n_hid, training=True)
z_mu_expr, z_ls_expr = nn.layers.get_output([lstm['output_mu'], lstm['output_ls']])
z_mu_expr_det, z_ls_expr_det = nn.layers.get_output([lstm['output_mu'],
lstm['output_ls']], deterministic=True)
loss = kl(tgt_mu_var, T.exp(tgt_ls_var), z_mu_expr, T.exp(z_ls_expr))
te_loss = kl(tgt_mu_var, T.exp(tgt_ls_var), z_mu_expr_det, T.exp(z_ls_expr_det))
params = nn.layers.get_all_params(lstm['output'], trainable=True)
updates = nn.updates.adam(loss, params, learning_rate=learning_rate)
train_fn = theano.function([z_var, z_ls_var, tgt_mu_var, tgt_ls_var], loss,
updates=updates)
test_fn = theano.function([z_var, z_ls_var, tgt_mu_var, tgt_ls_var], te_loss)
else:
lstm, _ = m.Z_LSTM(z_var, z_dim=z_dim, nhid=lstm_n_hid, training=True)
loss = nn.objectives.squared_error(nn.layers.get_output(lstm['output']),
tgt_mu_var).mean()
te_loss = nn.objectives.squared_error(nn.layers.get_output(lstm['output'],
deterministic=True), tgt_mu_var).mean()
params = nn.layers.get_all_params(lstm['output'], trainable=True)
updates = nn.updates.adam(loss, params, learning_rate=learning_rate)
train_fn = theano.function([z_var, tgt_mu_var], loss, updates=updates)
test_fn = theano.function([z_var, tgt_mu_var], te_loss)
if dataset == 'pf':
z_from_img = lambda x: zenc(convs_from_img(x))
z_ls_from_img = lambda x: zenc_ls(convs_from_img(x))
img_from_z = lambda z: img_from_convs(zdec(z))
elif dataset == 'mm':
z_from_img = zenc
z_ls_from_img = zenc_ls
img_from_z = zdec
# training loop
print('training for {} epochs'.format(num_epochs))
nbatch = (seq_length+1) * tr_batch_size * frame_skip / samples_per_image
data = u.DataH5PyStreamer(datafile, batch_size=nbatch)
# for taking arrays of uint8 (non square) and converting them to batches of sequences
def transform_data(ims_batch, center=False):
imb = u.raw_to_floatX(ims_batch, pixel_shift=pixel_shift,
center=center)[np.random.randint(frame_skip)::frame_skip]
zbatch = np.zeros((tr_batch_size, seq_length+1, z_dim), dtype=theano.config.floatX)
zsigbatch = np.zeros((tr_batch_size, seq_length+1, z_dim), dtype=theano.config.floatX)
for i in xrange(samples_per_image):
chunk = tr_batch_size/samples_per_image
if diffs:
zf = z_from_img(imb).reshape((chunk, seq_length+1, -1))
zbatch[i*chunk:(i+1)*chunk, 1:] = zf[:,1:] - zf[:,:-1]
if kl_loss:
zls = z_ls_from_img(imb).reshape((chunk, seq_length+1, -1))
zsigbatch[i*chunk:(i+1)*chunk, 1:] = zls[:,1:] - zls[:,:-1]
else:
zbatch[i*chunk:(i+1)*chunk] = z_from_img(imb).reshape((chunk, seq_length+1, -1))
if kl_loss:
zsigbatch[i*chunk:(i+1)*chunk] = z_ls_from_img(imb).reshape((chunk,
seq_length+1, -1))
if kl_loss:
return zbatch[:,:-1,:], zsigbatch[:,:-1,:], zbatch[:,1:,:], zsigbatch[:,1:,:]
return zbatch[:,:-1,:], zbatch[:,1:,:]
# we need sequences of images, so we do not shuffle data during trainin
hist = u.train_with_hdf5(data, num_epochs=num_epochs, train_fn=train_fn, test_fn=test_fn,
train_shuffle=False,
max_per_epoch=max_per_epoch,
tr_transform=lambda x: transform_data(x[0], center=False),
te_transform=lambda x: transform_data(x[0], center=True))
hist = np.asarray(hist)
u.save_params(lstm['output'], os.path.join(save_to, 'lstm_{}.npz'.format(hist[-1,-1])))
# build functions to sample from LSTM
# separate cell_init and hid_init from the other learned model parameters
all_param_values = nn.layers.get_all_param_values(lstm['output'])
init_indices = [i for i,p in enumerate(nn.layers.get_all_params(lstm['output']))
if 'init' in str(p)]
init_values = [all_param_values[i] for i in init_indices]
params_noinit = [p for i,p in enumerate(all_param_values) if i not in init_indices]
# build model without learnable init values, and load non-init parameters
if kl_loss:
lstm_sample, state_vars = m.Z_VLSTM(z_var, z_ls_var, z_dim=z_dim, nhid=lstm_n_hid,
training=False)
else:
lstm_sample, state_vars = m.Z_LSTM(z_var, z_dim=z_dim, nhid=lstm_n_hid, training=False)
nn.layers.set_all_param_values(lstm_sample['output'], params_noinit)
# extract layers representing thee hidden and cell states, and have sample_fn
# return their outputs
state_layers_keys = [k for k in lstm_sample.keys() if 'hidfinal' in k or 'cellfinal' in k]
state_layers_keys = sorted(state_layers_keys)
state_layers_keys = sorted(state_layers_keys, key = lambda x:int(x.split('_')[1]))
state_layers = [lstm_sample[s] for s in state_layers_keys]
if kl_loss:
sample_fn = theano.function([z_var, z_ls_var] + state_vars,
nn.layers.get_output([lstm['output_mu'], lstm['output_ls']] + state_layers,
deterministic=True))
else:
sample_fn = theano.function([z_var] + state_vars,
nn.layers.get_output([lstm['output']] + state_layers, deterministic=True))
from images2gif import writeGif
from PIL import Image
# sample approximately 30 different generated video sequences
te_stream = data.streamer(training=True, shuffled=False)
interval = data.ntrain / data.batch_size / 30
for idx,imb in enumerate(te_stream.get_epoch_iterator()):
if idx % interval != 0:
continue
z_tup = transform_data(imb[0], center=True)
seg_idx = np.random.randint(z_tup[0].shape[0])
if kl_loss:
z_in, z_ls_in = z_tup[0], z_tup[1]
z_last, z_ls_last = z_in[seg_idx:seg_idx+1], z_ls_in[seg_idx:seg_idx+1]
z_vars = [z_last, z_ls_last]
else:
z_in = z_tup[0]
z_last = z_in[seg_idx:seg_idx+1]
z_vars = [z_last]
images = []
state_values = [np.dot(np.ones((z_last.shape[0],1), dtype=theano.config.floatX), s)
for s in init_values]
output_list = sample_fn(*(z_vars + state_values))
# use whole sequence of predictions for output
z_pred = output_list[0]
state_values = output_list[2 if kl_loss else 1:]
rec = img_from_z(z_pred.reshape(-1, z_dim))
for k in xrange(rec.shape[0]):
images.append(Image.fromarray(u.get_picture_array(rec, index=k, shift=pixel_shift)))
k += 1
# slice prediction to feed into lstm
z_pred = z_pred[:,-1:,:]
if kl_loss:
z_ls_pred = output_list[1][:,-1:,:]
z_vars = [z_pred, z_ls_pred]
else:
z_vars = [z_pred]
for i in xrange(30): # predict 30 frames after the end of the priming video
output_list = sample_fn(*(z_vars + state_values))
z_pred = output_list[0]
state_values = output_list[2 if kl_loss else 1:]
rec = img_from_z(z_pred.reshape(-1, z_dim))
images.append(Image.fromarray(u.get_picture_array(rec, index=0, shift=pixel_shift)))
if kl_loss:
z_ls_pred = output_list[1]
z_vars = [z_pred, z_ls_pred]
else:
z_vars = [z_pred]
writeGif("sample_{}.gif".format(idx),images,duration=0.1,dither=0)
def main(n_hid=256,
lstm_layers=2,
num_epochs=100,
batch_size=32,
save_to='output',
max_per_epoch=-1):
# load current set of words used
words = open(c.words_used_file, 'r').readlines()
idx_to_words = dict((i + 1, w.strip()) for i, w in enumerate(words))
idx_to_words[0] = '<e>'
word_dim = len(words) + 1
# normalization expected by vgg-net
mean_values = np.array([104, 117, 123]).reshape(
(3, 1, 1)).astype(theano.config.floatX)
# build function for extraction convolutional features
img_var = T.tensor4('images')
net = m.build_vgg(shape=(c.img_size, c.img_size), input_var=img_var)
values = pickle.load(open(c.vgg_weights))['param values']
nn.layers.set_all_param_values(net['pool5'], values)
conv_feats = theano.function([img_var], nn.layers.get_output(net['pool5']))
conv_shape = nn.layers.get_output_shape(net['pool5'])
# helper function for converting word vector to one-hot
raw_word_var = T.matrix('seq_raw')
one_hot = theano.function([raw_word_var],
nn.utils.one_hot(raw_word_var, m=word_dim))
# build expressions for lstm
conv_feats_var = T.tensor4('conv')
seq_var = T.tensor3('seq')
lstm = m.build_rnn(conv_feats_var, seq_var, conv_shape, word_dim, n_hid,
lstm_layers)
output = nn.layers.get_output(lstm['output'])
output_det = nn.layers.get_output(lstm['output'], deterministic=True)
loss = m.categorical_crossentropy_logdomain(output, seq_var).mean()
te_loss = m.categorical_crossentropy_logdomain(output_det, seq_var).mean()
# compile training functions
params = nn.layers.get_all_params(lstm['output'], trainable=True)
lr = theano.shared(nn.utils.floatX(1e-3))
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([conv_feats_var, seq_var],
loss,
updates=updates)
test_fn = theano.function([conv_feats_var, seq_var], te_loss)
predict_fn = theano.function([conv_feats_var, seq_var],
T.exp(output_det[:, -1:]))
zeros = np.zeros((batch_size, 1, word_dim), dtype=theano.config.floatX)
def transform_data(imb):
y, x = imb
# data augmentation: flip = -1 if we do flip over y-axis, 1 if not
flip = -2 * np.random.binomial(1, p=0.5) + 1
# this vgg-net expects image values that are normalized by mean but not magnitude
x = (u.raw_to_floatX(x[:,:,::flip], pixel_shift=0.)\
.transpose(0,1,3,2)[:,::-1] * 255. - mean_values)
return conv_feats(x), np.concatenate([zeros, one_hot(y)], axis=1)
data = u.DataH5PyStreamer(c.twimg_hdf5_file, batch_size=batch_size)
hist = u.train_with_hdf5(data,
num_epochs=num_epochs,
train_fn=train_fn,
test_fn=test_fn,
max_per_epoch=max_per_epoch,
tr_transform=transform_data,
te_transform=transform_data)
np.savetxt('lstm_train_hist.csv',
np.asarray(hist),
delimiter=',',
fmt='%.5f')
u.save_params(
lstm['output'],
os.path.join(save_to, 'lstm_{}.npz'.format(np.asarray(hist)[-1, -1])))
# generate some example captions for one batch of images
streamer = data.streamer(training=False, shuffled=True)
y_raw, x_raw = next(streamer.get_epoch_iterator())
x, _ = transform_data((y_raw, x_raw))
y = zeros
captions = []
for idx in xrange(y.shape[0]):
captions.append([])
idx_to_words[0] = '<e>'
for sample_num in xrange(c.max_caption_len):
pred = predict_fn(x, y)
new_y = []
for idx in xrange(pred.shape[0]):
# reduce size by a small factor to prevent numerical imprecision from
# making it sum to > 1.
# reverse it so that <e> gets the additional probability, not a word
sample = np.random.multinomial(1,
pred[idx, 0, ::-1] * .999999)[::-1]
captions[idx].append(idx_to_words[np.argmax(sample)])
new_y.append(sample)
new_y = np.vstack(new_y).reshape(-1, 1,
word_dim).astype(theano.config.floatX)
y = np.concatenate([y, new_y], axis=1)
captions = [
'{},{}\n'.format(i, ' '.join(cap)) for i, cap in enumerate(captions)
]
with open(os.path.join(save_to, 'captions_sample.csv'), 'w') as wr:
wr.writelines(captions)
for idx in xrange(x_raw.shape[0]):
Image.fromarray(x_raw[idx].transpose(2, 1, 0)).save(
os.path.join(save_to, 'ex_{}.jpg'.format(idx)))
def main(
data_file="",
img_size=64,
num_epochs=10,
batch_size=128,
pxsh=0.5,
split_layer="conv7",
specstr=c.pf_cae_specstr,
cae_params=c.pf_cae_params,
save_to="params",
):
# transform function to go from images -> conv feats
conv_feats, _ = m.encoder_decoder(cae_params, specstr=specstr, layersplit=split_layer, shape=(img_size, img_size))
# build pretrained net for images -> convfeats in order to get the input shape
# for the reverse function
print("compiling functions")
conv_net = m.build_cae(input_var=None, specstr=specstr, shape=(img_size, img_size))
cae_layer_dict = dict((l.name, l) for l in nn.layers.get_all_layers(conv_net))
shape = nn.layers.get_output_shape(cae_layer_dict[split_layer])
# build net for convfeats -> images
imgs_var = T.tensor4("images")
convs_var = T.tensor4("conv_features")
deconv_net = m.build_deconv_net(input_var=convs_var, shape=shape, specstr=specstr)
loss = nn.objectives.squared_error(imgs_var, nn.layers.get_output(deconv_net)).mean()
te_loss = nn.objectives.squared_error(imgs_var, nn.layers.get_output(deconv_net, deterministic=True)).mean()
params = nn.layers.get_all_params(deconv_net, trainable=True)
lr = theano.shared(nn.utils.floatX(3e-3))
updates = nn.updates.adam(loss, params, learning_rate=lr)
# compile functions
train_fn = theano.function([convs_var, imgs_var], loss, updates=updates)
val_fn = theano.function([convs_var, imgs_var], te_loss)
deconv_fn = theano.function([convs_var], nn.layers.get_output(deconv_net, deterministic=True))
# run training loop
print("training for {} epochs".format(num_epochs))
def data_transform(x, do_center):
floatx_ims = u.raw_to_floatX(x, pixel_shift=pxsh, square=True, center=do_center)
return (conv_feats(floatx_ims), floatx_ims)
data = u.DataH5PyStreamer(data_file, batch_size=batch_size)
hist = u.train_with_hdf5(
data,
num_epochs=num_epochs,
train_fn=train_fn,
test_fn=val_fn,
tr_transform=lambda x: data_transform(x[0], do_center=False),
te_transform=lambda x: data_transform(x[0], do_center=True),
)
# generate examples, save training history and params
te_stream = data.streamer(shuffled=True)
imb, = next(te_stream.get_epoch_iterator())
imb = data_transform(imb, True)[0]
result = deconv_fn(imb)
for i in range(result.shape[0]):
Image.fromarray(u.get_picture_array(result, index=i, shift=pxsh)).save("output_{}.jpg".format(i))
hist = np.asarray(hist)
np.savetxt("deconv_train_hist.csv", np.asarray(hist), delimiter=",", fmt="%.5f")
u.save_params(deconv_net, os.path.join(save_to, "deconv_{}.npz".format(hist[-1, -1])))