def test_infer_shape(self):
admat = matrix()
bdmat = matrix()
admat_val = numpy.random.rand(3, 4).astype(config.floatX)
bdmat_val = numpy.random.rand(3, 4).astype(config.floatX)
self._compile_and_check([admat, bdmat], [SoftmaxGrad()(admat, bdmat)],
[admat_val, bdmat_val], SoftmaxGrad)
def test_min_informative_str():
""" evaluates a reference output to make sure the
min_informative_str function works as intended """
A = tensor.matrix(name='A')
B = tensor.matrix(name='B')
C = A + B
C.name = 'C'
D = tensor.matrix(name='D')
E = tensor.matrix(name='E')
F = D + E
G = C + F
mis = min_informative_str(G).replace("\t", " ")
reference = """A. Elemwise{add,no_inplace}
B. C
C. Elemwise{add,no_inplace}
D. D
E. E"""
if mis != reference:
print('--' + mis + '--')
print('--' + reference + '--')
assert mis == reference
def test_linear_regression():
inpt = T.matrix('inpt')
inpt.tag.test_value = np.zeros((3, 10))
inpt.tag.test_value
target = T.matrix('target')
target.tag.test_value = np.zeros((3, 2))
l = AffineNonlinear(inpt, 10, 2, 'tanh')
loss = squared(target, l.output).sum(1).mean()
m = SupervisedModel(inpt=inpt, target=target, output=l.output, loss=loss,
parameters=l.parameters)
f_predict = m.function([m.inpt], m.output)
f_loss = m.function([m.inpt, m.target], m.loss)
X = np.zeros((20, 10))
Z = np.zeros((20, 2))
Y = f_predict(X)
assert Y.shape == (20, 2), 'ouput has wrong shape'
l = f_loss(X, Z)
assert np.array(l).ndim == 0, 'loss is not a scalar'
def test_sequence_variable_inputs():
x, y = tensor.matrix(), tensor.matrix()
parallel_1 = Parallel(input_names=['input_1', 'input_2'],
input_dims=dict(input_1=4, input_2=5),
output_dims=dict(input_1=3, input_2=2),
prototype=Linear(), weights_init=Constant(2),
biases_init=Constant(1))
parallel_2 = Parallel(input_names=['input_1', 'input_2'],
input_dims=dict(input_1=3, input_2=2),
output_dims=dict(input_1=5, input_2=4),
prototype=Linear(), weights_init=Constant(2),
biases_init=Constant(1))
sequence = Sequence([parallel_1.apply, parallel_2.apply])
sequence.initialize()
new_x, new_y = sequence.apply(x, y)
x_val = numpy.ones((4, 4), dtype=theano.config.floatX)
y_val = numpy.ones((4, 5), dtype=theano.config.floatX)
assert_allclose(
new_x.eval({x: x_val}),
(x_val.dot(2 * numpy.ones((4, 3))) + numpy.ones((4, 3))).dot(
2 * numpy.ones((3, 5))) + numpy.ones((4, 5)))
assert_allclose(
new_y.eval({y: y_val}),
(y_val.dot(2 * numpy.ones((5, 2))) + numpy.ones((4, 2))).dot(
2 * numpy.ones((2, 4))) + numpy.ones((4, 4)))
def Z_LSTM(input_var, z_dim=256, nhid=512, layers=2, gradclip=10, training=True):
ret = {}
state_vars = []
ret['input'] = layer = nn.layers.InputLayer(input_var=input_var, shape=(None, None, z_dim))
batchsize, seqlen, _ = layer.input_var.shape
for lay in xrange(layers):
ret['drop_{}'.format(lay)] = layer = nn.layers.DropoutLayer(layer, p=0.3)
if training:
ret['lstm_{}'.format(lay)] = layer = LSTMSampleableLayer(layer, nhid,
grad_clipping=gradclip, learn_init=True)
else:
cell_var = T.matrix('cell_var_{}'.format(lay))
hid_var = T.matrix('hid_var_{}'.format(lay))
state_vars.append(cell_var)
state_vars.append(hid_var)
ret['lstm_{}'.format(lay)] = layer = LSTMSampleableLayer(layer, nhid,
cell_init=cell_var, hid_init=hid_var)
ret['cell_{}'.format(lay)] = nn.layers.SliceLayer(layer, axis=2,
indices=slice(None,nhid))
ret['hid_{}'.format(lay)] = layer = nn.layers.SliceLayer(layer, axis=2,
indices=slice(nhid,None))
ret['reshape'] = layer = nn.layers.ReshapeLayer(layer, (-1, nhid))
ret['project'] = layer = nn.layers.DenseLayer(layer, num_units=z_dim, nonlinearity=None)
ret['output'] = layer = nn.layers.ReshapeLayer(layer, (batchsize, seqlen, z_dim))
# final state slice layers for passing to next instance of lstm
for lay in xrange(layers):
ret['cellfinal_{}'.format(lay)] = nn.layers.SliceLayer(ret['cell_{}'.format(lay)],
axis=1, indices=-1)
ret['hidfinal_{}'.format(lay)] = nn.layers.SliceLayer(ret['hid_{}'.format(lay)],
axis=1, indices=-1)
return ret, state_vars
def initialise_model(self, X_train, y_train):
print 'Initialising model...'
self.input_shape = X_train.shape[1]
input_var = T.matrix('inputs')
target_var = T.matrix('targets')
if self.normalise:
y_train = self.normalise_y(y_train, reset = True)
X_train = self.normalise_X(X_train, reset = True)
# Create neural network model
self.network = self.build_custom_mlp(input_var)
prediction = lasagne.layers.get_output(self.network)
loss = lasagne.objectives.squared_error(prediction, target_var)
loss = loss.mean()
params = lasagne.layers.get_all_params(self.network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss, params,
learning_rate=self.learning_rate,
momentum=self.momentum)
test_prediction = lasagne.layers.get_output(self.network,
deterministic=True)
test_loss = lasagne.objectives.squared_error(test_prediction,
target_var)
test_loss = test_loss.mean()
self.train_fn = theano.function([input_var, target_var], loss,
updates=updates, allow_input_downcast=True)
self.predict_output = theano.function([input_var],
outputs=test_prediction,
allow_input_downcast=True)
self.initialised = True
def __init__(self, embedding_dim=100, num_hidden_layers=2, hidden_dim=200, in_dropout_p=0.2, hidden_dropout_p=0.5, update_hyperparams={'learning_rate': 0.01}):
self.embedding_dim = embedding_dim
self.num_hidden_layers = num_hidden_layers
self.hidden_dim = hidden_dim
self.in_dropout_p = in_dropout_p
self.hidden_dropout_p = update_hyperparams
print >> sys.stderr, 'Building computation graph for discriminator...'
self.input_var = T.matrix('input')
self.target_var = T.matrix('targer')
self.l_in = lasagne.layers.InputLayer(shape=(None, self.embedding_dim), input_var=T.tanh(self.input_var), name='l_in')
self.l_in_dr = lasagne.layers.DropoutLayer(self.l_in, 0.2)
self.layers = [self.l_in, self.l_in_dr]
for i in xrange(self.num_hidden_layers):
l_hid = lasagne.layers.batch_norm(lasagne.layers.DenseLayer(self.layers[-1], num_units=self.hidden_dim, nonlinearity=lasagne.nonlinearities.leaky_rectify, W=lasagne.init.GlorotUniform(gain=leaky_relu_gain), name=('l_hid_%s' % i)))
l_hid_dr = lasagne.layers.DropoutLayer(l_hid, 0.5)
self.layers.append(l_hid)
self.layers.append(l_hid_dr)
self.l_preout = lasagne.layers.batch_norm(lasagne.layers.DenseLayer(self.layers[-1], num_units=1, nonlinearity=None, name='l_preout'))
self.l_out = lasagne.layers.NonlinearityLayer(self.l_preout, nonlinearity=lasagne.nonlinearities.sigmoid, name='l_out')
self.prediction = lasagne.layers.get_output(self.l_out)
self.loss = lasagne.objectives.binary_crossentropy(self.prediction, self.target_var).mean()
self.accuracy = T.eq(T.ge(self.prediction, 0.5), self.target_var).mean()
self.params = lasagne.layers.get_all_params(self.l_out, trainable=True)
self.updates = lasagne.updates.adam(self.loss, self.params, **update_hyperparams)
print >> sys.stderr, 'Compiling discriminator...'
self.train_fn = theano.function([self.input_var, self.target_var], [self.loss, self.accuracy], updates=self.updates)
self.eval_fn = theano.function([self.input_var, self.target_var], [self.loss, self.accuracy])
def __init__(self, input_layer, loss_function=mse, aggregation='mean'):
"""
Constructor
:parameters:
- input_layer : a `Layer` whose output is the networks prediction
given its input
- loss_function : a loss function of the form `f(x, t, m)` that
returns a scalar loss given tensors that represent the
predicted values, true values and mask as arguments.
- aggregation : either:
- `None` or `'mean'` : the elements of the loss will be
multiplied by the mask and the mean returned
- `'sum'` : the elements of the loss will be multiplied by
the mask and the sum returned
- `'normalized_sum'` : the elements of the loss will be
multiplied by the mask, summed and divided by the sum of
the mask
"""
self.input_layer = input_layer
self.loss_function = loss_function
self.target_var = T.matrix("target")
self.mask_var = T.matrix("mask")
if aggregation not in self._valid_aggregation:
raise ValueError('aggregation must be \'mean\', \'sum\', '
'\'normalized_sum\' or None,'
' not {0}'.format(aggregation))
self.aggregation = aggregation
def init_variables(self):
self.input_var = T.matrix('inputs')
self.side_var = T.matrix('contexts')
# do regression
#self.target_var = T.ivector('targets')
self.target_var = T.vector('targets')
self.num_classes = 1 # regression -> dim matters, not classes
def __init__(self, dnodex,dim):
X = T.matrix()
Y = T.matrix()
eta = T.scalar()
temperature=T.scalar()
num_input = len(format(dnodex.npoi,'b'))
num_hidden = dim
num_output = len(format(dnodex.npoi,'b'))
inputs = InputLayer(X, name="inputs")
lstm1 = LSTMLayer(num_input, num_hidden, input_layer=inputs, name="lstm1")
lstm2 = LSTMLayer(num_hidden, num_hidden, input_layer=lstm1, name="lstm2")
#lstm3 = LSTMLayer(num_hidden, num_hidden, input_layer=lstm2, name="lstm3")
softmax = SoftmaxLayer(num_hidden, num_output, input_layer=lstm2, name="yhat", temperature=temperature)
Y_hat = softmax.output()
self.layers = inputs, lstm1, lstm2, softmax
params = get_params(self.layers)
caches = make_caches(params)
cost = T.mean(T.nnet.categorical_crossentropy(Y_hat, Y))
updates = momentum(cost, params, caches, eta)
self.train = theano.function([X, Y, eta, temperature], cost, updates=updates, allow_input_downcast=True)
predict_updates = one_step_updates(self.layers)
self.predict_char = theano.function([X, temperature], Y_hat, updates=predict_updates, allow_input_downcast=True)
def fine_train(nn,datasets,learning_Rate,batch_sizes,epochs):
train_set_x, train_set_y = datasets[0]
n_batches = train_set_x.get_value(borrow=True).shape[0] / batch_sizes
train_label = T.cast(train_label,'float64')
index = T.lscalar()
x = T.matrix('x')
y = T.matrix('y')
min_batch_cost = []
if nn is None:
mynn = ForwordNN(x,y,n_in,n_out,hidden_sizes)
else:
mynn=nn
cost,update = mynn.get_cost_update(x,y,learning_Rate)
train_nn = theano.function([index],
cost,
updates = update,
givens = {
x:train_data[index*batch_sizes:(index+1)*batch_sizes,:],
y:train_label[index*batch_sizes:(index+1)*batch_sizes,:]
}
)
for num_epochs in range(epochs):
t1=time.time()
for num_batch in xrange(n_train_batchs):
min_batch_cost.append(train_nn(num_batch))
t2=time.time()
print 'The %d/%dth training,takes %f seconds,cost is %f' %(num_epochs+1,epochs,(t2-t1),np.mean(min_batch_cost))
return mynn
def __init__(self, model, cost, monitoring_dataset, batch_size):
"""
Parameters
----------
model : pylearn2.models.model.Model
the model whose best parameters we want to keep track of
cost : tensor_like
cost function used to evaluate the model's performance
monitoring_dataset : pylearn2.datasets.dataset.Dataset
dataset on which to compute the cost
batch_size : int
size of the batches used to compute the cost
"""
self.model = model
self.cost = cost
self.dataset = monitoring_dataset
self.batch_size = batch_size
self.minibatch = T.matrix('minibatch')
self.target = T.matrix('target')
if cost.supervised:
self.supervised = True
self.cost_function = theano.function(inputs=[self.minibatch,
self.target],
outputs=cost(model,
self.minibatch,
self.target))
else:
self.supervised = False
self.cost_function = theano.function(inputs=[self.minibatch],
outputs=cost(model,
self.minibatch))
self.best_cost = numpy.inf
self.best_params = model.get_param_values()
def set_generation_function(recurrent_model, output_model):
# set input data (1*num_samples*features)
input_data = tensor.matrix(name='input_seq', dtype=floatX)
# set init hidden/cell(num_samples*hidden_size)
prev_hidden_data = tensor.matrix(name='prev_hidden_data', dtype=floatX)
prev_cell_data = tensor.matrix(name='prev_cell_data', dtype=floatX)
# get hidden data
recurrent_data = get_tensor_output(input=[input_data, prev_hidden_data, prev_cell_data], layers=recurrent_model, is_training=False)
cur_hidden_data = recurrent_data[0]
cur_cell_data = recurrent_data[1]
# get prediction data
output_data = get_tensor_output(input=cur_hidden_data, layers=output_model, is_training=False)
# input data
generation_function_inputs = [input_data,
prev_hidden_data,
prev_cell_data]
generation_function_outputs = [cur_hidden_data,
cur_cell_data,
output_data]
generation_function = theano.function(inputs=generation_function_inputs,
outputs=generation_function_outputs,
on_unused_input='ignore')
return generation_function
def test_compute_flag(self):
orig_compute_test_value = theano.config.compute_test_value
try:
x = T.matrix('x')
y = T.matrix('y')
y.tag.test_value = numpy.random.rand(4,5).astype(config.floatX)
# should skip computation of test value
theano.config.compute_test_value = 'off'
z = T.dot(x,y)
assert not hasattr(z.tag, 'test_value')
# should fail when asked by user
theano.config.compute_test_value = 'raise'
self.assertRaises(ValueError, T.dot, x, y)
# test that a warning is raised if required
theano.config.compute_test_value = 'warn'
warnings.simplefilter('error', UserWarning)
try:
self.assertRaises(UserWarning, T.dot, x, y)
finally:
# Restore the default behavior.
# TODO There is a cleaner way to do this in Python 2.6, once
# Theano drops support of Python 2.4 and 2.5.
warnings.simplefilter('default', UserWarning)
finally:
theano.config.compute_test_value = orig_compute_test_value
def test_string_var(self):
orig_compute_test_value = theano.config.compute_test_value
try:
theano.config.compute_test_value = 'raise'
x = T.matrix('x')
x.tag.test_value = numpy.random.rand(3,4).astype(config.floatX)
y = T.matrix('y')
y.tag.test_value = numpy.random.rand(4,5).astype(config.floatX)
z = theano.shared(numpy.random.rand(5,6).astype(config.floatX))
# should work
out = T.dot(T.dot(x,y), z)
assert hasattr(out.tag, 'test_value')
tf = theano.function([x,y], out)
assert _allclose(
tf(x.tag.test_value, y.tag.test_value),
out.tag.test_value)
def f(x,y,z):
return T.dot(T.dot(x,y),z)
# this test should fail
z.set_value(numpy.random.rand(7,6).astype(config.floatX))
self.assertRaises(ValueError, f, x, y, z)
finally:
theano.config.compute_test_value = orig_compute_test_value
def _construct_compute_fe_terms(self):
"""
Construct a function for computing terms in variational free energy.
"""
# setup some symbolic variables for theano to deal with
xi = T.matrix()
xo = T.matrix()
_, hi_zmuv = self._construct_zmuv_samples(xi, 1)
# construct values to output
nll = self.nlli[-1]
kld = self.kld_z.flatten() + self.kld_hi_q2p.flatten()
# compile theano function for a one-sample free-energy estimate
fe_term_sample = theano.function(inputs=[ xi, xo ], \
outputs=[nll, kld], \
givens={self.x_in: xi, \
self.x_out: xo, \
self.hi_zmuv: hi_zmuv}, \
updates=self.scan_updates)
# construct a wrapper function for multi-sample free-energy estimate
def fe_term_estimator(XI, XO, sample_count):
# compute a multi-sample estimate of variational free-energy
nll_sum = np.zeros((XI.shape[0],))
kld_sum = np.zeros((XI.shape[0],))
for i in range(sample_count):
result = fe_term_sample(XI, XO)
nll_sum += result[0].ravel()
kld_sum += result[1].ravel()
mean_nll = nll_sum / float(sample_count)
mean_kld = kld_sum / float(sample_count)
return [mean_nll, mean_kld]
return fe_term_estimator
def _construct_sample_from_prior(self):
"""
Construct a function for drawing independent samples from the
distribution generated by this MultiStageModel. This function returns
the full sequence of "partially completed" examples.
"""
z_sym = T.matrix()
x_sym = T.matrix()
irs = self.ir_steps
oputs = [self.obs_transform(self.s0)]
oputs.extend([self.obs_transform(self.si[i]) for i in range(irs)])
_, hi_zmuv = self._construct_zmuv_samples(x_sym, 1)
sample_func = theano.function(inputs=[z_sym, x_sym], outputs=oputs, \
givens={ self.z: z_sym, \
self.x_in: T.zeros_like(x_sym), \
self.x_out: T.zeros_like(x_sym), \
self.hi_zmuv: hi_zmuv }, \
updates=self.scan_updates)
def prior_sampler(samp_count):
x_samps = to_fX( np.zeros((samp_count, self.obs_dim)) )
old_switch = self.train_switch.get_value(borrow=False)
# set model to generation mode
self.set_train_switch(switch_val=0.0)
z_samps = to_fX( npr.randn(samp_count, self.z_dim) )
model_samps = sample_func(z_samps, x_samps)
# set model back to either training or generation mode
self.set_train_switch(switch_val=old_switch)
return model_samps
return prior_sampler
def test_wrong_dims(self):
a = tt.matrix()
increment = tt.matrix()
index = 0
self.assertRaises(TypeError, tt.set_subtensor, a[index], increment)
self.assertRaises(TypeError, tt.inc_subtensor, a[index], increment)
def __init__(self,rng=None,theano_rng=None,n_in=121,hidden_layers_sizes=[400,400,400],n_hidden=6,n_out=1):
self.dA_layers = []
self.sigmoid_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(rng.randint(2**30))
self.x = T.matrix('x')
self.y = T.matrix('y')
for i in xrange(self.n_layers):
if i == 0:
input_size = n_in
layer_input = self.x
else:
input_size = hidden_layers_sizes[i-1]
layer_input = self.sigmoid_layers[-1].output
sigmoid_layer = regressionLayer(rng=rng,input=layer_input,n_in=input_size,n_out=hidden_layers_sizes[i],activation=T.tanh)
self.sigmoid_layers.append(sigmoid_layer)
self.params.extend(sigmoid_layer.params)
dA_layer = daLayer(rng=rng,theano_rng=theano_rng,input=layer_input,n_in=input_size,n_hidden=hidden_layers_sizes[i],W=sigmoid_layer.W,bhid=sigmoid_layer.b,activation=T.tanh)
self.dA_layers.append(dA_layer)
self.reg_layer1 = regressionLayer(rng=rng,input=self.sigmoid_layers[-1].output,n_in=hidden_layers_sizes[-1],n_out=n_hidden)
self.reg_layer2 = regressionLayer(rng=rng,input=self.reg_layer1.output,n_in=n_hidden,n_out=n_out)
self.params.extend(self.reg_layer1.params)
self.params.extend(self.reg_layer2.params)
self.output = self.reg_layer2.output
self.errors = T.mean((self.output-self.y)**2)
def build_model(tparams, options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
x = tensor.matrix('x', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
y = tensor.vector('y', dtype='int64')
n_timesteps = x.shape[0]
n_samples = x.shape[1]
emb = tparams['Wemb'][x.flatten()].reshape([n_timesteps,
n_samples,
options['dim_proj']])
proj = get_layer(options['encoder'])[1](tparams, emb, options,
prefix=options['encoder'],
mask=mask)
if options['encoder'] == 'lstm':
proj = (proj * mask[:, :, None]).sum(axis=0)
proj = proj / mask.sum(axis=0)[:, None]
if options['use_dropout']:
proj = dropout_layer(proj, use_noise, trng)
pred = tensor.nnet.softmax(tensor.dot(proj, tparams['U']) + tparams['b'])
f_pred_prob = theano.function([x, mask], pred, name='f_pred_prob')
f_pred = theano.function([x, mask], pred.argmax(axis=1), name='f_pred')
cost = -tensor.log(pred[tensor.arange(n_samples), y] + 1e-8).mean()
return use_noise, x, mask, y, f_pred_prob, f_pred, cost
def rebuild_nn(nn_params):
W_e, W_p, W_o, b_o = read_obj(nn_params, 4)
mlp = MLPNoHid(W_e.get_value(), W_p.get_value(), W_o.get_value(), b_o.get_value())
wx = T.matrix('word', dtype='int32')
px = T.matrix('POS', dtype='int32')
f_pred = theano.function([wx, px], mlp.output(wx, px))
return f_pred
def test_pdf_compare_logpdf():
theano.config.compute_test_value = 'raise'
sample = T.matrix()
sample.tag.test_value = np.random.random((10, 5)).astype(theano.config.floatX)
mean = T.vector()
mean.tag.test_value = np.empty(5).astype(theano.config.floatX)
cov = T.matrix()
cov.tag.test_value = np.random.random((5, 5)).astype(theano.config.floatX)
density = mvn.pdf(sample, mean, cov)
log_density = mvn.logpdf(sample, mean, cov)
f_density = theano.function([sample, mean, cov], density)
f_logdensity = theano.function([sample, mean, cov], log_density)
some_sample = np.random.random((20, 5)).astype(theano.config.floatX)
some_mean = np.array([1., 2., 3., 4., 5.]).astype(theano.config.floatX)
w = np.random.random((5, 5)).astype(theano.config.floatX)
some_cov = np.dot(w, w.T) + np.eye(5).astype(theano.config.floatX)
d = f_density(some_sample, some_mean, some_cov)
log_d = f_logdensity(some_sample, some_mean, some_cov)
assert np.allclose(np.log(d), log_d)
def build_model(self):
######################
# BUILD ACTUAL MODEL #
######################
logger.info('... building the model')
U, W, V, bh, by = self.U, self.W, self.V, self.bh, self.by
x = T.matrix('x')
y = T.matrix('y')
def forward_prop_step(x_t, s_tm1, U, W, bh):
s_t = self.activation(T.dot(U, x_t) + T.dot(W, s_tm1) + bh)
return s_t
s, _ = theano.scan(
forward_prop_step,
sequences=x,
outputs_info=[dict(initial=T.zeros(self.hidden_dim))],
non_sequences=[U, W, bh],
mode='DebugMode')
p_y = T.nnet.softmax(T.dot(self.V, s[-1]) + by)
prediction = T.argmax(p_y, axis=1)
o_error = T.sum(T.nnet.categorical_crossentropy(p_y, y))
self.cost = o_error + self.L1_reg * self.L1 + self.L2_reg * self.L2_sqr
# Assign functions
self.forward_propagation = theano.function([x], s[-1])
self.predict = theano.function([x], prediction)
self.ce_error = theano.function([x, y], o_error)
l_r = T.scalar('l_r', dtype=theano.config.floatX) # learning rate (may change)
mom = T.scalar('mom', dtype=theano.config.floatX) # momentum
self.bptt, self.f_update = self.Momentum(x, y, l_r, mom)
def est_both_assert_merge_2_reverse(self):
# Test case "test_both_assert_merge_2" but in reverse order
x1 = T.matrix('x1')
x2 = T.matrix('x2')
x3 = T.matrix('x3')
e = T.dot(x1, T.opt.assert_op(x2, (x2 > x3).all())) +\
T.dot(T.opt.assert_op(x1, (x1 > x3).all()), x2)
g = FunctionGraph([x1, x2, x3], [e])
MergeOptimizer().optimize(g)
strg = theano.printing.debugprint(g, file='str')
strref = '''Elemwise{add,no_inplace} [@A] '' 7
|dot [@B] '' 6
| |Assert{msg='Theano Assert failed!'} [@C] '' 5
| | |x1 [@D]
| | |All [@E] '' 3
| | |Elemwise{gt,no_inplace} [@F] '' 1
| | |x1 [@D]
| | |x3 [@G]
| |Assert{msg='Theano Assert failed!'} [@H] '' 4
| |x2 [@I]
| |All [@J] '' 2
| |Elemwise{gt,no_inplace} [@K] '' 0
| |x2 [@I]
| |x3 [@G]
|dot [@B] '' 6
'''
print(strg)
assert strg == strref, (strg, strref)
def get_model(Ws, bs, dropout=False):
v = T.matrix('input')
m = T.matrix('missing')
q = T.matrix('target')
k = T.vector('normalization factor')
# Set all missing/target values to 0.5
keep_mask = (1-m) * (1-q)
h = keep_mask * (v * 2 - 1) # Convert to +1, -1
# Normalize layer 0
h *= k.dimshuffle(0, 'x')
for l in xrange(len(Ws)):
h = T.dot(h, Ws[l]) + bs[l]
if l < len(Ws) - 1:
h = h * (h > 0) # relu
if dropout:
mask = srng.binomial(n=1, p=0.5, size=h.shape)
h = h * mask * 2
output = sigmoid(h)
LL = v * T.log(output) + (1 - v) * T.log(1 - output)
# loss = -(q * LL).sum() / q.sum()
loss = -((1 - m) * LL).sum() / (1 - m).sum()
return v, m, q, k, output, loss
def test_hgemm_swap():
from theano.sandbox.cuda import nvcc_compiler
if nvcc_compiler.nvcc_version < '7.5':
raise SkipTest("SgemmEx is only avaialble on cuda 7.5+")
v = tensor.vector(dtype='float16')
m = tensor.matrix(dtype='float16')
m2 = tensor.matrix(dtype='float16')
m32 = tensor.matrix(dtype='float32')
# test that we don't try to replace anything but matrix x matrix in float16
f = theano.function([v, m], tensor.dot(v, m), mode=mode_with_gpu)
assert len([node for node in f.maker.fgraph.apply_nodes
if isinstance(node.op, GpuGemm)]) == 0
f = theano.function([m32, m], tensor.dot(m32, m), mode=mode_with_gpu)
assert len([node for node in f.maker.fgraph.apply_nodes
if isinstance(node.op, GpuGemm)]) == 0
f = theano.function([m, m2], tensor.dot(m, m2), mode=mode_with_gpu)
assert len([node for node in f.maker.fgraph.apply_nodes
if isinstance(node.op, GpuGemm)]) == 1
v1 = numpy.random.random((3, 4)).astype('float16')
v2 = numpy.random.random((4, 2)).astype('float16')
of = f(v1, v2)
on = numpy.dot(v1, v2)
utt.assert_allclose(of, on)
def _setup_vars(self, sparse_input):
'''Setup Theano variables for our network.
Parameters
----------
sparse_input : bool
Not used -- sparse inputs are not supported for recurrent networks.
Returns
-------
vars : list of theano variables
A list of the variables that this network requires as inputs.
'''
_warn_dimshuffle()
assert not sparse_input, 'Theanets does not support sparse recurrent models!'
self.src = TT.ftensor3('src')
#self.src_mask = TT.imatrix('src_mask')
self.src_mask = TT.matrix('src_mask')
self.dst = TT.ftensor3('dst')
self.labels = TT.imatrix('labels')
self.weights = TT.matrix('weights')
if self.weighted:
return [self.src, self.src_mask, self.dst, self.labels, self.weights]
return [self.src, self.dst]
def funcs(dataset, network, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, momentum=MOMENTUM, alpha=L2_CONSTANT):
"""
Method the returns the theano functions that are used in
training and testing. These are the train and predict functions.
The predict function returns out output of the network.
"""
# symbolic variables
X_batch = T.matrix()
y_batch = T.matrix()
# this is the cost of the network when fed throught the noisey network
l2 = lasagne.regularization.l2(X_batch)
train_output = lasagne.layers.get_output(network, X_batch)
cost = lasagne.objectives.mse(train_output, y_batch)
cost = cost.mean() #+ alpha*l2
# test the performance of the netowork without noise
test = lasagne.layers.get_output(network, X_batch, deterministic=True)
pred = T.argmax(test, axis=1)
accuracy = T.mean(T.eq(pred, y_batch), dtype=theano.config.floatX)
all_params = lasagne.layers.get_all_params(network)
updates = lasagne.updates.nesterov_momentum(cost, all_params, learning_rate, momentum)
train = theano.function(inputs=[X_batch, y_batch], outputs=cost, updates=updates, allow_input_downcast=True)
valid = theano.function(inputs=[X_batch, y_batch], outputs=cost, allow_input_downcast=True)
predict = theano.function(inputs=[X_batch], outputs=pred, allow_input_downcast=True)
return dict(
train=train,
valid=valid,
predict=predict
)
def __init__(self, n_in, n_out, n_h, learning_rate=0.12):
self.x = T.matrix(dtype=theano.config.floatX) # @UndefinedVariable
self.target = T.matrix(dtype=theano.config.floatX) # @UndefinedVariable
bound_x = numpy.sqrt(6. / (n_in + n_h))
bound_h = numpy.sqrt(6. / (n_h + n_h))
self.params = []
self.w_x = theano.shared(np.array(np.random.uniform(low=-bound_x, high=bound_x, size=(n_in, n_h)), dtype=theano.config.floatX)) # @UndefinedVariable
self.params.append(self.w_x)
self.w_h = theano.shared(np.array(np.random.uniform(low=-bound_h, high=bound_h, size=(n_h, n_h)), dtype=theano.config.floatX)) # @UndefinedVariable
self.params.append(self.w_h)
self.b_h = theano.shared(np.array(np.random.uniform(low=-bound_h, high=bound_h, size=(n_h,)), dtype=theano.config.floatX)) # @UndefinedVariable
self.params.append(self.b_h)
self.w = theano.shared(np.array(np.random.uniform(low=-bound_h, high=bound_h, size=(n_h, n_out)), dtype=theano.config.floatX)) # @UndefinedVariable
self.params.append(self.w)
self.b = theano.shared(np.array(np.random.uniform(low=-bound_h, high=bound_h, size=(n_out,)), dtype=theano.config.floatX)) # @UndefinedVariable
self.params.append(self.b)
self.h0 = theano.shared(np.array(np.random.uniform(low=-bound_x, high=bound_x, size=(n_h,)), dtype=theano.config.floatX)) # @UndefinedVariable
self.params.append(self.h0)
def one_step(x, h1):
h = T.nnet.sigmoid(T.dot(x, self.w_x) + T.dot(h1, self.w_h) + self.b_h)
y = T.nnet.sigmoid(T.dot(h, self.w) + self.b)
return h, y
[hs, ys], _ = theano.scan(fn=one_step, sequences=self.x, outputs_info=[self.h0, None])
cost = -T.mean(self.target * T.log(ys) + (1 - self.target) * T.log(1 - ys))
grads = T.grad(cost, self.params)
updates = [(param, param - learning_rate * grad) for param, grad in zip(self.params, grads)]
self.train = theano.function([self.x, self.target], cost, updates=updates)
self.predict = theano.function([self.x], ys)
def test_one_step(self):
h0 = tensor.matrix('h0')
c0 = tensor.matrix('c0')
x = tensor.matrix('x')
h1, c1 = self.lstm.apply(x, h0, c0, iterate=False)
next_h = theano.function(inputs=[x, h0, c0], outputs=[h1])
h0_val = 0.1 * numpy.array([[1, 1, 0], [0, 1, 1]],
dtype=theano.config.floatX)
c0_val = 0.1 * numpy.array([[1, 1, 0], [0, 1, 1]],
dtype=theano.config.floatX)
x_val = 0.1 * numpy.array([range(12), range(12, 24)],
dtype=theano.config.floatX)
W_state_val = 2 * numpy.ones((3, 12), dtype=theano.config.floatX)
W_cell_to_in = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_out = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_forget = 2 * numpy.ones((3,), dtype=theano.config.floatX)
# omitting biases because they are zero
activation = numpy.dot(h0_val, W_state_val) + x_val
def sigmoid(x):
return 1. / (1. + numpy.exp(-x))
i_t = sigmoid(activation[:, :3] + c0_val * W_cell_to_in)
f_t = sigmoid(activation[:, 3:6] + c0_val * W_cell_to_forget)
next_cells = f_t * c0_val + i_t * numpy.tanh(activation[:, 6:9])
o_t = sigmoid(activation[:, 9:12] +
next_cells * W_cell_to_out)
h1_val = o_t * numpy.tanh(next_cells)
assert_allclose(h1_val, next_h(x_val, h0_val, c0_val)[0],
rtol=1e-6)
reg_cost = lib.ops.kl_unit_gaussian.kl_unit_gaussian(
mu,
log_sigma
).sum(axis=1)
alpha = T.minimum(
1,
T.cast(total_iters, theano.config.floatX) / lib.floatX(ALPHA_ITERS)
)
if VANILLA:
cost = reconst_cost
else:
cost = reconst_cost + (alpha * reg_cost)
sample_fn_latents = T.matrix('sample_fn_latents')
sample_fn = theano.function(
[sample_fn_latents, images],
T.nnet.sigmoid(decode_algo(sample_fn_latents, images)),
on_unused_input='warn'
)
eval_fn = theano.function(
[images, total_iters],
cost.mean()
)
train_data, dev_data, test_data = lib.mnist_binarized.load(
BATCH_SIZE,
TEST_BATCH_SIZE
)
def shuffle_data(samples, labels):
idx = np.arange(samples.shape[0])
np.random.shuffle(idx)
#print (samples.shape, labels.shape)
samples, labels = samples[idx], labels[idx]
return samples, labels
decay = 1e-6
learning_rate = 0.01
epochs = 1000
# theano expressions
X = T.matrix() #features
Y = T.matrix() #output
w1, b1 = create_weights(36, 10), create_bias(
10) #weights and biases from input to hidden layer
w2, b2 = create_weights(10, 6, logistic=False), create_bias(
6) #weights and biases from hidden to output layer
h1 = T.nnet.sigmoid(T.dot(X, w1) + b1)
py = T.nnet.softmax(T.dot(h1, w2) + b2)
y_x = T.argmax(py, axis=1)
cost = T.mean(T.nnet.categorical_crossentropy(
py, Y)) + decay * (T.sum(T.sqr(w1) + T.sum(T.sqr(w2))))
params = [w1, b1, w2, b2]
def trainword(keyword, window_radius = 3, learning_rate = 0.1, n_epochs = 10,batch_size = 1,filter_height=3,filter_width = 50, pool_height=1,pool_width = 1, loginput_num = 50, vector_size = 50):
print '==training parameters=='
print 'window_radius: '+str(window_radius)
print 'vector_size: '+str(vector_size)
print 'filter_height: '+str(filter_height)
print 'filter_width: '+str(filter_width)
print 'pool_height: '+str(pool_height)
print 'pool_width: '+str(pool_width)
print 'loginput_num: '+str(loginput_num)
print 'learning_rate: '+str(learning_rate)
print 'n_epochs: '+str(n_epochs)
print 'batch_size: '+str(batch_size)
rng = numpy.random.RandomState(23455)
datasets = load_data_word(keyword, window_radius, vector_size)
train_set_x, train_set_y, trainsentence = datasets[0][0]
valid_set_x, valid_set_y, validsentence = datasets[0][1]
test_set_x, test_set_y, testsentence = datasets[0][2]
senselist = datasets[1]
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
print n_train_batches, n_valid_batches, n_test_batches
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
print '... building the model for '+keyword
layer0_input = x.reshape((batch_size, 1, 2*window_radius+1, vector_size))
layer0 = WsdConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 2*window_radius+1, vector_size),
filter_shape=(1, 1, filter_height, filter_width),
poolsize=(pool_height, pool_width)
)
layer1_input = layer0.output.flatten(2)
#layer1_input = layer0_input.flatten(2)
layer1 = HiddenLayer(
rng,
input=layer1_input,
#n_in=(2*window_radius+1)*(vector_size+1-filter_width+1-pool_width),
n_in=int((2*window_radius+2-filter_height)/float(pool_height))*int((vector_size+1-filter_width)/float(pool_width)),
n_out=loginput_num,
activation=T.tanh
)
layer2 = LogisticRegression(input=layer1_input, n_in=int((2*window_radius+2-filter_height)/float(pool_height))*int((vector_size+1-filter_width)/float(pool_width)), n_out=20)
cost = layer2.negative_log_likelihood(y)
test_model = theano.function(
[index],
layer2.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
validate_model = theano.function(
[index],
layer2.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
output_size = theano.function(
[index],
[layer0.output.shape],
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size]
}
)
output_model = theano.function(
[index],
[layer2.y_pred],
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size]
}
)
output_test = theano.function(
[index],
[layer2.y_pred],
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size]
}
)
params = layer2.params + layer0.params
grads = T.grad(cost, params)
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)
]
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
print '... training'
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_params = 0
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i
in xrange(n_valid_batches)]
#for index in range(0, n_valid_batches):
# print output_model(index)
# print valid_set_y[index * batch_size: (index + 1) * batch_size].eval()
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
best_params = [copy.deepcopy(layer0.params), copy.deepcopy(layer1.params), copy.deepcopy(layer2.params)]
# test it on the test set
test_losses = [
test_model(i)
for i in xrange(n_test_batches)
]
#print params[0].eval()
#print (params[0].eval() == layer2.params[0].eval())
#print validation_losses
for index in range(0, n_valid_batches):
for i in range(0, batch_size):
true_i = batch_size*index+i
#print output_model(index)
print validsentence[true_i], '\t',senselist[output_model(index)[0][i]], '\t', senselist[valid_set_y[true_i].eval()]
#print test_losses
test_score = numpy.mean(test_losses)
for index in range(0, n_test_batches):
for i in range(0, batch_size):
true_i = batch_size*index+i
#print output_model(index)
print testsentence[true_i], '\t',senselist[output_test(index)[0][i]], '\t', senselist[test_set_y[true_i].eval()]
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
for index in range(0, n_test_batches):
for i in range(0, batch_size):
true_i = batch_size*index+i
#print output_model(index)
print testsentence[true_i], '\t',senselist[output_test(index)[0][i]], '\t', senselist[test_set_y[true_i].eval()]
layer0.W = copy.deepcopy(best_params[0][0])
layer0.b = copy.deepcopy(best_params[0][1])
#layer0.params = [layer0.W, layer0.b]
layer1.W = copy.deepcopy(best_params[1][0])
layer1.b = copy.deepcopy(best_params[1][1])
#layer1.params = [layer1.W, layer1.b]
layer2.W = copy.deepcopy(best_params[2][0])
layer2.b = copy.deepcopy(best_params[2][1])
#layer2.params = [layer2.W, layer2.b]
for index in range(0, n_test_batches):
for i in range(0, batch_size):
true_i = batch_size*index+i
#print output_model(index)
print testsentence[true_i], '\t',senselist[output_test(index)[0][i]], '\t', senselist[test_set_y[true_i].eval()]
print('Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def test_GRBM_DBN(finetune_lr=0.2, pretraining_epochs=1,
pretrain_lr=0.01, k=1, training_epochs=10,
dataset='mnist.pkl.gz', batch_size=10, annealing_learning_rate=0.999):
"""
Demonstrates how to train and test a Deep Belief Network.
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used in the finetune stage
:type pretraining_epochs: int
:param pretraining_epochs: number of epoch to do pretraining
:type pretrain_lr: float
:param pretrain_lr: learning rate to be used during pre-training
:type k: int
:param k: number of Gibbs steps in CD/PCD
:type training_epochs: int
:param training_epochs: maximal number of iterations ot run the optimizer
:type dataset: string
:param dataset: path the the pickled dataset
:type batch_size: int
:param batch_size: the size of a minibatch
"""
datasets = load_data_grbm(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# numpy random generator
numpy_rng = numpy.random.RandomState(123)
print '... building the model'
# construct the Deep Belief Network
x_skeleton = T.matrix('x')
dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=28 * 28,
hidden_layers_sizes=[1000, 1000, 1000],
n_outs=10, finetune_lr=finetune_lr, input=x_skeleton)
#########################
# PRETRAINING THE MODEL #
#########################
print '... getting the pretraining functions'
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size,
k=k)
# The following part is to get the value for testing
if False:
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 2D vector of [float32] labels
dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=28 * 28,
hidden_layers_sizes=[1000, 1000, 1000],
n_outs=10)
dbn.load('dbn_params.npy')
#train_fn, validate_model, test_model = dbn.build_finetune_functions(
# datasets=datasets, batch_size=batch_size,
# learning_rate=finetune_lr)
valid_score_i = theano.function([index], dbn.errors,
givens={dbn.x: valid_set_x[index * batch_size:
(index + 1) * batch_size],
dbn.y: valid_set_y[index * batch_size:
(index + 1) * batch_size]})
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_valid_batches /= batch_size
validation_losses = [valid_score_i(i) for i in xrange(n_valid_batches)]
validation_losses = valid_score_i()
this_validation_loss = numpy.mean(validation_losses)
## get the actual softmax layer
temp = theano.function([index],dbn.logLayer.p_y_given_x,
givens={dbn.x: valid_set_x[index * batch_size:
(index + 1) * batch_size]})
temp_out = [temp(i) for i in xrange(n_valid_batches)]
print '... pre-training the model'
start_time = time.clock()
## Pre-train layer-wise
for i in xrange(dbn.n_layers):
start_time_temp = time.clock()
if i==0:
# for GRBM, the The learning rate needs to be about one or
#two orders of magnitude smaller than when using
#binary visible units and some of the failures reported in the
# literature are probably due to using a
pretrain_lr_new = pretrain_lr*0.1
else:
pretrain_lr_new = pretrain_lr
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index=batch_index,
lr=pretrain_lr_new))
end_time_temp = time.clock()
print 'Pre-training layer %i, epoch %d, cost %f ' % (i, epoch, numpy.mean(c)) + ' ran for %d sec' % ((end_time_temp - start_time_temp) )
end_time = time.clock()
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
########################
# FINETUNING THE MODEL #
########################
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, validate_model = dbn.build_finetune_functions(
datasets=datasets, batch_size=batch_size,
annealing_learning_rate=annealing_learning_rate)
print '... finetunning the model'
# early-stopping parameters
patience = 4 * n_train_batches # look as this many examples regardless
patience_increase = 2. # wait this much longer when a new best is
# found
improvement_threshold = 0.999 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < training_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
import warnings
warnings.filterwarnings("ignore")
validation_losses = validate_model()
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if (this_validation_loss < best_validation_loss *
improvement_threshold):
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(('Optimization complete with best validation score of %f %%,'
'with test performance %f %%') %
(best_validation_loss * 100., test_score * 100.))
print >> sys.stderr, ('The fine tuning code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time)
/ 60.))
print dbn.state_learning_rate.get_value()
def test_mlp(learning_rate=0.05,
L1_reg=0.00,
L2_reg=0.0001,
n_epochs=1000,
split=0,
batch_size=1,
n_hidden=[100],
rot=5,
seuil=0.25):
datasets = load_data(split)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = valid_set_x.get_value(
borrow=True).shape[0] #/ batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] #/ batch_size
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.matrix('y') # the labels are presented as 1D vector of
# [int] labels
rng = numpy.random.RandomState(1234)
shp = train_set_x.get_value().shape[1]
# construct the MLP class
classifier = MLP(rng=rng, input=x, n_in=shp, n_hidden=n_hidden, n_out=shp)
# start-snippet-4
# the cost we minimize during training is the negative log likelihood of
# the model plus the regularization terms (L1 and L2); cost is expressed
# here symbolically
cost = (classifier.negative_log_likelihood(y) + L1_reg * classifier.L1 +
L2_reg * classifier.L2_sqr)
# end-snippet-4
# compiling a Theano function that computes the mistakes that are made
# by the model on a minibatch
pred_test = theano.function(inputs=[index],
outputs=[classifier.y_pred, y],
givens={
x: test_set_x[index:(index + 1)],
y: test_set_y[index:(index + 1)]
})
pred_train = theano.function(inputs=[index],
outputs=[classifier.y_pred, y],
givens={
x: train_set_x[index:(index + 1)],
y: train_set_y[index:(index + 1)]
})
pred_valid = theano.function(inputs=[index],
outputs=[classifier.y_pred, y],
givens={
x: valid_set_x[index:(index + 1)],
y: valid_set_y[index:(index + 1)]
})
def evaluation(fn, d, ens, epoch, seuil, plot):
x = d.get_value()
n_samples = x.shape[0]
if plot: bigpic = []
acc = []
for i in xrange(n_samples):
pred, true = fn(i)
pred_mask = pred * (x[i] > 0)
pred_out = (pred_mask >= seuil).astype(numpy.int)
true_out = true.astype(numpy.int)
acc += [jaccard(pred_out, true_out)]
if plot:
bigpic += [x[i], pred, pred_mask, pred_out, true_out]
this_acc = numpy.mean(acc)
std_acc = numpy.std(acc)
print('epoch %i, %s error %f +- %f %%' %
(epoch, ens, this_acc * 100., std_acc * 100.))
if plot:
bigpic = numpy.vstack(bigpic)
tile = tile_raster_images(bigpic, (311, 457),
(n_samples // 4, 5 * 4),
output_pixel_vals=True)
Im.fromarray(tile).convert("RGB").save("images/" + ens +
str(epoch) + ".png")
return this_acc
gparams = [T.grad(cost, param) for param in classifier.params]
updates = [(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, gparams)]
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
###############
# TRAIN MODEL #
###############
n_training_samples = train_set_x.get_value().shape[0]
print '... training over %i training samples' % n_training_samples
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 1 # 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = -numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
evaluation(pred_train, train_set_x, "train", epoch, seuil, True)
print "training started..."
while (epoch < n_epochs) and (not done_looping):
rotate_data((train_set_x, train_set_y), rot)
epoch = epoch + 1
minibatch_avg_cost = []
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost += [train_model(minibatch_index)]
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
print "mean avg cost over training :: ", numpy.mean(
minibatch_avg_cost)
evaluation(pred_train, train_set_x, "train", epoch, seuil,
True)
val = evaluation(pred_valid, valid_set_x, "valid", epoch,
seuil, True)
# if we got the best validation score until now
if val > best_validation_loss:
#improve patience if loss improvement is good enough
if (val > best_validation_loss * improvement_threshold):
patience = max(patience, iter * patience_increase)
best_validation_loss = val
best_iter = iter
evaluation(pred_test, test_set_x, "test", epoch, seuil,
True)
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(('Optimization complete. Best validation score of %f %% '
'obtained at iteration %i, with test performance %f %%') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def __init__(self, rng, filter_shape, image_shape, poolsize=2, xin=None):
assert image_shape[1] == filter_shape[1]
self.image_shape=theano.shared(
value=np.asarray(image_shape,dtype='int16'),borrow=True)
self.poolsize=(poolsize,poolsize)
#self.input = input
if xin:
self.x=xin
else:
self.x = T.matrix(name='input')
self.x1=self.x.reshape(self.image_shape,ndim=4)
self.filter_shape=filter_shape
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = np.prod(filter_shape[1:])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) /
np.prod(self.poolsize))
# initialize weights with random weights
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
np.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
self.W_prime=self.W[:,:,::-1,::-1]
self.W_prime=self.W_prime.dimshuffle(1,0,2,3)
#self.W_prime=self.W_prime[:,::-1]
#print self.W.get_value()
#print self.W_prime.eval()
# the bias is a 1D tensor -- one bias per output feature map
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
bp_values = np.zeros((filter_shape[1],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
self.b_prime = theano.shared(value=bp_values, borrow=True)
if poolsize<-1:
self.x1=self.x1.repeat(int(-poolsize), axis=2).repeat(int(-poolsize), axis=3)
# convolve input feature maps with filters
conv_out = conv2d(
input=self.x1,
filters=self.W,
filter_shape=filter_shape,
#image_shape=self.image_shape.eval(),
border_mode='full'
)
bp=(filter_shape[2]-1)/2
conv_out=conv_out[:,:,bp:-bp,bp:-bp]
# downsample each feature map individually, using maxpooling
if poolsize>1:
try:
self.pooled_out = pool.pool_2d(
input=conv_out,
ws=self.poolsize,
ignore_border=True
)
except:
self.pooled_out = pool.pool_2d(
input=conv_out,
ds=self.poolsize,
ignore_border=True
)
else:
self.pooled_out=conv_out
self.hidden = T.maximum(0,(self.pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')))
# store parameters of this layer
self.params = [self.W,self.b]
def __init__(self, numpy_rng, theano_rng=None, n_ins=784,
hidden_layers_sizes=[500, 500], n_outs=10, finetune_lr=0.1, input_x=None, label=None):
self.sigmoid_layers = []
self.rbm_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
# wudi add the mean and standard deviation of the activation values to exam the neural net
# Reference: Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot, Yoshua Bengio
self.out_mean = []
self.out_std = []
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
if input_x is None:
self.x = T.matrix('x') # the data is presented as rasterized images
else:
self.x = input_x
if label is None:
self.y = T.ivector('y') # the labels are presented as 1D vector
# of [int] labels
else:
self.y = label
for i in xrange(self.n_layers):
if i == 0:
input_size = n_ins
layer_input = self.x
else:
input_size = hidden_layers_sizes[i - 1]
layer_input = self.sigmoid_layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=T.nnet.sigmoid)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
self.out_mean.append(T.mean(sigmoid_layer.output))
self.out_std.append(T.std(sigmoid_layer.output))
self.params.extend(sigmoid_layer.params)
# Construct an RBM that shared weights with this layer
if i == 0:
rbm_layer = GBRBM(input=layer_input, n_in=input_size, n_hidden=hidden_layers_sizes[i], \
W=None, hbias=None, vbias=None, numpy_rng=None, transpose=False, activation=T.nnet.sigmoid,
theano_rng=None, name='grbm', W_r=None, dropout=0, dropconnect=0)
else:
rbm_layer = RBM(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
W=sigmoid_layer.W,
hbias=sigmoid_layer.b)
self.rbm_layers.append(rbm_layer)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_outs)
self.params.extend(self.logLayer.params)
# compute the cost for second phase of training, defined as the
# negative log likelihood of the logistic regression (output) layer
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
# compute the gradients with respect to the model parameters
# symbolic variable that points to the number of errors made on the
# minibatch given by self.x and self.y
self.errors = self.logLayer.errors(self.y)
#################################################
# Wudi change the annealing learning rate:
#################################################
self.state_learning_rate = theano.shared(numpy.asarray(finetune_lr,
dtype=theano.config.floatX),
borrow=True)
def __init__(
self,
input_shape,
output_dim,
prob_network=None,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
optimizer=None,
use_trust_region=True,
step_size=0.01,
normalize_inputs=True,
name=None,
):
"""
:param input_shape: Shape of the input data.
:param output_dim: Dimension of output.
:param hidden_sizes: Number of hidden units of each layer of the mean network.
:param hidden_nonlinearity: Non-linearity used for each layer of the mean network.
:param optimizer: Optimizer for minimizing the negative log-likelihood.
:param use_trust_region: Whether to use trust region constraint.
:param step_size: KL divergence constraint for each iteration
"""
Serializable.quick_init(self, locals())
if optimizer is None:
if use_trust_region:
optimizer = PenaltyLbfgsOptimizer()
else:
optimizer = LbfgsOptimizer()
self.output_dim = output_dim
self._optimizer = optimizer
if prob_network is None:
prob_network = MLP(
input_shape=input_shape,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=NL.softmax,
)
l_prob = prob_network.output_layer
LasagnePowered.__init__(self, [l_prob])
xs_var = prob_network.input_layer.input_var
ys_var = TT.imatrix("ys")
old_prob_var = TT.matrix("old_prob")
x_mean_var = theano.shared(
np.zeros((1,) + input_shape),
name="x_mean",
broadcastable=(True,) + (False, ) * len(input_shape)
)
x_std_var = theano.shared(
np.ones((1,) + input_shape),
name="x_std",
broadcastable=(True,) + (False, ) * len(input_shape)
)
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
prob_var = L.get_output(l_prob, {prob_network.input_layer: normalized_xs_var})
old_info_vars = dict(prob=old_prob_var)
info_vars = dict(prob=prob_var)
dist = self._dist = Categorical()
mean_kl = TT.mean(dist.kl_sym(old_info_vars, info_vars))
loss = - TT.mean(dist.log_likelihood_sym(ys_var, info_vars))
predicted = special.to_onehot_sym(TT.argmax(prob_var, axis=1), output_dim)
self._f_predict = ext.compile_function([xs_var], predicted)
self._f_prob = ext.compile_function([xs_var], prob_var)
self._l_prob = l_prob
optimizer_args = dict(
loss=loss,
target=self,
network_outputs=[prob_var],
)
if use_trust_region:
optimizer_args["leq_constraint"] = (mean_kl, step_size)
optimizer_args["inputs"] = [xs_var, ys_var, old_prob_var]
else:
optimizer_args["inputs"] = [xs_var, ys_var]
self._optimizer.update_opt(**optimizer_args)
self._use_trust_region = use_trust_region
self._name = name
self._normalize_inputs = normalize_inputs
self._x_mean_var = x_mean_var
self._x_std_var = x_std_var
def createGradientFunctions(self):
# Create the Theano variables
x_arg1 = T.matrix('x_arg1', dtype='float32')
x_arg2 = T.matrix('x_arg2', dtype='float32')
true_class = T.matrix('true_class', dtype='float32')
eps = T.matrix("eps", dtype='float32')
Arg1_W1, Arg1_W2, Arg1_W3, Arg1_W4, Arg1_W5, \
Arg2_W2, Arg2_W3, Arg2_W4, \
Arg1_W2_prior, Arg1_W3_prior, Arg2_W2_prior, Arg2_W3_prior, \
Arg1_b1, Arg1_b4, Arg1_b5, Arg2_b4, b2, b3, \
L_Wc, L_bc, Label_W1, Label_W2, Label_W3, \
L_W4, Label_b1, L_b4, b2_prior, b3_prior, L_W6, L_b6, L_W7, L_b7, L_W8, L_b8 = self.tparams
# Parameter Tying
Arg2_W1 = Arg1_W1
Arg2_b1 = Arg1_b1
Arg2_W5 = Arg1_W5
Arg2_b5 = Arg1_b5
# Neural Inferencer
h_arg1_encoder = T.tanh(T.dot(Arg1_W1,x_arg1) + Arg1_b1.dimshuffle(0, 'x'))
h_arg2_encoder = T.tanh(T.dot(Arg2_W1,x_arg2) + Arg2_b1.dimshuffle(0, 'x'))
l_encoder = T.tanh(T.dot(Label_W1,true_class) + Label_b1.dimshuffle(0, 'x'))
mu_poster_encoder = T.dot(Arg1_W2,h_arg1_encoder) + T.dot(Arg2_W2,h_arg2_encoder) \
+ T.dot(Label_W2,l_encoder) + b2.dimshuffle(0, 'x')
log_sigma_poster_encoder = \
np.float32(0.5)*(T.dot(Arg1_W3,h_arg1_encoder) + T.dot(Arg2_W3,h_arg2_encoder) \
+ T.dot(Label_W3,l_encoder) + b3.dimshuffle(0, 'x'))
mu_prior_encoder = T.dot(Arg1_W2_prior,h_arg1_encoder) + T.dot(Arg2_W2_prior,h_arg2_encoder) \
+ b2_prior.dimshuffle(0, 'x')
log_sigma_prior_encoder = \
np.float32(0.5)*(T.dot(Arg1_W3_prior,h_arg1_encoder) + T.dot(Arg2_W3_prior,h_arg2_encoder) \
+ b3_prior.dimshuffle(0, 'x'))
#Find the hidden variable z
z = mu_poster_encoder + T.exp(log_sigma_poster_encoder)*eps
prior = T.sum((log_sigma_prior_encoder - log_sigma_poster_encoder) + \
(T.exp(log_sigma_poster_encoder)**np.float32(2) + \
(mu_poster_encoder - mu_prior_encoder)**np.float32(2)) /
(np.float32(2)*(T.exp(log_sigma_prior_encoder)**np.float32(2))) - np.float32(0.5))
#Neural Generator
h_arg1_decoder = T.tanh(T.dot(Arg1_W4,z) + Arg1_b4.dimshuffle(0, 'x'))
h_arg2_decoder = T.tanh(T.dot(Arg2_W4,z) + Arg2_b4.dimshuffle(0, 'x'))
y_arg1 = T.nnet.sigmoid(T.dot(Arg1_W5,h_arg1_decoder) + Arg1_b5.dimshuffle(0, 'x'))
y_arg2 = T.nnet.sigmoid(T.dot(Arg2_W5,h_arg2_decoder) + Arg2_b5.dimshuffle(0, 'x'))
logpxz = -(T.nnet.binary_crossentropy(y_arg1,x_arg1).sum() \
+ T.nnet.binary_crossentropy(y_arg2,x_arg2).sum())
l_decoder = T.tanh(T.dot(L_W4,z) + L_b4.dimshuffle(0, 'x'))
l_pred_decoder = T.tanh(T.dot(L_W4, mu_prior_encoder) + L_b4.dimshuffle(0, 'x'))
l_decoder = T.tanh(T.dot(L_W6,l_decoder) + L_b6.dimshuffle(0, 'x'))
l_pred_decoder = T.tanh(T.dot(L_W6,l_pred_decoder) + L_b6.dimshuffle(0, 'x'))
l_decoder = T.tanh(T.dot(L_W7,l_decoder) + L_b7.dimshuffle(0, 'x'))
l_pred_decoder = T.tanh(T.dot(L_W7,l_pred_decoder) + L_b7.dimshuffle(0, 'x'))
l_decoder = T.tanh(T.dot(L_W8,l_decoder) + L_b8.dimshuffle(0, 'x'))
l_pred_decoder = T.tanh(T.dot(L_W8,l_pred_decoder) + L_b8.dimshuffle(0, 'x'))
pred_class = T.nnet.softmax(T.dot(L_Wc,l_decoder) + L_bc.dimshuffle(0, 'x'))
logpc = -(T.nnet.categorical_crossentropy(pred_class,true_class).sum())
pred_level = T.nnet.softmax(T.dot(L_Wc,l_pred_decoder) + L_bc.dimshuffle(0, 'x'))
logp = - logpxz - logpc + prior
#Compute all the gradients
derivatives = T.grad(logp,wrt=self.tparams)
# apply gradient clipping here
if self.clip_c > 0.:
g2 = 0.
for g in derivatives:
g2 += (g**2).sum()
new_grads = []
for g in derivatives:
new_grads.append(T.switch(g2 > (self.clip_c**2),
g / T.sqrt(g2) * self.clip_c,
g))
derivatives = new_grads
#Add the lowerbound so we can keep track of results
derivatives.append(logp)
self.gradientfunction = theano.function([x_arg1,x_arg2,true_class,eps], \
derivatives, on_unused_input='ignore')
self.lowerboundfunction = theano.function([x_arg1,x_arg2,true_class,eps], \
logp, on_unused_input='ignore')
self.predictionfunction = theano.function([x_arg1,x_arg2], \
pred_level.T, on_unused_input='ignore')
#Adam Optimizer
# This code is adapted from https://github.com/nyu-dl/dl4mt-tutorial/blob/master/session2/nmt.py
def adam(lr, tparams, grads, inp, cost, beta1=0.9, beta2=0.999, e=1e-8):
gshared = [theano.shared(p.get_value() * 0., name='%s_grad' % k)
for k, p in zip(self.params_names, tparams)]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inp, cost, updates=gsup, profile=False)
updates = []
t_prev = theano.shared(np.float32(0.))
t = t_prev + 1.
lr_t = lr * T.sqrt(1. - beta2**t) / (1. - beta1**t)
for p, g in zip(tparams, gshared):
m = theano.shared(p.get_value() * 0., p.name + '_mean')
v = theano.shared(p.get_value() * 0., p.name + '_variance')
m_t = beta1 * m + (1. - beta1) * g
v_t = beta2 * v + (1. - beta2) * g**2
step = lr_t * m_t / (T.sqrt(v_t) + e)
p_t = p - step
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((t_prev, t))
f_update = theano.function([lr], [], updates=updates,
on_unused_input='ignore', profile=False)
return f_grad_shared, f_update
lr = T.scalar(name='lr')
self.f_grad_shared, self.f_update = \
adam(lr, self.tparams, derivatives[:-1], [x_arg1,x_arg2,true_class,eps], logp)
def make_node(self, _x):
x = as_tensor_variable(_x)
if x.type.ndim != 1:
raise TypeError('AllocDiag only works on vectors', _x)
return Apply(self, [x], [tensor.matrix(dtype=x.type.dtype)])
def main():
usage="""Segment a tomograph using convolutional neural network. Please run this program from the GUI in e2projectmanager.py."""
#print usage
parser = EMArgumentParser(usage=usage,version=EMANVERSION)
#parser.add_header(name="tmpheader", help='temp label', title="### This program is NOT avaliable yet... ###", row=0, col=0, rowspan=1, colspan=2, mode="train,test")
parser.add_argument("--trainset",help="Training set.", default=None, guitype='filebox', browser="EMParticlesTable(withmodal=True)", row=1, col=0,rowspan=1, colspan=3, mode="train")
parser.add_argument("--from_trained", type=str,help="Start from pre-trained neural network", default=None,guitype='filebox',browser="EMBrowserWidget(withmodal=True)", row=2, col=0, rowspan=1, colspan=3, mode="train,test")
parser.add_argument("--netout", type=str,help="Output neural net file name", default="nnet_save.hdf",guitype='strbox', row=3, col=0, rowspan=1, colspan=3, mode="train")
parser.add_argument("--learnrate", type=float,help="Learning rate ", default=.01, guitype='floatbox', row=4, col=0, rowspan=1, colspan=1, mode="train")
parser.add_argument("--niter", type=int,help="Training iterations", default=20, guitype='intbox', row=4, col=1, rowspan=1, colspan=1, mode="train")
parser.add_argument("--ncopy", type=int,help="Number of copies for each particle", default=1, guitype='intbox', row=5, col=0, rowspan=1, colspan=1, mode="train")
parser.add_argument("--batch", type=int,help="Batch size for the stochastic gradient descent. Default is 20.", default=20, guitype='intbox', row=5, col=1, rowspan=1, colspan=1, mode="train")
parser.add_argument("--nkernel", type=str,help="Number of kernels for each layer, from input to output. The number of kernels in the last layer must be 1. ", default="40,40,1", guitype='strbox', row=6, col=0, rowspan=1, colspan=1, mode="train")
parser.add_argument("--ksize", type=str,help="Width of kernels of each layer, the numbers must be odd. Note the number of layers should be the same as the nkernel option. ", default="15,15,15", guitype='strbox', row=6, col=1, rowspan=1, colspan=1, mode="train")
parser.add_argument("--poolsz", type=str,help="Pooling size for each layer. Note the number of layers should be the same as the nkernel option. ", default="2,1,1", guitype='strbox', row=7, col=0, rowspan=1, colspan=1, mode="train")
parser.add_argument("--weightdecay", type=float,help="Weight decay. Used for regularization.", default=1e-6, guitype='floatbox', row=7, col=1, rowspan=1, colspan=1, mode="train")
parser.add_argument("--trainout", action="store_true", default=False ,help="Output the result of the training set", guitype='boolbox', row=8, col=0, rowspan=1, colspan=1, mode='train[True]')
parser.add_argument("--training", action="store_true", default=False ,help="Doing training", guitype='boolbox', row=8, col=1, rowspan=1, colspan=1, mode='train[True]')
parser.add_argument("--tomograms", type=str,help="Tomograms input.", default=None,guitype='filebox',browser="EMBrowserWidget(withmodal=True)", row=1, col=0, rowspan=1, colspan=3, mode="test")
parser.add_argument("--applying", action="store_true", default=False ,help="Applying the neural network on tomograms", guitype='boolbox', row=4, col=0, rowspan=1, colspan=1, mode='test[True]')
parser.add_argument("--dream", action="store_true", default=False ,help="Iterativly applying the neural network on noise")
parser.add_argument("--to3d", action="store_true", default=True ,help="convert to result to 3D.", guitype='boolbox', row=5, col=1, rowspan=1, colspan=1, mode='test')
parser.add_argument("--output", type=str,help="Segmentation out file name", default="tomosegresult.hdf", guitype='strbox', row=3, col=0, rowspan=1, colspan=1, mode="test")
parser.add_argument("--threads", type=int,help="Number of thread to use when applying neural net on test images. Not used during trainning", default=12, guitype='intbox', row=10, col=0, rowspan=1, colspan=1, mode="test")
parser.add_argument("--ppid", type=int, help="Set the PID of the parent process, used for cross platform PPID",default=-1)
(options, args) = parser.parse_args()
E2n=E2init(sys.argv,options.ppid)
#### parse the options.
options.nkernel=[int(i) for i in options.nkernel.split(',')]
options.ksize=[int(i) for i in options.ksize.split(',')]
if options.poolsz:
options.poolsz=[int(i) for i in options.poolsz.split(',')]
#### This is supposed to test the overfitting of the network by applying it on pure noise repeatly
#### The function is no longer maintained so it may or may not work..
if options.dream:
print("This function is no longer supported.. exit.")
return
#os.environ["THEANO_FLAGS"]="optimizer=None"
#print "Testing on big images, Theano optimizer disabled"
#import_theano()
#convnet=load_model(options.from_trained)
#dream(convnet,options)
#E2end(E2n)
#exit()
if options.applying:
apply_neuralnet(options)
E2end(E2n)
exit()
os.environ["THEANO_FLAGS"]="optimizer=fast_run"
import_theano()
batch_size=options.batch
#### Train da with particles first.
if options.trainset==None:
print("No training set input...exit.")
exit()
rng = np.random.RandomState(123)
labelshrink=np.prod(options.poolsz)
print("loading particles...")
particles=load_particles(options.trainset,labelshrink,options.ncopy, rng)
train_set_x= particles[0]
labels=particles[1]
shape=particles[2]
ntrain=particles[3]
#print "Number of particles: {}".format(train_set_x.shape.eval()[0])
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
image_shape=(batch_size, shape[2], shape[0],shape[1])
if options.from_trained!=None:
convnet=load_model(options.from_trained)
convnet.update_shape(image_shape)
else:
print("setting up model")
convnet = StackedConvNet(
rng,
nkernel=options.nkernel,
ksize=options.ksize,
poolsz=options.poolsz,
imageshape=image_shape
)
#print shape
if (options.niter>0):
print("training the convolutional network...")
classify=convnet.get_classify_func(train_set_x,labels,batch_size)
learning_rate=options.learnrate
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
v0=np.inf
nbad=0
for epoch in xrange(options.niter):
# go through the training set
c = [] #### train set loss
v = [] #### valid set loss
if epoch==0:
print(classify(0,lr=learning_rate,wd=options.weightdecay))
for batch_index in xrange(n_train_batches):
if batch_index*batch_size < ntrain:
err=classify(batch_index,
lr=learning_rate,
wd=options.weightdecay)
c.append(err)
if epoch==0 and batch_index<5:
print(err)
else:
err=classify(batch_index,
lr=0,
wd=options.weightdecay)
v.append(err)
#print len(v), len(c)
learning_rate*=.9
print("Training epoch {:d}, train loss {:.3f}, learning rate {:.3f}".format(epoch, np.mean(c), learning_rate), end=' ')
if len(v)>0:
print("valid loss {:.3f}".format(np.mean(v)), end=' ')
if np.mean(v)>v0 and np.mean(v)>np.mean(c):
nbad+=1
print('*')
else:
nbad=0
print()
v0=np.mean(v)
if nbad>2:
print("loss increase in validation set. Overfitting. Stop.")
break
else:
print()
#######################################
#print convnet.clslayer.W.get_value()
#print convnet.clslayer.b.get_value()
if options.trainout:
print("Generating results ...")
nsample=100
convnet.update_shape((nsample, shape[2], shape[0],shape[1]))
test_cls = theano.function(
inputs=[],
outputs=convnet.clslayer.get_image(False),
givens={
convnet.x: train_set_x[:nsample]
}
)
if options.netout.endswith(".hdf"):
fname="trainout_{}".format(options.netout)
else:
fname="trainout_{}.hdf".format(options.netout)
try:os.remove(fname)
except: pass
#print convnet.outsize,shape
mid=test_cls()
ipt= train_set_x[:nsample]
ipt= ipt.eval()
lb= labels[:nsample].eval()
amp=[]
for t in range(nsample):
#### raw image
if shape[2]==1:
img=ipt[t].reshape(shape[0],shape[1])
else:
img=ipt[t].reshape(shape[2],shape[0],shape[1])
img=img[shape[2]/2]
e0 = from_numpy(img.astype("float32"))
e0.write_image(fname,-1)
#### manual annotation
img=lb[t].reshape(convnet.outsize,convnet.outsize)
e1 = from_numpy(img.astype("float32"))
e1=e1.get_clip(Region((convnet.outsize-shape[0])/2,(convnet.outsize-shape[0])/2,shape[0],shape[0]))
e1.scale(float(shape[0])/float(convnet.outsize))
e1.process_inplace("threshold.binary", {"value":.67})
e1.write_image(fname,-1)
#### neural net output
img=mid[t].reshape(convnet.outsize,convnet.outsize)
e2 = from_numpy(img.astype("float32"))
e2=e2.get_clip(Region((convnet.outsize-shape[0])/2,(convnet.outsize-shape[0])/2,shape[0],shape[0]))
#print float(shape[0])/float(convnet.outsize)
e2.scale(float(shape[0])/float(convnet.outsize))
e2.write_image(fname,-1)
#### measure the amplitude of the neural network output by comparing it to the label
e2.mult(e1)
amp.append(e2["mean_nonzero"])
print("amplitude: ", np.mean(amp))
convnet.amplitude=np.mean(amp)
print("Writing output on training set in {}".format(fname))
save_model(convnet, options.netout, options)
print("Done")
E2end(E2n)
def build_model(tparams, model_options):
x = T.matrix('x', dtype='float32')
start_temperature = T.scalar('start_temperature', dtype='float32')
num_step = T.scalar('num_step', dtype='int32')
loss = compute_loss(x, model_options, tparams, start_temperature, num_step)
return x, loss, start_temperature, num_step
def __init__(self,N_tot,D,Q,Domain_number,Ydim,Hiddenlayerdim1,Hiddenlayerdim2,num_MC,n_rff):
########################################
#BCなXの設定 後でこれもレイヤー化する
self.Xlabel=T.matrix('Xlabel')
self.X=T.matrix('X')
self.Y=T.matrix('Y')
N=self.X.shape[0]
self.Weight=T.matrix('Weight')
########################
#hiddenlyaerの作成
self.Data_input=T.tile(self.X,(num_MC,1,1))
##########################################
####X側の推論
self.RFF_X=RFFLayer(rng, self.Data_input, n_in=D, n_out=Q, num_MC=num_MC,num_FF=n_rff,Domain_number=Domain_number,number="X",Domain_consideration=True)
self.params = self.RFF_X.all_params
self.hyp_params=self.RFF_X.hyp_params
self.variational_params=self.RFF_X.variational_params
##############################################################################################
###Y側の計算
self.RFF_Y=RFFLayer(rng, self.RFF_X.output, n_in=Q, n_out=Ydim, num_MC=num_MC,num_FF=n_rff,number="Y",Domain_consideration=False)
self.params.extend(self.RFF_Y.all_params)
self.hyp_params.append(self.RFF_Y.lhyp)
self.variational_params.extend(self.RFF_Y.variational_params)
##########################################
#パラメータの格納
#self.no_updates=self.RFF_X.no_update
self.wrt={}
for i in self.params:
self.wrt[str(i)]=i
###########################################
###目的関数
#############X側
#self.LL_X = self.RFF_X.likelihood_domain(self.X,self.Xlabel)*N_tot/(N*num_MC)
self.KL_WX = self.RFF_X.KL_W
#############Y側
self.LL_Y = self.RFF_Y.liklihood_nodomain(self.Y)*N_tot/(N*num_MC)
self.KL_WY = self.RFF_Y.KL_W
#y=self.Gaussian_layer_Y.softmax_class()
#self.LLY = -T.mean(T.nnet.categorical_crossentropy(y, self.Y))*N
#############真ん中と予測
#self.error = self.RFF_Y.error_RMSE(self.Y)
pred = T.mean(self.RFF_Y.output,0)
self.error = (T.mean((self.Y - pred)**2,0))**0.5
#mu=T.mean(target,0)
#self.error= (T.mean(T.mean((self.Y[None,:,:] - self.RFF_Y.output)**2,0)))**0.5
###########################################
self.MMD=self.RFF_Y.MMD_central_penalty(self.Xlabel)*N_tot
def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
nkerns=[48, 64, 96], batch_size=500):
""" Demonstrates lenet on MNIST dataset
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: path to the dataset used for training /testing (MNIST here)
:type nkerns: list of ints
:param nkerns: number of kernels on each layer 这样默认就是第一次20个kernel，第二层50个kernel？
"""
rng = numpy.random.RandomState(23455)
'''
原来的load data
datasets = load_data(dataset)
'''
datasets = loadData()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
print(n_train_batches)
print(n_valid_batches)
if n_test_batches == 0:
n_test_batches = 1
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
ishape = (50, 50) # this is the size of MNIST images
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 50, 50))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
image_shape=(batch_size, 1, 50, 50),
filter_shape=(nkerns[0], 1, 5, 5), poolsize=(2, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], 23, 23),
filter_shape=(nkerns[1], nkerns[0], 3, 3), poolsize=(2, 2))
layer1_3 = LeNetConvPoolLayer(rng, input=layer1.output,
image_shape=(batch_size, nkerns[1], 10, 10),
filter_shape=(nkerns[2], nkerns[1], 3, 3), poolsize=(2, 2))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1_3.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[2] * 4 * 4,
n_out=1000, activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=1000, n_out=58)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
test_model = theano.function([index], layer3.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]})
validate_model = theano.function([index], layer3.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]})
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1_3.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i],grads[i]) pairs.
updates = []
for param_i, grad_i in zip(params, grads):
updates.append((param_i, param_i - learning_rate * grad_i))
train_model = theano.function([index], cost, updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]})
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 1000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i
in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [test_model(i) for i in xrange(n_test_batches)]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
best_params = params
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
saveParams(params)
def train(args,
model_args):
#model_id = '/data/lisatmp4/lambalex/lsun_walkback/walkback_'
model_id = '/data/lisatmp4/anirudhg/cifar_walk_back/walkback_'
model_dir = create_log_dir(args, model_id)
model_id2 = 'logs/walkback_'
model_dir2 = create_log_dir(args, model_id2)
print model_dir
print model_dir2 + '/' + 'log.jsonl.gz'
logger = mimir.Logger(filename=model_dir2 + '/log.jsonl.gz', formatter=None)
# TODO batches_per_epoch should not be hard coded
lrate = args.lr
import sys
sys.setrecursionlimit(10000000)
args, model_args = parse_args()
#trng = RandomStreams(1234)
if args.resume_file is not None:
print "Resuming training from " + args.resume_file
from blocks.scripts import continue_training
continue_training(args.resume_file)
## load the training data
if args.dataset == 'MNIST':
print 'loading MNIST'
from fuel.datasets import MNIST
dataset_train = MNIST(['train'], sources=('features',))
dataset_test = MNIST(['test'], sources=('features',))
n_colors = 1
spatial_width = 28
elif args.dataset == 'CIFAR10':
from fuel.datasets import CIFAR10
dataset_train = CIFAR10(['train'], sources=('features',))
dataset_test = CIFAR10(['test'], sources=('features',))
n_colors = 3
spatial_width = 32
elif args.dataset == "lsun" or args.dataset == "lsunsmall":
print "loading lsun class!"
from load_lsun import load_lsun
print "loading lsun data!"
if args.dataset == "lsunsmall":
dataset_train, dataset_test = load_lsun(args.batch_size, downsample=True)
spatial_width=32
else:
dataset_train, dataset_test = load_lsun(args.batch_size, downsample=False)
spatial_width=64
n_colors = 3
elif args.dataset == "celeba":
print "loading celeba data"
from fuel.datasets.celeba import CelebA
dataset_train = CelebA(which_sets = ['train'], which_format="64", sources=('features',), load_in_memory=False)
dataset_test = CelebA(which_sets = ['test'], which_format="64", sources=('features',), load_in_memory=False)
spatial_width = 64
n_colors = 3
tr_scheme = SequentialScheme(examples=dataset_train.num_examples, batch_size=args.batch_size)
ts_scheme = SequentialScheme(examples=dataset_test.num_examples, batch_size=args.batch_size)
train_stream = DataStream.default_stream(dataset_train, iteration_scheme = tr_scheme)
test_stream = DataStream.default_stream(dataset_test, iteration_scheme = ts_scheme)
dataset_train = train_stream
dataset_test = test_stream
#epoch_it = train_stream.get_epoch_iterator()
elif args.dataset == 'Spiral':
print 'loading SPIRAL'
train_set = Spiral(num_examples=100000, classes=1, cycles=2., noise=0.01,
sources=('features',))
dataset_train = DataStream.default_stream(train_set,
iteration_scheme=ShuffledScheme(
train_set.num_examples, args.batch_size))
else:
raise ValueError("Unknown dataset %s."%args.dataset)
model_options = locals().copy()
if args.dataset != 'lsun' and args.dataset != 'celeba':
train_stream = Flatten(DataStream.default_stream(dataset_train,
iteration_scheme=ShuffledScheme(
examples=dataset_train.num_examples - (dataset_train.num_examples%args.batch_size),
batch_size=args.batch_size)))
else:
train_stream = dataset_train
test_stream = dataset_test
print "Width", WIDTH, spatial_width
shp = next(train_stream.get_epoch_iterator())[0].shape
print "got epoch iterator"
Xbatch = next(train_stream.get_epoch_iterator())[0]
scl = 1./np.sqrt(np.mean((Xbatch-np.mean(Xbatch))**2))
shft = -np.mean(Xbatch*scl)
print 'Building model'
params = init_params(model_options)
if args.reload_:
print "Trying to reload parameters"
if os.path.exists(args.saveto_filename):
print 'Reloading Parameters'
print args.saveto_filename
params = load_params(args.saveto_filename, params)
tparams = init_tparams(params)
print tparams
x, cost, start_temperature, step_chain = build_model(tparams, model_options)
inps = [x.astype('float32'), start_temperature, step_chain]
x_Data = T.matrix('x_Data', dtype='float32')
temperature = T.scalar('temperature', dtype='float32')
step_chain_part = T.scalar('step_chain_part', dtype='int32')
forward_diffusion = one_step_diffusion(x_Data, model_options, tparams, temperature, step_chain_part)
print tparams
grads = T.grad(cost, wrt=itemlist(tparams))
#get_grads = theano.function(inps, grads)
for j in range(0, len(grads)):
grads[j] = T.switch(T.isnan(grads[j]), T.zeros_like(grads[j]), grads[j])
# compile the optimizer, the actual computational graph is compiled here
lr = T.scalar(name='lr')
print 'Building optimizers...',
optimizer = args.optimizer
f_grad_shared, f_update = getattr(optimizers, optimizer)(lr, tparams, grads, inps, cost)
print 'Done'
#for param in tparams:
# print param
# print tparams[param].get_value().shape
print 'Buiding Sampler....'
f_sample = sample(tparams, model_options)
print 'Done'
uidx = 0
estop = False
bad_counter = 0
max_epochs = 4000
batch_index = 1
print 'Number of steps....'
print args.num_steps
print "Number of metasteps...."
print args.meta_steps
print 'Done'
count_sample = 1
for eidx in xrange(max_epochs):
n_samples = 0
print 'Starting Next Epoch ', eidx
for data in train_stream.get_epoch_iterator():
if args.dataset == 'CIFAR10':
if data[0].shape[0] == args.batch_size:
data_use = (data[0].reshape(args.batch_size,3*32*32),)
else:
continue
t0 = time.time()
batch_index += 1
n_samples += len(data_use[0])
uidx += 1
if data_use[0] is None:
print 'No data '
uidx -= 1
continue
ud_start = time.time()
t1 = time.time()
data_run = data_use[0]
temperature_forward = args.temperature
meta_cost = []
for meta_step in range(0, args.meta_steps):
data_run = data_run.astype('float32')
meta_cost.append(f_grad_shared(data_run, temperature_forward, meta_step))
f_update(lrate)
if args.meta_steps > 1:
data_run, sigma, _, _ = forward_diffusion(data_run, temperature_forward, meta_step)
temperature_forward *= args.temperature_factor
cost = sum(meta_cost) / len(meta_cost)
ud = time.time() - ud_start
#gradient_updates_ = get_grads(data_use[0],args.temperature)
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
return 1.
logger.log({'epoch': eidx,
'batch_index': batch_index,
'uidx': uidx,
'training_error': cost})
if batch_index%20==0:
print batch_index, "cost", cost
if batch_index%1000==0:
print 'saving params'
params = unzip(tparams)
save_params(params, model_dir + '/' + 'params_' + str(batch_index) + '.npz')
if batch_index%200==0:
count_sample += 1
'''
temperature = args.temperature * (args.temperature_factor ** (args.num_steps*args.meta_steps -1 ))
temperature_forward = args.temperature
for num_step in range(args.num_steps * args.meta_steps):
print "Forward temperature", temperature_forward
if num_step == 0:
x_data, sampled, sampled_activation, sampled_preactivation = forward_diffusion(data[0].astype('float32'), temperature_forward, num_step)
x_data = np.asarray(x_data).astype('float32').reshape(args.batch_size, INPUT_SIZE)
x_temp = x_data.reshape(args.batch_size, n_colors, WIDTH, WIDTH)
plot_images(x_temp, model_dir + '/' + "batch_" + str(batch_index) + '_corrupted' + 'epoch_' + str(count_sample) + '_time_step_' + str(num_step))
else:
x_data, sampled, sampled_activation, sampled_preactivation = forward_diffusion(x_data.astype('float32'), temperature_forward, num_step)
x_data = np.asarray(x_data).astype('float32').reshape(args.batch_size, INPUT_SIZE)
x_temp = x_data.reshape(args.batch_size, n_colors, WIDTH, WIDTH)
plot_images(x_temp, model_dir + '/batch_' + str(batch_index) + '_corrupted' + '_epoch_' + str(count_sample) + '_time_step_' + str(num_step))
temperature_forward = temperature_forward * args.temperature_factor;
x_temp2 = data_use[0].reshape(args.batch_size, n_colors, WIDTH, WIDTH)
plot_images(x_temp2, model_dir + '/' + 'orig_' + 'epoch_' + str(eidx) + '_batch_index_' + str(batch_index))
temperature = args.temperature * (args.temperature_factor ** (args.num_steps*args.meta_steps - 1 ))
for i in range(args.num_steps*args.meta_steps + args.extra_steps):
x_data, sampled, sampled_activation, sampled_preactivation = f_sample(x_data.astype('float32'), temperature, args.num_steps*args.meta_steps -i - 1)
print 'On backward step number, using temperature', i, temperature
reverse_time(scl, shft, x_data, model_dir + '/'+ "batch_" + str(batch_index) + '_samples_backward_' + 'epoch_' + str(count_sample) + '_time_step_' + str(i))
x_data = np.asarray(x_data).astype('float32')
x_data = x_data.reshape(args.batch_size, INPUT_SIZE)
if temperature == args.temperature:
temperature = temperature
else:
temperature /= args.temperature_factor
'''
if args.noise == "gaussian":
x_sampled = np.random.normal(0.5, 2.0, size=(args.batch_size,INPUT_SIZE)).clip(0.0, 1.0)
else:
s = np.random.binomial(1, 0.5, INPUT_SIZE)
temperature = args.temperature * (args.temperature_factor ** (args.num_steps*args.meta_steps - 1))
x_data = np.asarray(x_sampled).astype('float32')
for i in range(args.num_steps*args.meta_steps + args.extra_steps):
x_data, sampled, sampled_activation, sampled_preactivation = f_sample(x_data.astype('float32'), temperature, args.num_steps*args.meta_steps -i - 1)
print 'On step number, using temperature', i, temperature
reverse_time(scl, shft, x_data, model_dir + '/batch_index_' + str(batch_index) + '_inference_' + 'epoch_' + str(count_sample) + '_step_' + str(i))
x_data = np.asarray(x_data).astype('float32')
x_data = x_data.reshape(args.batch_size, INPUT_SIZE)
if temperature == args.temperature:
temperature = temperature
else:
temperature /= args.temperature_factor
ipdb.set_trace()
import theano import theano.tensor as T from load import mnist import numpy as np from costs import categorical_crossentropy from updates import Adadelta batch_size = 128 X = T.matrix() Y = T.matrix() n_in = 28*28 n_hidden = 512 n_out = 10 w_in = theano.shared(floatX(np.random.randn(n_in,n_hidden)*0.01)) w_out = theano.shared(floatX(np.random.randn(n_hidden,n_out)*0.01)) b_in = theano.shared(floatX(np.zeros((n_hidden)))) b_out = theano.shared(floatX(np.zeros((n_out)))) def model(X): h = T.tanh(T.dot(X,w_in)+b_in) y = T.nnet.softmax(T.dot(h,w_out)+b_out) return y out = model(X) err = categorical_crossentropy(Y,out) params = [w_in,b_in,w_out,b_out] grads = T.grad(err,params) updates = Adadelta(params,grads) train = theano.function([X,Y],err,updates=updates)
def main():
parser = build_parser()
args = parser.parse_args()
np.random.seed(args.seed)
trng = RandomStreams(args.seed)
rng = np.random.RandomState(args.seed + 1)
model_file = args.model_prefix + "_pars.npz"
model_opts = args.model_prefix + "_opts.pkl"
model_options = pkl.load(open(model_opts, 'rb'))
# Load data
data = IMDB_JMARS("./experiments/data",
seq_len=16,
batch_size=args.nb_samples,
topk=16000)
model_options["dim_input"] = data.voc_size
for num, (x, y, x_mask) in enumerate(data.get_valid_batch()):
data.print_batch(x)
break
params = init_params(model_options)
print('Loading model parameters...')
params = load_params(model_file, params)
tparams = init_tparams(params)
x = T.lmatrix('x')
y = T.lmatrix('y')
x_mask = T.matrix('x_mask')
# Debug test_value
x.tag.test_value = np.random.rand(11, 20).astype("int64")
y.tag.test_value = np.random.rand(11, 20).astype("int64")
x_mask.tag.test_value = np.ones((11, 20)).astype("float32")
is_train.tag.test_value = np.float32(0.)
zmuv = T.tensor3('zmuv')
zmuv.tag.test_value = np.ones(
(11, 20, model_options['dim_z'])).astype("float32")
# build the symbolic computational graph
nll_rev, states_rev, updates_rev = \
build_rev_model(tparams, model_options, x, y, x_mask)
nll_gen, states_gen, kld, rec_cost_rev, updates_gen, \
log_pxIz, log_pz, log_qzIx, z, _ = \
build_gen_model(tparams, model_options, x, y, x_mask, zmuv, states_rev)
# Build sampler
f_next = build_sampler(tparams, model_options, trng, provide_z=True)
# Build inference
get_latents = theano.function([x, y, x_mask, zmuv],
z,
updates=(updates_gen + updates_rev),
givens={is_train: np.float32(0.)})
while True:
s1 = raw_input("s1:").strip().split()
s2 = raw_input("s2:").strip().split()
s1_id = [data.word2idx.get(word, data.unk_id) for word in s1]
s2_id = [data.word2idx.get(word, data.unk_id) for word in s2]
batch = data.prepare_batch([s1_id, s2_id])
data.print_batch(batch[0])
zmuv = rng.normal(loc=0.0,
scale=1.0,
size=(batch[0].shape[1], 2,
model_options['dim_z'])).astype('float32')
batch_z = get_latents(batch[0].T, batch[1].T, batch[2].T, zmuv)
z1 = batch_z[:, [0], :]
z2 = batch_z[:, [1], :]
print("Beam Search")
data.print_batch(batch[0][[0]], eos_id=data.eos_id, print_number=False)
for i in np.linspace(0, 1, 11):
print("{}: ".format(i), end="")
z = ((1 - i) * z1) + (i * z2) # Interpolate latent
z = np.repeat(z, 10, axis=1)
sample, sample_score = beam_sample(tparams,
f_next,
model_options,
maxlen=20,
zmuv=z,
unk_id=data.unk_id,
eos_id=data.eos_id,
bos_id=data.bos_id)
sample = [sample[0]]
data.print_batch(sample, eos_id=data.eos_id, print_number=False)
data.print_batch(batch[0][[1]], eos_id=data.eos_id, print_number=False)
# Interpolation
print("Samples")
data.print_batch(batch[0][[0]], eos_id=data.eos_id, print_number=False)
for i in np.linspace(0, 1, 11):
print("{}: ".format(i), end="")
z = ((1 - i) * z1) + (i * z2) # Interpolate latent
z = np.repeat(z, 10, axis=1)
sample, sample_score = gen_sample(tparams,
f_next,
model_options,
maxlen=20,
argmax=False,
zmuv=z,
unk_id=data.unk_id,
eos_id=data.eos_id,
bos_id=data.bos_id)
sample = [sample.T[np.argsort(sample_score)[-1]]]
data.print_batch(sample, eos_id=data.eos_id, print_number=False)
data.print_batch(batch[0][[1]], eos_id=data.eos_id, print_number=False)
print("Argmax")
data.print_batch(batch[0][[0]], eos_id=data.eos_id, print_number=False)
for i in np.linspace(0, 1, 11):
print("{}: ".format(i), end="")
z = ((1 - i) * z1) + (i * z2) # Interpolate latent
sample, sample_score = gen_sample(tparams,
f_next,
model_options,
maxlen=20,
argmax=True,
zmuv=z,
unk_id=data.unk_id,
eos_id=data.eos_id,
bos_id=data.bos_id)
data.print_batch(sample.T, eos_id=data.eos_id, print_number=False)
data.print_batch(batch[0][[1]], eos_id=data.eos_id, print_number=False)
raw_input("-- Next --")
sys.exit(0)
cost = T.mean((self.x - self.z)**2)
gparams = T.grad(cost, self.params)
updates = [(param, param - learning_rate * gparam)
for param, gparam in zip(self.params, gparams)]
# updates = gradient_updates_momentum(cost, self.params)
return (cost, updates)
print 'nTrials x nFeatures ', np.shape(X_eeg)
print 'Target vector ', np.shape(y_eeg)
print 'Total number of subjects: ', subject_count
""" Generate symbolic variables for input (X and y
represent a minibatch)
"""
X = T.matrix('X') # 2100 x 60 data
y = T.vector('y') # labels, presented as 1D vector of [int] labels
""" Construct the logistic regression class """
rng = np.random.RandomState(1234)
n_hidden = 50
n_visible = np.shape(X_eeg)[1]
da = dA(numpy_rng=rng, input=X, n_visible=n_visible, n_hidden=n_hidden)
cost, updates = da.get_cost_updates(corruption_level=0.2, learning_rate=0.01)
train = theano.function(inputs=[X],
outputs=cost,
updates=updates,
allow_input_downcast=True)
predict = theano.function(inputs=[X], outputs=da.z)
""" Leave One Out """
sys.path.insert(1, os.path.join(base_path, '../../common'))
sys.path.insert(2, os.path.join(base_path, '../../database'))
sys.path.insert(1, os.path.join(base_path, '../'))
from db import DB
from project import Project
from performance import Performance
from data import Data
from cnn import CNN
if __name__ == '__main__':
# load the model to use for performance evaluation
x = T.matrix('x')
rng = numpy.random.RandomState(1234)
# retrieve the project settings from the database
project = DB.getProject('evalcnn')
# create the model based on the project
model = CNN(rng=rng,
input=x,
offline=True,
batch_size=project.batchSize,
patch_size=project.patchSize,
nkerns=project.nKernels,
kernel_sizes=project.kernelSizes,
hidden_sizes=project.hiddenUnits,
def make_matrix():
"""
Returns a new Theano matrix.
"""
return T.matrix()
def train_conv_net(datasets,
U,
img_w=300,
filter_hs=[3, 4, 5],
hidden_units=[100, 2],
dropout_rate=[0.5],
shuffle_batch=True,
n_epochs=25,
batch_size=50,
lr_decay=0.95,
conv_non_linear="relu",
activations=[Iden],
sqr_norm_lim=9,
non_static=True):
"""
Train a simple conv net
img_h = sentence length (padded where necessary)
img_w = word vector length (300 for word2vec)
filter_hs = filter window sizes
hidden_units = [x,y] x is the number of feature maps (per filter window), and y is the penultimate layer
sqr_norm_lim = s^2 in the paper
lr_decay = adadelta decay parameter
"""
rng = np.random.RandomState(3435)
img_h = len(datasets[0][0]) - 1
filter_w = img_w
feature_maps = hidden_units[0]
filter_shapes = []
pool_sizes = []
for filter_h in filter_hs:
filter_shapes.append((feature_maps, 1, filter_h, filter_w))
pool_sizes.append((img_h - filter_h + 1, img_w - filter_w + 1))
parameters = [("image shape", img_h, img_w),
("filter shape", filter_shapes),
("hidden_units", hidden_units), ("dropout", dropout_rate),
("batch_size", batch_size), ("non_static", non_static),
("learn_decay", lr_decay),
("conv_non_linear", conv_non_linear),
("non_static", non_static), ("sqr_norm_lim", sqr_norm_lim),
("shuffle_batch", shuffle_batch)]
print parameters
#define model architecture
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
Words = theano.shared(value=U, name="Words")
zero_vec_tensor = T.vector()
zero_vec = np.zeros(img_w)
set_zero = theano.function([zero_vec_tensor],
updates=[
(Words,
T.set_subtensor(Words[0, :],
zero_vec_tensor))
],
allow_input_downcast=True)
layer0_input = Words[T.cast(x.flatten(), dtype="int32")].reshape(
(x.shape[0], 1, x.shape[1], Words.shape[1]))
conv_layers = []
layer1_inputs = []
for i in xrange(len(filter_hs)):
filter_shape = filter_shapes[i]
pool_size = pool_sizes[i]
conv_layer = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, img_h,
img_w),
filter_shape=filter_shape,
poolsize=pool_size,
non_linear=conv_non_linear)
layer1_input = conv_layer.output.flatten(2)
conv_layers.append(conv_layer)
layer1_inputs.append(layer1_input)
layer1_input = T.concatenate(layer1_inputs, 1)
hidden_units[0] = feature_maps * len(filter_hs)
classifier = MLPDropout(rng,
input=layer1_input,
layer_sizes=hidden_units,
activations=activations,
dropout_rates=dropout_rate)
#define parameters of the model and update functions using adadelta
params = classifier.params
for conv_layer in conv_layers:
params += conv_layer.params
if non_static:
#if word vectors are allowed to change, add them as model parameters
params += [Words]
cost = classifier.negative_log_likelihood(y)
dropout_cost = classifier.dropout_negative_log_likelihood(y)
grad_updates = sgd_updates_adadelta(params, dropout_cost, lr_decay, 1e-6,
sqr_norm_lim)
#shuffle dataset and assign to mini batches. if dataset size is not a multiple of mini batches, replicate
#extra data (at random)
np.random.seed(3435)
if datasets[0].shape[0] % batch_size > 0:
extra_data_num = batch_size - datasets[0].shape[0] % batch_size
train_set = np.random.permutation(datasets[0])
extra_data = train_set[:extra_data_num]
new_data = np.append(datasets[0], extra_data, axis=0)
else:
new_data = datasets[0]
new_data = np.random.permutation(new_data)
n_batches = new_data.shape[0] / batch_size
n_train_batches = int(np.round(n_batches * 0.9))
#divide train set into train/val sets
test_set_x = datasets[1][:, :img_h]
test_set_y = np.asarray(datasets[1][:, -1], "int32")
train_set = new_data[:n_train_batches * batch_size, :]
val_set = new_data[n_train_batches * batch_size:, :]
train_set_x, train_set_y = shared_dataset(
(train_set[:, :img_h], train_set[:, -1]))
val_set_x, val_set_y = shared_dataset((val_set[:, :img_h], val_set[:, -1]))
n_val_batches = n_batches - n_train_batches
val_model = theano.function(
[index],
classifier.errors(y),
givens={
x: val_set_x[index * batch_size:(index + 1) * batch_size],
y: val_set_y[index * batch_size:(index + 1) * batch_size]
},
allow_input_downcast=True)
#compile theano functions to get train/val/test errors
test_model = theano.function(
[index],
classifier.errors(y),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
},
allow_input_downcast=True)
train_model = theano.function(
[index],
cost,
updates=grad_updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
},
allow_input_downcast=True)
test_pred_layers = []
test_size = test_set_x.shape[0]
test_layer0_input = Words[T.cast(x.flatten(), dtype="int32")].reshape(
(test_size, 1, img_h, Words.shape[1]))
for conv_layer in conv_layers:
test_layer0_output = conv_layer.predict(test_layer0_input, test_size)
test_pred_layers.append(test_layer0_output.flatten(2))
test_layer1_input = T.concatenate(test_pred_layers, 1)
test_y_pred = classifier.predict(test_layer1_input)
test_error = T.mean(T.neq(test_y_pred, y))
test_model_all = theano.function([x, y],
test_error,
allow_input_downcast=True)
#start training over mini-batches
print '... training'
epoch = 0
best_val_perf = 0
val_perf = 0
test_perf = 0
cost_epoch = 0
while (epoch < n_epochs):
start_time = time.time()
epoch = epoch + 1
if shuffle_batch:
for minibatch_index in np.random.permutation(
range(n_train_batches)):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
else:
for minibatch_index in xrange(n_train_batches):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
train_losses = [test_model(i) for i in xrange(n_train_batches)]
train_perf = 1 - np.mean(train_losses)
val_losses = [val_model(i) for i in xrange(n_val_batches)]
val_perf = 1 - np.mean(val_losses)
print(
'epoch: %i, training time: %.2f secs, train perf: %.2f %%, val perf: %.2f %%'
% (epoch, time.time() - start_time, train_perf * 100.,
val_perf * 100.))
if val_perf >= best_val_perf:
best_val_perf = val_perf
test_loss = test_model_all(test_set_x, test_set_y)
test_perf = 1 - test_loss
return test_perf
