def getProb(bestModel, dataset, probFilename, P):
print "...getting probability"
setX, setY, setName = dataset
sharedSetX, sharedSetY, castSharedSetY = dnnUtils.sharedDataXY(setX, setY)
idx = T.ivector('i')
sX = T.matrix(dtype=theano.config.floatX)
sY = T.ivector()
# bulid best DNN model
predicter = DNN( input = dnnUtils.splicedX(sX, idx, P.spliceWidth), P = P, params = bestModel )
# Validation model
Model = theano.function( inputs = [idx], outputs = predicter.p_y_given_x,
givens={sX:sharedSetX, sY:castSharedSetY}, on_unused_input='ignore')
# Center Index
centerIdx = dnnUtils.findCenterIdxList(setY)
# Total Center Index
totalCenterIdxSize = len(centerIdx)
# Make mini-Batch
batchIdx = dnnUtils.makeBatch(totalCenterIdxSize, 16384)
# Writing Probability
dnnUtils.writeProb(Model, batchIdx, centerIdx, setName, probFilename)
dnnUtils.clearSharedDataXY(sharedSetX, sharedSetY)
def __init__(self, vocab_size, dim, lr=0.5):
W = np.asarray(np.random.rand(vocab_size, dim),
dtype=theano.config.floatX) / float(dim)
W1 = np.asarray((np.random.rand(vocab_size, dim)),
dtype=theano.config.floatX) / float(dim)
self.W = theano.shared(W, name='W', borrow=True)
self.W1 = theano.shared(W1, name='W1', borrow=True)
gW = np.asarray(np.ones((vocab_size, dim)), dtype=theano.config.floatX)
gW1 = np.asarray(
np.ones((vocab_size, dim)), dtype=theano.config.floatX)
self.gW = theano.shared(gW, name='gW', borrow=True)
self.gW1 = theano.shared(gW1, name='gW1', borrow=True)
X = T.vector()
fX = T.vector()
ind_W = T.ivector()
ind_W1 = T.ivector()
w = self.W[ind_W, :]
w1 = self.W1[ind_W1, :]
cost = T.sum(fX * ((T.sum(w * w1, axis=1) - X) ** 2))
grad = T.clip(T.grad(cost, [w, w1]), -5.0, 5.0)
updates1 = [(self.gW, T.inc_subtensor(self.gW[ind_W, :],
grad[0] ** 2))]
updates2 = [(self.gW1, T.inc_subtensor(self.gW1[ind_W1, :],
grad[1] ** 2))]
updates3 = [(self.W, T.inc_subtensor(self.W[ind_W, :],
- (lr / T.sqrt(self.gW[ind_W, :])) *
grad[0]))]
updates4 = [(self.W1, T.inc_subtensor(self.W1[ind_W1, :],
- (lr / T.sqrt(self.gW1[ind_W1, :])) *
grad[1]))]
updates = updates1 + updates2 + updates3 + updates4
self.cost_fn = theano.function(
inputs=[ind_W, ind_W1, X, fX], outputs=cost, updates=updates)
def test_CSMGrad(self):
imshp = (3, 3)
nkern = 1 # per output pixel
kshp = (2, 2)
# ssizes = ((1,1),(2,2))
ssizes = ((1, 1),)
# convmodes = ('full','valid',)
convmodes = ("full",)
kerns = tensor.dvector()
indices = tensor.ivector()
indptr = tensor.ivector()
spmat_shape = tensor.ivector()
for mode in ["FAST_COMPILE", "FAST_RUN"]:
for conv_mode in convmodes:
for ss in ssizes:
indvals, indptrvals, spshapevals, sptype, outshp, kmap = sp.convolution_indices.sparse_eval(
imshp, kshp, nkern, ss, conv_mode
)
kvals = numpy.random.random(nkern * numpy.prod(kshp) * numpy.prod(outshp)).flatten()
def d(kerns):
return theano.sparse.dense_from_sparse(
theano.sparse.CSM(sptype, kmap)(kerns, indvals, indptrvals, spshapevals)
)
# symbolic stuff
utt.verify_grad(d, [kvals])
def predict_next_batch(self, session_ids, input_item_ids, predict_for_item_ids=None, batch=100):
'''
Gives predicton scores for a selected set of items. Can be used in batch mode to predict for multiple independent events (i.e. events of different sessions) at once and thus speed up evaluation.
If the session ID at a given coordinate of the session_ids parameter remains the same during subsequent calls of the function, the corresponding hidden state of the network will be kept intact (i.e. that's how one can predict an item to a session).
If it changes, the hidden state of the network is reset to zeros.
Parameters
--------
session_ids : 1D array
Contains the session IDs of the events of the batch. Its length must equal to the prediction batch size (batch param).
input_item_ids : 1D array
Contains the item IDs of the events of the batch. Every item ID must be must be in the training data of the network. Its length must equal to the prediction batch size (batch param).
predict_for_item_ids : 1D array (optional)
IDs of items for which the network should give prediction scores. Every ID must be in the training set. The default value is None, which means that the network gives prediction on its every output (i.e. for all items in the training set).
batch : int
Prediction batch size.
Returns
--------
out : pandas.DataFrame
Prediction scores for selected items for every event of the batch.
Columns: events of the batch; rows: items. Rows are indexed by the item IDs.
'''
if self.error_during_train: raise Exception
if self.predict is None or self.predict_batch!=batch:
X = T.ivector()
Y = T.ivector()
for i in range(len(self.layers)):
self.H[i].set_value(np.zeros((batch,self.layers[i]), dtype=theano.config.floatX), borrow=True)
if predict_for_item_ids is not None:
H_new, yhat, _ = self.model(X, self.H, Y, 0)
else:
H_new, yhat = self.model_test(X, self.H)
updatesH = OrderedDict()
for i in range(len(self.H)):
updatesH[self.H[i]] = H_new[i]
if predict_for_item_ids is not None:
self.predict = function(inputs=[X, Y], outputs=yhat, updates=updatesH, allow_input_downcast=True)
else:
self.predict = function(inputs=[X], outputs=yhat, updates=updatesH, allow_input_downcast=True)
self.current_session = np.ones(batch) * -1
self.predict_batch = batch
session_change = np.arange(batch)[session_ids != self.current_session]
if len(session_change) > 0:
for i in range(len(self.H)):
tmp = self.H[i].get_value(borrow=True)
tmp[session_change] = 0
self.H[i].set_value(tmp, borrow=True)
self.current_session=session_ids.copy()
in_idxs = self.itemidmap[input_item_ids]
if predict_for_item_ids is not None:
iIdxs = self.itemidmap[predict_for_item_ids]
preds = np.asarray(self.predict(in_idxs, iIdxs)).T
return pd.DataFrame(data=preds, index=predict_for_item_ids)
else:
in_idxs.values[np.isnan(in_idxs.values)] = 0
preds = np.asarray(self.predict(in_idxs)).T
return pd.DataFrame(data=preds, index=self.itemidmap.index)
def bsgd1(nn, data, name='sgd', lr=0.022, alpha=0.3, batch_size=500, epochs = 10):
train_set_x, train_set_y = data[0]
valid_set_x, valid_set_y = data[1]
test_set_x, test_set_y = data[2]
# valid_y_numpy = y_numpy[0]
# test_y_numpy = y_numpy[1]
test_y_numpy = map_48_to_39(test_y_numpy)
valid_y_numpy = map_48_to_39(valid_y_numpy)
print test_y_numpy
num_samples = train_set_x.get_value(borrow=True).shape[0]
num_batches = num_samples / batch_size
layers = nn.layers
x = T.matrix('x')
y = T.ivector('y')
y_eval = T.ivector('y_eval')
cost = nn.cost(x, y)
accuracy = nn.calcAccuracy(x, y)
params = nn.params
delta_params = nn.delta_params
print theano.pp(cost)
# theano.pp(accuracy)
p_grads = [T.grad(cost=cost, wrt = p) for p in params]
# implementing gradient descent with momentum
print p_grads
updates = OrderedDict()
for dp, gp in zip(delta_params, p_grads):
updates[dp] = dp*alpha - gp*lr
for p, dp in zip(params, delta_params):
updates[p] = p + updates[dp]
# updates = [(p, p - lr*gp) for p, gp in zip(params, p_grads)]
index = T.ivector('index')
batch_sgd_train = theano.function(inputs=[index], outputs=[cost, accuracy], updates=updates, givens={x: train_set_x[index], y:train_set_y[index]})
batch_sgd_valid = theano.function(inputs=[], outputs=[nn.calcAccuracy(x, y), nn.calcAccuracyTimit(x,y)], givens={x: valid_set_x, y:valid_set_y})
batch_sgd_test = theano.function(inputs=[], outputs=nn.calcAccuracy(x, y), givens={x: test_set_x, y:test_set_y})
indices = np.arange(num_samples, dtype=np.dtype('int32'))
np.random.shuffle(indices)
for n in xrange(epochs):
np.random.shuffle(indices)
for i in xrange(num_batches):
batch = indices[i*batch_size: (i+1)*batch_size]
batch_sgd_train(batch)
# y_np = y.get_value()
# print y.eval()
print "epoch:", n, " validation accuracy:", batch_sgd_valid()
print batch_sgd_test()
def create_TrainFunc_tranPES(simfn, embeddings, marge=0.5, alpha=1., beta=1.):
# parse the embedding data
embedding = embeddings[0] # D x N matrix
lembedding = embeddings[1]
# declare the symbolic variables for training triples
hp = S.csr_matrix('head positive') # N x batchsize matrix
rp = S.csr_matrix('relation')
tp = S.csr_matrix('tail positive')
hn = S.csr_matrix('head negative')
tn = S.csr_matrix('tail negative')
lemb = T.scalar('embedding learning rate')
lremb = T.scalar('relation learning rate')
subtensorE = T.ivector('batch entities set')
subtensorR = T.ivector('batch link set')
# Generate the training positive and negative triples
hpmat = S.dot(embedding.E, hp).T # batchsize x D dense matrix
rpmat = S.dot(lembedding.E, rp).T
tpmat = S.dot(embedding.E, tp).T
hnmat = S.dot(embedding.E, hn).T
tnmat = S.dot(embedding.E, tn).T
# calculate the score
pos = tranPES3(simfn, T.concatenate([hpmat, tpmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tpmat)
negh = tranPES3(simfn, T.concatenate([hnmat, tpmat], axis=1).reshape((hnmat.shape[0], 2, hnmat.shape[1])).dimshuffle(0, 2, 1), hnmat, rpmat, tpmat)
negt = tranPES3(simfn, T.concatenate([hpmat, tnmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tnmat)
costh, outh = margeCost(pos, negh, marge)
costt, outt = margeCost(pos, negt, marge)
embreg = regEmb(embedding, subtensorE, alpha)
lembreg = regLink(lembedding, subtensorR, beta)
cost = costh + costt + embreg[0] + lembreg
out = T.concatenate([outh, outt])
outc = embreg[1]
# list of inputs to the function
list_in = [lemb, lremb, hp, rp, tp, hn, tn, subtensorE, subtensorR]
# updating the embeddings using gradient descend
emb_grad = T.grad(cost, embedding.E)
New_embedding = embedding.E - lemb*emb_grad
remb_grad = T.grad(cost, lembedding.E)
New_rembedding = lembedding.E - lremb * remb_grad
updates = OrderedDict({embedding.E: New_embedding, lembedding.E: New_rembedding})
return theano.function(list_in, [cost, T.mean(out), T.mean(outc), embreg[0], lembreg],
updates=updates, on_unused_input='ignore')
def test_multMatVect():
A1 = tensor.lmatrix('A1')
s1 = tensor.ivector('s1')
m1 = tensor.iscalar('m1')
A2 = tensor.lmatrix('A2')
s2 = tensor.ivector('s2')
m2 = tensor.iscalar('m2')
g0 = rng_mrg.DotModulo()(A1, s1, m1, A2, s2, m2)
f0 = theano.function([A1, s1, m1, A2, s2, m2], g0)
i32max = numpy.iinfo(numpy.int32).max
A1 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s1 = numpy.random.randint(0, i32max, 3).astype('int32')
m1 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
A2 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s2 = numpy.random.randint(0, i32max, 3).astype('int32')
m2 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
f0.input_storage[0].storage[0] = A1
f0.input_storage[1].storage[0] = s1
f0.input_storage[2].storage[0] = m1
f0.input_storage[3].storage[0] = A2
f0.input_storage[4].storage[0] = s2
f0.input_storage[5].storage[0] = m2
r_a1 = rng_mrg.matVecModM(A1, s1, m1)
r_a2 = rng_mrg.matVecModM(A2, s2, m2)
f0.fn()
r_b = f0.output_storage[0].value
assert numpy.allclose(r_a1, r_b[:3])
assert numpy.allclose(r_a2, r_b[3:])
def directRNN():
####################### NumPy
x0=0.5
s=0.5
times=[1,10,20,30,40,50]
yhat=direct(x0, s, times)
############################### Symbolic
x0_ = T.scalar("x0")
c_= T.log((1-x0_)/x0_)
times_ = T.ivector("times")
S__=theano.shared(np.asarray(s, dtype = theano.config.floatX), 'S')
yhat_= T.nnet.sigmoid(S__*times_/2-c_)
Predict_ = theano.function(inputs=[x0_,times_], outputs=yhat_)
############################### Symbolic Recursive
x0_ = T.scalar("x0")
times_ = T.ivector("times")
S__=theano.shared(np.asarray(s, dtype = theano.config.floatX), 'S')
# predall_, updatesRecurrence_ = theano.scan(lambda x_prev, s: (s*x_prev*x_prev+s*x_prev +2*x_prev)/(2*s*x_prev+2), outputs_info=x0_,non_sequences=S__,n_steps=times_[-1])
predall_, updatesRecurrence_ = theano.scan(lambda x_prev, s: x_prev+(s*x_prev*(1-x_prev))/(2*s*x_prev+2), outputs_info=x0_,non_sequences=S__,n_steps=times_[-1])
pred_=predall_[times_-1] #we only have target at some generations e.g. 10,20,...
Feedforward_ = theano.function(inputs=[x0_,times_], outputs=pred_, updates=updatesRecurrence_)
############################# Comparison
x_0=0.5
x_1=x_0+(s*x_0*(1-x_0))/(2*s*x_0+2)
print '{:20s}{}'.format('NumPy', yhat)
print '{:20s}{}'.format('Symbolic Direct', Predict_(x0,list(times)))
print '{:20s}{}'.format('Symbolic Recursive', Feedforward_(x0,list(times)))
print '{:20s}[ {} ]'.format('x_1', x_1)
def multMatVect(v, A, m1, B, m2):
"""
multiply the first half of v by A with a modulo of m1
and the second half by B with a modulo of m2
Note: The parameters of dot_modulo are passed implicitly because passing
them explicitly takes more time then running the function's C-code.
"""
if multMatVect.dot_modulo is None:
A_sym = tensor.lmatrix("A")
s_sym = tensor.ivector("s")
m_sym = tensor.iscalar("m")
A2_sym = tensor.lmatrix("A2")
s2_sym = tensor.ivector("s2")
m2_sym = tensor.iscalar("m2")
o = DotModulo()(A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym)
multMatVect.dot_modulo = function([A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym], o)
# This way of calling the Theano fct is done to bypass Theano overhead.
f = multMatVect.dot_modulo
f.input_storage[0].storage[0] = A
f.input_storage[1].storage[0] = v[:3]
f.input_storage[2].storage[0] = m1
f.input_storage[3].storage[0] = B
f.input_storage[4].storage[0] = v[3:]
f.input_storage[5].storage[0] = m2
f.fn()
r = f.output_storage[0].storage[0]
return r
def __init__(self, numpy_rng, theano_rng=None, y=None,
alpha=0.9, sample_rate=0.1, n_ins=784,
hidden_layers_sizes=[500, 500], n_outs=10,
corruption_levels=[0.1, 0.1],
allX=None,allY=None,srng=None):
self.sigmoid_layers = []
self.sugar_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
self.allXs = []
if y == None:
self.y = tensor.ivector(name='y')
else:
self.y = y
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
self.x = tensor.matrix('x')
self.x = tensor.matrix('x')
self.y = tensor.ivector('y')
self.y = tensor.ivector('y')
for i in xrange(self.n_layers):
if i == 0:
input_size = n_ins
else:
input_size = hidden_layers_sizes[i - 1]
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[-1].output
if i == 0:
self.allXs.append(allX)
else:
self.allXs.append(tensor.dot(self.allXs[i-1], self.sigmoid_layers[-1].W) + self.sigmoid_layers[-1].b)
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=tensor.nnet.sigmoid)
self.sigmoid_layers.append(sigmoid_layer)
self.params.extend(sigmoid_layer.params)
sugar_layer = sugar(numpy_rng=numpy_rng,
alpha=alpha,
sample_rate=sample_rate,
x=layer_input,
y=self.y,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
W=sigmoid_layer.W,
bhid=sigmoid_layer.b,
allX=self.allXs[i],
allY=allY,
srng=srng)
self.sugar_layers.append(sugar_layer)
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)
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
def __init__(self, dnodex,inputdim,dim):
X=T.ivector()
Y=T.ivector()
Z=T.lscalar()
eta = T.scalar()
temperature=T.scalar()
self.dnodex=dnodex
num_input = inputdim
dnodex.umatrix=theano.shared(floatX(np.random.randn(*(self.dnodex.nuser,inputdim, inputdim))))
dnodex.pmatrix=theano.shared(floatX(np.random.randn(*(self.dnodex.npoi,inputdim))))
dnodex.p_l2_norm=(dnodex.pmatrix**2).sum()
dnodex.u_l2_norm=(dnodex.umatrix**2).sum()
num_hidden = dim
num_output = inputdim
inputs = InputPLayer(dnodex.pmatrix[X,:], dnodex.umatrix[Z,:,:], 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 = SoftmaxPLayer(num_hidden, num_output, dnodex.umatrix[Z,:,:], input_layer=lstm3, name="yhat", temperature=temperature)
Y_hat = softmax.output()
self.layers = inputs, lstm1,lstm2,lstm3,softmax
params = get_params(self.layers)
#caches = make_caches(params)
cost = T.mean(T.nnet.categorical_crossentropy(Y_hat, T.dot(dnodex.pmatrix[Y,:],dnodex.umatrix[Z,:,:])))+eta*dnodex.p_l2_norm+eta*dnodex.u_l2_norm
updates = PerSGD(cost,params,eta,X,Z,dnodex)#momentum(cost, params, caches, eta)
self.train = theano.function([X,Y,Z, eta, temperature], cost, updates=updates, allow_input_downcast=True)
predict_updates = one_step_updates(self.layers)
self.predict_char = theano.function([X, Z, temperature], Y_hat, updates=predict_updates, allow_input_downcast=True)
def __theano_build__(self):
params = self.params
param_names = self.param_names
hidden_dim = self.hidden_dim
x1 = T.imatrix('x1') # first sentence
x2 = T.imatrix('x2') # second sentence
x1_mask = T.fmatrix('x1_mask') #mask
x2_mask = T.fmatrix('x2_mask')
y = T.ivector('y') # label
y_c = T.ivector('y_c') # class weights
# Embdding words
_E1 = params["E"].dot(params["W"][0]) + params["B"][0]
_E2 = params["E"].dot(params["W"][1]) + params["B"][1]
statex1 = _E1[x1.flatten(), :].reshape([x1.shape[0], x1.shape[1], hidden_dim])
statex2 = _E2[x2.flatten(), :].reshape([x2.shape[0], x2.shape[1], hidden_dim])
def rnn_cell(x, mx, ph, Wh):
h = T.tanh(ph.dot(Wh) + x)
h = mx[:, None] * h + (1-mx[:, None]) * ph
return [h]
[h1], updates = theano.scan(
fn=rnn_cell,
sequences=[statex1, x1_mask],
truncate_gradient=self.truncate,
outputs_info=[dict(initial=T.zeros([self.batch_size, self.hidden_dim]))],
non_sequences=params["W"][2])
[h2], updates = theano.scan(
fn=rnn_cell,
sequences=[statex2, x2_mask],
truncate_gradient=self.truncate,
outputs_info=[dict(initial=h1[-1])],
non_sequences=params["W"][3])
#predict
_s = T.nnet.softmax(h1[-1].dot(params["lrW"][0]) + h2[-1].dot(params["lrW"][1]) + params["lrb"])
_p = T.argmax(_s, axis=1)
_c = T.nnet.categorical_crossentropy(_s, y)
_c = T.sum(_c * y_c)
_l = T.sum(params["lrW"]**2)
_cost = _c + 0.01 * _l
# SGD parameters
learning_rate = T.scalar('learning_rate')
decay = T.scalar('decay')
# Gradients and updates
_grads, _updates = rms_prop(_cost, param_names, params, learning_rate, decay)
# Assign functions
self.bptt = theano.function([x1, x2, x1_mask, x2_mask, y, y_c], _grads)
self.loss = theano.function([x1, x2, x1_mask, x2_mask, y, y_c], _c)
self.weights = theano.function([x1, x2, x1_mask, x2_mask], _s)
self.predictions = theano.function([x1, x2, x1_mask, x2_mask], _p)
self.sgd_step = theano.function(
[x1, x2, x1_mask, x2_mask, y, y_c, learning_rate, decay],
updates=_updates)
def main(num_epochs=NUM_EPOCHS):
print("Building network ...")
# First, we build the network, starting with an input layer
# Recurrent layers expect input of shape
# (batch size, SEQ_LENGTH, num_inputs)
#The network model
l_in = lasagne.layers.InputLayer(shape=(BATCH_SIZE, SEQ_LENGTH, num_inputs))
l_forward_1 = lasagne.layers.LSTMLayer(l_in, N_HIDDEN, grad_clipping=GRAD_CLIP,nonlinearity=lasagne.nonlinearities.tanh)
l_forward_2 = lasagne.layers.LSTMLayer(l_forward_1, N_HIDDEN, grad_clipping=GRAD_CLIP,nonlinearity=lasagne.nonlinearities.tanh)
l_shp = lasagne.layers.ReshapeLayer(l_forward_2, (-1, N_HIDDEN))
l_dense = lasagne.layers.DenseLayer(l_shp, num_units=num_inputs, lasagne.nonlinearity=linear)
l_out = lasagne.layers.ReshapeLayer(l_dense, (-1, SEQ_LENGTH, num_inputs))
# create output out of input in order to save memory?
network_output = lasagne.layers.get_output(l_out)
cost = lasagne.objectives.squared_error(network_output,target_values).mean()
all_params = lasagne.layers.get_all_params(l_out,trainable=True)
updates = lasagne.updates.adagrad(cost, all_params, LEARNING_RATE)
input_values = T.ivector('target_output')
target_values = T.ivector('target_output')
# Theano functions for training and computing cost
print("Compiling functions ...")
train = theano.function([l_in.input_var, target_values], cost, updates=updates, allow_input_downcast=True)
compute_cost = theano.function([l_in.input_var, target_values], cost, allow_input_downcast=True)
def __init__(self, input_params=None, sentenceLayerNodesNum=[150, 120], sentenceLayerNodesSize=[(2, 200), (3, 1)], negativeLambda=1, poolingSize=[(2, 1)], mode="max"):
"""
mode is in {'max', 'average_inc_pad', 'average_exc_pad', 'sum'}
"""
rng = numpy.random.RandomState(23455)
self._corpusWithEmbeddings = T.matrix("wordIndeices")
self._dialogSentenceCount = T.ivector("dialogSentenceCount")
self._sentenceWordCount = T.ivector("sentenceWordCount")
# for list-type data
self._layer0 = layer0 = SentenceEmbeddingMultiNN(self._corpusWithEmbeddings, self._dialogSentenceCount, self._sentenceWordCount, rng, wordEmbeddingDim=200, \
sentenceLayerNodesNum=sentenceLayerNodesNum, \
sentenceLayerNodesSize=sentenceLayerNodesSize,
poolingSize=poolingSize,
mode=mode)
layer1 = HiddenLayer(
rng,
input=layer0.output,
n_in=layer0.outputDimension,
n_out=layer0.outputDimension,
activation=T.tanh
)
self._nextSentence = layer1.output
self._params = layer1.params + layer0.params
self._setParameters(input_params)
self.negativeLambda = negativeLambda
zero_count = 1
for sentence, pooling in zip(sentenceLayerNodesSize[-1::-1], [(1, 1)] + poolingSize[-1::-1]):
zero_count *= pooling[0]
zero_count += sentence[0] - 1
self.zero_count = zero_count - 1
def __theano_build__(self):
U, V, W = self.U, self.V, self.W
x = T.ivector('x')
y = T.ivector('y')
def forward_prop_step(x_t, s_t_prev, U, V, W):
s_t = T.tanh(U[:,x_t] + W.dot(s_t_prev))
o_t = T.nnet.softmax(V.dot(s_t))
return [o_t[0], s_t]
[o,s], updates = theano.scan(
forward_prop_step,
sequences=x,
outputs_info=[None, dict(initial=T.zeros(self.hidden_dim))],
non_sequences=[U, V, W],
truncate_gradient=self.bptt_truncate,
strict=True)
prediction = T.argmax(o, axis=1)
o_error = T.sum(T.nnet.categorical_crossentropy(o, y))
# Gradients
dU = T.grad(o_error, U)
dV = T.grad(o_error, V)
dW = T.grad(o_error, W)
# Assign functions
self.forward_propagation = theano.function([x], o)
self.predict = theano.function([x], prediction)
self.ce_error = theano.function([x, y], o_error)
self.bptt = theano.function([x, y], [dU, dV, dW])
# SGD
learning_rate = T.scalar('learning_rate')
self.sgd_step = theano.function([x,y,learning_rate], [],
updates=[(self.U, self.U - learning_rate * dU),
(self.V, self.V - learning_rate * dV),
(self.W, self.W - learning_rate * dW)])
def build_finetune_functions(self, learning_rate):
is_train = T.iscalar('is_train')
X = T.matrix('X')
AtRisk = T.ivector('AtRisk')
Observed = T.ivector('Observed')
#call the optimization function
opt = Opt()
forward = theano.function(
on_unused_input='ignore',
inputs=[X, Observed, AtRisk, is_train],
outputs=[self.riskLayer.cost(self.o, self.AtRisk), self.riskLayer.output, self.riskLayer.input],
givens={
self.x: X,
self.o: Observed,
self.AtRisk: AtRisk,
self.is_train:is_train
},
name='forward'
)
backward = theano.function(
on_unused_input='ignore',
inputs=[X, Observed, AtRisk, is_train],
updates=opt.SGD(self.riskLayer.cost(self.o, self.AtRisk), self.params, learning_rate),
outputs=T.grad(self.riskLayer.cost(self.o, self.AtRisk), self.params),
givens={
self.x: X,
self.o: Observed,
self.AtRisk: AtRisk,
self.is_train:is_train
},
name='forward'
)
return forward, backward
def test_unwrapper():
emb_size = 5
y_time = tt.ivector()
y_seq_id = tt.ivector()
x = tt.tensor3()
emb = IdentityInput(x, size=5)
sequn = SeqUnwrapper(20)
sequn.connect(emb, y_time, y_seq_id)
rng = np.random.RandomState(23455)
conv = LeNetConvPoolLayer()
conv.connect(sequn, rng, (3, 1, 5, emb_size), (1, 1, ))
#prev_layer = conv
f = theano.function([x, y_time, y_seq_id], conv.output())
xx = np.random.randn(20, 4, emb_size)
y_time = [3, 7, 10, 12]
y_seq_id = [0, 0, 0, 0]
res = f(xx, y_time, y_seq_id)
print res.shape
print res
import ipdb; ipdb.set_trace()
def get_training_functions(self, x_lab_np=None, y_lab_np=None, x_unlab_np=None):
# assert xlab.shape[0] == len(y_lab)
assert self.x_lab_np.shape[0] == len(y_lab)
self.x_lab = self._shared_dataset(self.x_lab_np)
self.y_lab = self._shared_dataset(self.y_lab_np)
self.x_unlab = self._shared_dataset(self.x_unlab_np)
self.alpha = float(xlab.shape[0] / xunlab.shape[0])
index_unlab = T.ivector('index_unlab')
index_lab = T.ivector('index_lab')
momentum = T.scalar('momentum')
learning_rate = T.scalar('learning_rate')
# cost, updates = self.get_cost_updates(self.x_lab, self.x_unlab, self.y_lab)
self.batch_size_lab = self.batch_size * self.alpha
self.batch_size_unlab = self.batch_size * (1-self.alpha)
x_lab = T.matrix('x_lab')
x_unlab = T.matrix('x_unlab')
y_lab = T.ivector('y_lab')
self.num_labels = self.x_lab_np.shape[0]
self.num_unlabels = self.x_unlab_np[0]
self.num_samples = num_labels + num_unlabels
num_batches = num_samples / float(self.batch_size)
pretraining_fns = []
for i in xrange(len(hidden_layers)):
ssda = self.layers[i]
exit()
cost, updates = ssda.get_cost_updates(self.x_lab, self.x_unlab, self.y_lab)
train_fn = theano.function(inputs=[index_lab, index_unlab], updates=updates, outputs=[cost], givens={self.x_lab:self.x_lab[index_lab], self.x_unlab:self.x_unlab[index_unlab], self.y_lab:self.y_lab[index_lab]})
pretraining_fns.append(train_fn)
return pretraining_fns
def EnergyVecFn(fnsim, embeddings, leftop, rightop):
embedding, relationl, relationr = parse_embeddings(embeddings)
idxl, idxo, idxr = T.ivector('idxl'), T.ivector('idxo'), T.ivector('idxr')
lhs, rhs = embedding.E[:, idxl].T, embedding.E[:, idxr].T
rell, relr = relationl.E[:, idxo].T, relationr.E[:, idxo].T
energy = - fnsim(leftop(lhs, rell), rightop(rhs, relr))
return theano.function([idxl, idxr, idxo], [energy], on_unused_input='ignore')
def train(self, word_emb): X_local = T.ivector(name="X_local") X = T.iscalar(name="X") X_neg = T.ivector(name="X_neg") X_g = T.dvector(name="X_g") [o_error], updates = theano.scan(self.target_function, sequences=X_neg,\ non_sequences=[word_emb, X_local, X, X_g]) error_sum = T.sum(o_error) self.c_error = theano.function([X_local, X, X_neg, X_g], error_sum) d_word_emb = T.grad(error_sum, word_emb) d_W1 = T.grad(error_sum, self.W1) d_b1 = T.grad(error_sum, self.b1) d_W2 = T.grad(error_sum, self.W2) d_b2 = T.grad(error_sum, self.b2) d_Wg1 = T.grad(error_sum, self.Wg1) d_bg1 = T.grad(error_sum, self.bg1) d_Wg2 = T.grad(error_sum, self.Wg2) d_bg2 = T.grad(error_sum, self.bg2) self.train_step = theano.function([X_local, X, X_neg, X_g], [], \ updates=[(word_emb-d_word_emb) (self.W1-d_W1), (self.b1-d_b1), (self.W2-d_W2), (self.b2-d_b2), (self.Wg1-d_Wg1), (self.bg1-d_bg1), (self.Wg2-d_Wg2), (self.bg2-d_bg2)])
def multMatVect(v, A, m1, B, m2):
# TODO : need description for parameter and return
"""
Multiply the first half of v by A with a modulo of m1 and the second half
by B with a modulo of m2.
Notes
-----
The parameters of dot_modulo are passed implicitly because passing them
explicitly takes more time than running the function's C-code.
"""
if multMatVect.dot_modulo is None:
A_sym = tensor.lmatrix('A')
s_sym = tensor.ivector('s')
m_sym = tensor.iscalar('m')
A2_sym = tensor.lmatrix('A2')
s2_sym = tensor.ivector('s2')
m2_sym = tensor.iscalar('m2')
o = DotModulo()(A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym)
multMatVect.dot_modulo = function(
[A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym], o, profile=False)
# This way of calling the Theano fct is done to bypass Theano overhead.
f = multMatVect.dot_modulo
f.input_storage[0].storage[0] = A
f.input_storage[1].storage[0] = v[:3]
f.input_storage[2].storage[0] = m1
f.input_storage[3].storage[0] = B
f.input_storage[4].storage[0] = v[3:]
f.input_storage[5].storage[0] = m2
f.fn()
r = f.output_storage[0].storage[0]
return r
def compile(self):
'''
Forward pass and Gradients
'''
# Get nicer names for parameters
W1, W2, W3 = [self.W1] + self.params
# FORWARD PASS
# Embedding layer subspace
self.z0 = T.ivector() # tweet in one hot
# Use an intermediate sigmoid
z1 = W1[:, self.z0] # embedding
z2 = T.nnet.sigmoid(T.dot(W2, z1)) # subspace
# Hidden layer
z3 = T.dot(W3, z2)
z4 = T.sum(z3, 1) # Bag of words
self.hat_y = T.nnet.softmax(z4.T).T
self.fwd = theano.function([self.z0], self.hat_y)
# TRAINING COST AND GRADIENTS
# Train cost minus log probability
self.y = T.ivector() # reference out
self.F = -T.mean(T.log(self.hat_y)[self.y]) # For softmax out
# Update only last three parameters
self.nablas = [] # Symbolic gradients
self.grads = [] # gradients
for W in self.params:
self.nablas.append(T.grad(self.F, W))
self.grads.append(theano.function([self.z0, self.y], T.grad(self.F, W)))
self.cost = theano.function([self.z0, self.y], self.F)
def initialize(self):
users = T.ivector()
items = T.ivector()
ratings = T.vector()
self.U = theano.shared(
np.array(
np.random.normal(scale=0.001, size=(self.n_users, self.n_factors)),
dtype=theano.config.floatX
)
)
self.I = theano.shared(
np.array(
np.random.normal(scale=0.001, size=(self.n_items, self.n_factors)),
dtype=theano.config.floatX
)
)
predictions = (self.U[users] * self.I[items]).sum(axis=1)
train_error = (
((predictions - ratings) ** 2).mean() +
self.regularization * (
T.sum(self.U ** 2) +
T.sum(self.I ** 2)
)
)
test_error = ((predictions - ratings) ** 2).mean()
params = [self.U, self.I]
learning_rate = theano.shared(np.array(self.learning_rate, dtype=theano.config.floatX))
updates = self.optimizer(train_error, params, learning_rate=learning_rate)
self.train_theano = theano.function([users, items, ratings], train_error, updates=updates)
self.test_theano = theano.function([users, items, ratings], test_error)
self.predict_theano = theano.function([users, items], predictions)
def __init__(self, vocabulary_size, hidden_size, output_size):
X = tensor.ivector()
Y = tensor.ivector()
keep_prob = tensor.fscalar()
learning_rate = tensor.fscalar()
emb_layer = Embedding(vocabulary_size, hidden_size)
lstm_layer = BiLSTM(hidden_size, hidden_size)
dropout_layer = Dropout(keep_prob)
fc_layer = FullConnect(2*hidden_size, output_size)
crf = CRF(output_size)
# graph defination
X_emb = emb_layer(X)
scores = fc_layer(tensor.tanh(lstm_layer(dropout_layer(X_emb))))
loss, predict = crf(scores, Y, isTraining=True)
# loss, predict and accuracy
accuracy = tensor.sum(tensor.eq(predict, Y)) * 1.0 / Y.shape[0]
params = emb_layer.params + lstm_layer.params + fc_layer.params + crf.params
updates = MomentumSGD(loss, params, lr=learning_rate)
print("Compiling train function: ")
train = theano.function(inputs=[X, Y, keep_prob, learning_rate], outputs=[predict, accuracy, loss],
updates=updates, allow_input_downcast=True)
print("Compiling evaluate function: ")
evaluate = theano.function(inputs=[X_emb, Y, keep_prob], outputs=[predict, accuracy, loss],
allow_input_downcast=True)
self.embedding_tensor = emb_layer.params[0]
self.train = train
self.evaluate = evaluate
self.params = params
def set_model(args, init_w_emb, w_emb_dim, vocab_word, vocab_char, vocab_tag):
print '\nBuilding a neural model: %s\n' % args.model
""" neural architecture parameters """
c_emb_dim = args.c_emb_dim
w_hidden_dim = args.w_hidden_dim
c_hidden_dim = args.c_hidden_dim
output_dim = vocab_tag.size()
window = args.window
opt = args.opt
""" symbol definition """
x = T.ivector()
c = T.ivector()
b = T.ivector()
y = T.ivector()
lr = T.fscalar('lr')
if args.model == 'char':
return nn_char.Model(name=args.model, w=x, c=c, b=b, y=y, lr=lr,
init_w_emb=init_w_emb, vocab_w_size=vocab_word.size(), vocab_c_size=vocab_char.size(),
w_emb_dim=w_emb_dim, c_emb_dim=c_emb_dim, w_hidden_dim=w_hidden_dim,
c_hidden_dim=c_hidden_dim, output_dim=output_dim,
window=window, opt=opt)
else:
return nn_word.Model(name=args.model, x=x, y=y, lr=lr,
init_emb=init_w_emb, vocab_size=vocab_word.size(),
emb_dim=w_emb_dim, hidden_dim=w_hidden_dim, output_dim=output_dim,
window=window, opt=opt)
def create_iter_funcs_valid(l_out, bs=None, N=50, mc_dropout=False):
X = T.tensor4('X')
y = T.ivector('y')
X_batch = T.tensor4('X_batch')
y_batch = T.ivector('y_batch')
if not mc_dropout:
y_hat = layers.get_output(l_out, X, deterministic=True)
else:
if bs is None:
raise ValueError('a fixed batch size is required for mc dropout')
X_repeat = T.extra_ops.repeat(X, N, axis=0)
y_sample = layers.get_output(
l_out, X_repeat, deterministic=False)
sizes = [X_repeat.shape[0] / X.shape[0]] * bs
y_sample_split = T.as_tensor_variable(
T.split(y_sample, sizes, bs, axis=0))
y_hat = T.mean(y_sample_split, axis=1)
valid_loss = T.mean(
T.nnet.categorical_crossentropy(y_hat, y))
valid_acc = T.mean(
T.eq(y_hat.argmax(axis=1), y))
valid_iter = theano.function(
inputs=[theano.Param(X_batch), theano.Param(y_batch)],
outputs=[valid_loss, valid_acc],
givens={
X: X_batch,
y: y_batch,
},
)
return valid_iter
def create_iter_funcs_train(l_out, lr, mntm, wd):
X = T.tensor4('X')
y = T.ivector('y')
X_batch = T.tensor4('X_batch')
y_batch = T.ivector('y_batch')
y_hat = layers.get_output(l_out, X, deterministic=False)
# softmax loss
train_loss = T.mean(
T.nnet.categorical_crossentropy(y_hat, y))
# L2 regularization
train_loss += wd * regularize_network_params(l_out, l2)
train_acc = T.mean(
T.eq(y_hat.argmax(axis=1), y))
all_params = layers.get_all_params(l_out, trainable=True)
updates = lasagne.updates.nesterov_momentum(
train_loss, all_params, lr, mntm)
train_iter = theano.function(
inputs=[theano.Param(X_batch), theano.Param(y_batch)],
outputs=[train_loss, train_acc],
updates=updates,
givens={
X: X_batch,
y: y_batch,
},
)
return train_iter
def evaluate_ready(self, ispro = True): var_x = T.ivector() var_y = T.ivector() print "adopt mention level evaluate ???????????????????? "+str(self.ismention) if self.model_type == "softmax" or self.model_type == "softmax_reg": if self.istransition: output = self.structure1(var_x, ispro = False) self.evafunc = theano.function([var_x], output) else: output = self.structure1(var_x, ispro) self.evafunc = theano.function([var_x], output) elif self.model_type == "maxneg": out1, out2 = self.structure2(var_x,ispro) self.evafunc = theano.function([var_x], [out1,out2]) elif self.model_type == "maxout": out1, out2 = self.structure2(var_x,False) self.evafunc = theano.function([var_x], [out1,out2]) else: raise Exception
def __init__(self, input_params=None):
rng = numpy.random.RandomState(23455)
self._corpusWithEmbeddings = T.matrix("wordIndeices")
self._dialogSentenceCount = T.ivector("dialogSentenceCount")
self._sentenceWordCount = T.ivector("sentenceWordCount")
# for list-type data
self._layer0 = SentenceEmbeddingNN(self._corpusWithEmbeddings, self._dialogSentenceCount, self._sentenceWordCount, rng, wordEmbeddingDim=200, \
sentenceLayerNodesNum=1000, \
sentenceLayerNodesSize=[5, 200])
self._average_layer = sentenceEmbeddingAverage(self._corpusWithEmbeddings, self._dialogSentenceCount, self._sentenceWordCount, rng, wordEmbeddingDim=200)
# Get sentence layer W
semanicTransformW = theano.shared(
numpy.asarray(
rng.uniform(low=-0.2, high=0.2, size=(self._layer0.outputDimension, 200)),
dtype=config.globalFloatType()
),
borrow=True
)
self._nextSentence = T.dot(self._layer0.output, semanicTransformW)
# construct the parameter array.
self._params = [semanicTransformW] + self._layer0.params
self._setParameters(input_params)
def build(self):
x=T.ivector('x')
y=T.ivector('y')
lr=T.scalar('learning_rate')
def _recurrence(x_t,s_tm1):
s_t=T.tanh(self.U[:,x_t]+T.dot(s_tm1,self.W))
o_t=T.nnet.softmax(T.dot(s_t,self.V))
return [o_t[0],s_t]
[o,s],updates=theano.scan(fn=_recurrence,
sequences=x,
outputs_info=[None,dict(initial=T.zeros(self.hidden_dim))],
truncate_gradient=self.bptt_truncate,
strict=True)
prediction=T.argmax(o,axis=1)
o_error=T.sum(T.nnet.categorical_crossentropy(o,y))
# Gradients
gparams=T.grad(o_error,self.params)
updates=[(param,param-lr*gparam) for param,gparam in zip(self.params,gparams)]
# Assign functions
self.forward_propagation=theano.function([x],o)
self.predict=theano.function([x],prediction)
self.train=theano.function(intputs=[x,y,lr],
outputs=o_error,
updates=updates)
def main(model='mlp', num_epochs=500):
# Load the dataset
print("Loading data...")
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset()
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
if model == 'mlp':
network = build_mlp(input_var)
elif model.startswith('custom_mlp:'):
depth, width, drop_in, drop_hid = model.split(':', 1)[1].split(',')
network = build_custom_mlp(input_var, int(depth), int(width),
float(drop_in), float(drop_hid))
elif model == 'cnn':
network = build_cnn(input_var)
else:
print("Unrecognized model type %r." % model)
return
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize (for our multi-class problem, it is the cross-entropy loss):
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
# We could add some weight decay as well here, see lasagne.regularization.
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss,
params,
learning_rate=0.01,
momentum=0.9)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(
test_prediction, target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
# We iterate over epochs:
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train, y_train, 500, shuffle=True):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(X_val, y_val, 500, shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(epoch + 1, num_epochs,
time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err / train_batches))
print(" validation loss:\t\t{:.6f}".format(val_err / val_batches))
print(" validation accuracy:\t\t{:.2f} %".format(val_acc /
val_batches * 100))
# After training, we compute and print the test error:
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test, y_test, 500, shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(test_err / test_batches))
print(" test accuracy:\t\t{:.2f} %".format(test_acc / test_batches * 100))
def __init__(self, numpy_rng, theano_rng=None, n_ins=None,
hidden_layers_sizes=[50], iBNhl = -1, n_outs=None):
"""This class is made to support a variable number of layers.
:type numpy_rng: numpy.random.RandomState
:param numpy_rng: numpy random number generator used to draw initial
weights
:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
:param theano_rng: Theano random generator; if None is given one is
generated based on a seed drawn from `rng`
:type n_ins: int
:param n_ins: dimension of the input to the DBN
:type n_layers_sizes: list of ints
:param n_layers_sizes: intermediate layers size, must contain
at least one value
:type n_outs: int
:param n_outs: dimension of the output of the network
"""
self.sigmoid_layers = []
self.rbm_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x') # the data is presented as rasterized images
self.y = T.ivector('y') # the labels are presented as 1D vector
# of [int] labels
# The DBN is an MLP, for which all weights of intermediate
# layers are shared with a different RBM. We will first
# construct the DBN as a deep multilayer perceptron, and when
# constructing each sigmoidal layer we also construct an RBM
# that shares weights with that layer. During pretraining we
# will train these RBMs (which will lead to chainging the
# weights of the MLP as well) During finetuning we will finish
# training the DBN by doing stochastic gradient descent on the
# MLP.
for i in xrange(self.n_layers):
# construct the sigmoidal layer
# the size of the input is either the number of hidden
# units of the layer below or the input size if we are on
# the first layer
if i == 0:
input_size = n_ins
else:
input_size = hidden_layers_sizes[i - 1]
# the input to this layer is either the activation of the
# hidden layer below or the input of the DBN if you are on
# the first layer
if i == 0:
layer_input = self.x
else:
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)
activation=T.tanh)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
# its arguably a philosophical question... but we are
# going to only declare that the parameters of the
# sigmoid_layers are parameters of the DBN. The visible
# biases in the RBM are parameters of those RBMs, but not
# of the DBN.
self.params.extend(sigmoid_layer.params)
# Construct an RBM that shared weights with this layer
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)
self.y_pred = self.logLayer.y_pred
self.p_y = self.logLayer.p_y_given_x
#print(len(self.sigmoid_layers))
#for l in self.sigmoid_layers:
# print('0 ',l.output.shape)
self.BN_f = self.sigmoid_layers[iBNhl].output
def sgd_optimization_mnist(learning_rate=0.13,
n_epochs=1000,
dataset='../data/mnist.pkl.gz',
batch_size=600):
"""
Demonstrate stochastic gradient descent optimization of a log-linear
model
This is demonstrated on MNIST.
: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: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets = load_data(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
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.ivector('y') # the labels are presented as 1D vector of
# [int] labels
# construct the logistic regression class
# Each MNIST image has size 28*28
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
# the cost we minimize during training is the negative log likelihood of
# the model in symbolic format
cost = classifier.negative_log_likelihood(y)
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.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(
inputs=[index],
outputs=classifier.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]
})
# compute the gradient of cost with respect to theta = (W,b)
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs.
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
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 #
###############
print '... training the model'
# early-stopping parameters
patience = 5000 # 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
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
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:
# 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)
best_validation_loss = this_validation_loss
# 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.))
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 'The code run for %d epochs, with %f epochs/sec' % (
epoch, 1. * epoch / (end_time - start_time))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time)))
def train_cnn_model(self, X_train, y_train):
print 'Training CNN model....'
print X_train.shape
num_classes = len(set(y_train))
######### Build CNN #########################################
#self.data_width = int(X_train.shape[2]/10)
#X_train = X_train.reshape(X_train.shape[0], 3, -1)
print X_train.shape
l_in = lasagne.layers.InputLayer(shape=(None, X_train.shape[1], X_train.shape[2]))
conv_network = l_in
#Build Convolution layers
for l in range(self.params['conv_layers']):
conv_network = lasagne.layers.Conv1DLayer(conv_network, num_filters=self.params['conv_filter_num'][l], filter_size=self.params['conv_filter_dims'], nonlinearity=lasagne.nonlinearities.rectify)
print 'l_conv%d output: '%l+str(lasagne.layers.get_output_shape(conv_network))
conv_network = lasagne.layers.MaxPool1DLayer(conv_network, pool_size=self.params['pool_size'])
print 'l_pool%d output: '%l+str(lasagne.layers.get_output_shape(conv_network))
conv_output = lasagne.layers.get_output(conv_network)
network = conv_network
#Build fully connected hidden layers
for i in range(self.params['hid_layers']):
units = self.params['hid_units'][i]
network = lasagne.layers.DenseLayer(network, num_units=units, nonlinearity=lasagne.nonlinearities.tanh)
network = lasagne.layers.DropoutLayer(network, p=0.5)
#Build output layer
network = lasagne.layers.DenseLayer(network, num_units=num_classes, nonlinearity=lasagne.nonlinearities.softmax)
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
predictions = lasagne.layers.get_output(network)
conv_weights = lasagne.layers.get_output(conv_network)
self.classifier = theano.function([l_in.input_var],predictions)
self.cnn_weights = theano.function([l_in.input_var], conv_output)
loss = lasagne.objectives.categorical_crossentropy(predictions, target_var)
loss = loss.mean()
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss,params, learning_rate=0.01, momentum=0.9)
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var), dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([l_in.input_var, target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
self.val_fn = theano.function([l_in.input_var, target_var], [test_loss, test_acc])
num_epochs = self.params['epochs']
for epoch in range(num_epochs):
start_time = time.time()
train_err = 0
train_batches = 0
for batch in self.iterate_batches(X_train, y_train):
inputs, targets = batch
train_err += train_fn(inputs, targets.astype(np.int32))
train_batches += 1
#val_err = 0
#val_acc = 0
#val_batches = 0
#for batch in self.iterate_batches(X_val, y_val):
#inputs, targets = batch
#err, acc = val_fn(X_val, y_val)
#val_err += err
#val_acc += acc
#val_batches += 1
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err / train_batches))
#print(" validation loss:\t\t{:.6f}".format(val_err / val_batches))
#print(" validation accuracy:\t\t{:.2f} %".format(
#val_acc / val_batches * 100))
cnn_X_train = self.cnn_weights(X_train)
cnn_X_train = cnn_X_train.reshape([cnn_X_train.shape[0], -1])
self.svm = SVC()
self.svm = self.svm.fit(cnn_X_train, y_train)
def mlp_run(train_set,
valid_set,
test_set,
learning_rate=0.01,
L1_reg=0.00,
L2_reg=0.0001,
n_epochs=1000,
batch_size=20,
n_hidden=500):
"""
Demonstrate stochastic gradient descent optimization of a log-linear
model
"""
print 'loading ', train_set, ' for train'
train_set_x, train_set_y = load_data(train_set)
print 'loading ', valid_set, ' for valid'
valid_set_x, valid_set_y = load_data(valid_set)
print 'loading ', test_set, ' for test'
if test_set != valid_set:
test_set_x, test_set_y = load_data(test_set)
else:
test_set_x, test_set_y = valid_set_x, valid_set_y
# 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
print "train_set_x size:", train_set_x.get_value(borrow=True).shape[0]
print "batch_size:", batch_size
print "n_train_batches:", n_train_batches
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# generate symbolic variables for input (x and y represent a
# minibatch)
x = T.matrix('x') # data, presented as rasterized images
y = T.ivector('y') # labels, presented as 1D vector of [int] labels
# construct the logistic regression class
# Each MNIST image has size 28*28
total_dim = train_set_x.get_value(borrow=True).shape[1]
rng = numpy.random.RandomState(1234)
# construct the MLP class
classifier = MLP(rng=rng,
input=x,
n_in=total_dim,
n_hidden=n_hidden,
n_out=2)
# 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
# start-snippet-5
# compute the gradient of cost with respect to theta (sotred in params)
# the resulting gradients will be stored in a list gparams
gparams = [T.grad(cost, param) for param in classifier.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
# given two lists of the same length, A = [a1, a2, a3, a4] and
# B = [b1, b2, b3, b4], zip generates a list C of same size, where each
# element is a pair formed from the two lists :
# C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
updates = [(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, gparams)]
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.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(
inputs=[index],
outputs=classifier.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]
})
validate_auc = theano.function(inputs=[],
outputs=classifier.auc(y),
givens={
x: valid_set_x,
y: valid_set_y
})
test_auc = theano.function(inputs=[],
outputs=classifier.auc(y),
givens={
x: test_set_x,
y: test_set_y
})
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
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]
})
# end-snippet-3
###############
# TRAIN MODEL #
###############
print '... training the model'
# early-stopping parameters
patience = FLAGS.iter # 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
print "n_train_batches:", n_train_batches
print "validation_frequency:", validation_frequency
best_validation_loss = numpy.inf
best_auc = 0
test_score = 0.
start_time = timeit.default_timer()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
if epoch == 10:
learning_rate *= 0.8
if epoch == 20:
learning_rate *= 0.5
if epoch == 30:
learning_rate = 0.01
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:
# 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.))
auc_values = [validate_auc()]
auc = numpy.mean(auc_values)
print "current valid auc: ", auc, " best auc: ", best_auc, " imporve: ", auc - best_auc, " significant?: ", auc - best_auc > FLAGS.min_improvement
#print validate_auc(0)
if auc > best_auc:
if auc - best_auc > FLAGS.min_improvement:
print 'before patience:', patience, ' iter:', iter
patience = max(patience, iter * patience_increase)
print 'after patience:', patience
best_auc = auc
auc_values = [test_auc()]
testauc = numpy.mean(auc_values)
print "test auc: ", testauc
#cPickle.dump(classifier, open('best_model.pkl', 'wb'))
if patience <= iter:
done_looping = True
print "patience:", patience, "iter:", iter, "done_looping:", done_looping
break
end_time = timeit.default_timer()
print(('Optimization complete with best validation score of %f %%,'
'with test performance %f %%') %
(best_validation_loss * 100., test_score * 100.))
print 'The code run for %d epochs, with %f epochs/sec' % (
epoch, 1. * epoch / (end_time - start_time))
print 'best valid auc is ', best_auc
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time)))
def __init__(
self,
numpy_rng,
theano_rng=None,
cfg=None, # the network configuration
dnn_shared=None,
shared_layers=[],
input=None):
self.layers = []
self.params = []
self.delta_params = []
self.rnn_layerX = 2
print "Use DRN"
self.cfg = cfg
self.n_ins = cfg.n_ins
self.n_outs = cfg.n_outs
self.hidden_layers_sizes = cfg.hidden_layers_sizes
self.hidden_layers_number = len(self.hidden_layers_sizes)
self.activation = cfg.activation
self.do_maxout = cfg.do_maxout
self.pool_size = cfg.pool_size
self.max_col_norm = cfg.max_col_norm
self.l1_reg = cfg.l1_reg
self.l2_reg = cfg.l2_reg
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2**30))
# allocate symbolic variables for the data
if input == None:
self.x = T.matrix('x')
else:
self.x = input
self.y = T.ivector('y')
for i in xrange(self.hidden_layers_number):
# construct the hidden layer
if i == 0:
input_size = self.n_ins
layer_input = self.x
else:
input_size = self.hidden_layers_sizes[i - 1]
layer_input = self.layers[-1].output
W = None
b = None
if (i in shared_layers):
W = dnn_shared.layers[i].W
b = dnn_shared.layers[i].b
if i == self.rnn_layerX:
hidden_layer = RnnLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i],
W=W,
b=b,
activation=self.activation)
else:
if self.do_maxout == True:
hidden_layer = HiddenLayer(
rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i] * self.pool_size,
W=W,
b=b,
activation=(lambda x: 1.0 * x),
do_maxout=True,
pool_size=self.pool_size)
else:
hidden_layer = HiddenLayer(
rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i],
W=W,
b=b,
activation=self.activation)
# add the layer to our list of layers
self.layers.append(hidden_layer)
self.params.extend(hidden_layer.params)
self.delta_params.extend(hidden_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(input=self.layers[-1].output,
n_in=self.hidden_layers_sizes[-1],
n_out=self.n_outs)
if self.n_outs > 0:
self.layers.append(self.logLayer)
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
if self.l1_reg is not None:
for i in xrange(self.hidden_layers_number):
W = self.layers[i].W
self.finetune_cost += self.l1_reg * (abs(W).sum())
if self.l2_reg is not None:
for i in xrange(self.hidden_layers_number):
W = self.layers[i].W
self.finetune_cost += self.l2_reg * T.sqr(W).sum()
def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
dataset=DataSet,
nkerns=[cls1, cls2], batch_size=100):
""" 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
"""
rng = numpy.random.RandomState(23455)
datasets = load_data(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]
print type(train_set_x)
#train_set_x.set_value(train_set_x.get_value(borrow=True)[:,:540])
#valid_set_x.set_value(valid_set_x.get_value(borrow=True)[:,:540])
#test_set_x.set_value(test_set_x.get_value(borrow=True)[:,:540])
#train_set_x = train_set_x / 100
#valid_set_x = valid_set_x / 100
#test_set_x = test_set_x / 100
# 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
#n_test_batches = (n_test_batches/batch_size) + (n_test_batches % batch_size > 0)
print (n_test_batches)
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
Alr = T.scalar('Alr')
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 = (nFB, nFs) # 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
dFeatureV = 2*nFB*nFs
xinp = x[:,:dFeatureV]
# print (x.shahpe)
layer0_input = xinp.reshape((batch_size, 2, nFB, nFs))
# 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, 2, nFB, nFs),
filter_shape=(nkerns[0], 2, fsx, fsy), poolsize=(p1, 1))
cl2x = (nFB - fsx + 1)/p1
cl2y = (nFs - fsy + 1)
# 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], cl2x, cl2y),
filter_shape=(nkerns[1], nkerns[0], fsx, 1), poolsize=(p2, 1))
hl1 = (cl2x - fsx + 1)/p2
# 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.output.flatten(2)
layer2_inputT = T.concatenate([layer2_input,x[:,dFeatureV:]],axis = 1)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_inputT, n_in=(nkerns[1] * hl1 * 1)+12,
n_out=nhu1, activation=T.tanh)
layer22 = HiddenLayer(rng, input=layer2.output, n_in=nhu1,
n_out=nhu1, activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer22.output, n_in=nhu1, n_out=n_out)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
#yPred = layer3.ypred(layer2.output)
# create a function to compute the mistakes that are made by the model
test_model = theano.function([index], [layer3.errors(y), layer3.y_pred],
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 + layer22.params + layer2.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))
updates.append((param_i, param_i - Alr * grad_i))
train_model = theano.function([index, Alr], 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 = 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_params = None
best_params = []
best_validation_loss = numpy.inf
prev_validation_loss = 200
best_iter = 0
test_score = 0.
start_time = time.clock()
Alrc = 0.1
AlrE = 0.00001
epochC = 0
epoch = 0
done_looping = False
for param in params:
best_params.append(param.get_value())
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
epochC = epochC + 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, Alrc)
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)
lossratio = (this_validation_loss - prev_validation_loss)/(prev_validation_loss+1)
print (lossratio)
print('epoch %i, minibatch %i/%i, validation error %f, lr %f %%' % \
(epoch, minibatch_index + 1, n_train_batches, \
this_validation_loss * 100., Alrc))
# if we got the best validation score until now
#if this_validation_loss < best_validation_loss:
if lossratio <= 0.0:
for i in range(len(params)):
best_params[i] = params[i].get_value()
#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
prev_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
#tm = test_model(0)
yP = numpy.asarray([])
test_losses = [test_model(i)[0] for i in xrange(n_test_batches)]
for i in xrange(n_test_batches):
yP = numpy.concatenate((yP,test_model(i)[1]))
print yP.shape
test_score = numpy.mean(test_losses)
#yP = yPred#yPred(layer2.output.owner.inputs[0].get_value())
y = test_set_y.owner.inputs[0].get_value()[:3000]
print (yP.shape)
print (y.shape)
I1 = numpy.nonzero(y==0.0)
I2 = numpy.nonzero(y==1.0)
I3 = numpy.nonzero(y==2.0)
I4 = numpy.nonzero(y==3.0)
print (I1[0].shape)
print (I2[0].shape)
print (I3[0].shape)
print (I4[0].shape)
I11 = numpy.nonzero(yP[I1[0]]==0)
I12 = numpy.nonzero(yP[I1[0]]==1)
I13 = numpy.nonzero(yP[I1[0]]==2)
I14 = numpy.nonzero(yP[I1[0]]==3)
I21 = numpy.nonzero(yP[I2[0]]==0)
I22 = numpy.nonzero(yP[I2[0]]==1)
I23 = numpy.nonzero(yP[I2[0]]==2)
I24 = numpy.nonzero(yP[I2[0]]==3)
I31 = numpy.nonzero(yP[I3[0]]==0)
I32 = numpy.nonzero(yP[I3[0]]==1)
I33 = numpy.nonzero(yP[I3[0]]==2)
I34 = numpy.nonzero(yP[I3[0]]==3)
I41 = numpy.nonzero(yP[I4[0]]==0)
I42 = numpy.nonzero(yP[I4[0]]==1)
I43 = numpy.nonzero(yP[I4[0]]==2)
I44 = numpy.nonzero(yP[I4[0]]==3)
acc1 = float(float(I11[0].size)/float(I1[0].size))
acc2 = float(float(I22[0].size)/float(I2[0].size))
if n_out == 3:
acc3 = float(float(I33[0].size)/float(I3[0].size))
elif n_out == 4:
acc3 = float(float(I33[0].size)/float(I3[0].size))
acc4 = float(float(I44[0].size)/float(I4[0].size))
else:
acc3 = 0
acc4 = 0
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f, acc1 = %f, acc2 = %f, acc3 = %f, acc4 = %f, I11 = %i, I12 = %i, I13 = %i, I14 = %i, I21 = %i, I22 = %i, I23 = %i, I24 = %i, I31 = %i, I32 = %i, I33 = %i, I34 = %i, I41 = %i, I42 = %i, I43 = %i, I44 = %i %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100., acc1 * 100., acc2 * 100., acc3 * 100, acc4 * 100, I11[0].size, I12[0].size, I13[0].size, I14[0].size, I21[0].size, I22[0].size, I23[0].size, I24[0].size, I31[0].size, I32[0].size, I33[0].size, I34[0].size, I41[0].size, I42[0].size, I43[0].size, I44[0].size))
#print((' epoch %i, minibatch %i/%i, test error of best '
# 'model %f %%') %
# (epoch, minibatch_index + 1, n_train_batches,
# test_score * 100.))
else:
if Alrc <= AlrE:
done_looping = True
break
elif epochC > 40:
Alrc = Alrc/2
for param, best_param in zip(params,best_params):
param.set_value(best_param)
epochC = 0
#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.))
def fit(self,
X,
Y,
learning_rate=10e-5,
mu=0.9,
decay=0.99,
epochs=10,
batch_sz=100,
eps=10e-10,
display_cost=False):
#learning_rate=10e-7, mu=0.99, decay=0.999, epochs=100, batch_sz=30, l2=0.0, eps=10e-10
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
decay = np.float32(decay)
eps = np.float32(eps)
'''
In Theano we can't actually 'drop' the nodes;
that would result in a different computational graph,
we are instead to multiply nodes by 1 and 0;
for each layer we then need to create a 'mask' - array of 0s and 1s;
Theano graph nodes don't have values, so we can't multiply them by numpy array 'mask';
instead we want Theano to generate random values every time it's called;
thus we create an instance of RandomStreams object:
'''
self.rng = RandomStreams()
# first, make a validation set:
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:, :], Y[-1000:]
X, Y = X[:-1000, :], Y[:-1000]
#initialize the hidden layers:
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
# the size of the first dimension of the first matrix:
M1 = D
count = 0 # for the id of the weigts/biases
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2 # update the first dimension size fir the next iteration
count += 1
# for the last weight/bias matrix (vector):
W, b = init_weight_and_bias(M1, K)
self.W = theano.shared(W, 'W%s' % count)
self.b = theano.shared(b, 'b%s' % count)
# collect all the parameters we are going to use during Gradient Descent:
self.parameters = [self.W, self.b]
for h in self.hidden_layers[::-1]:
self.parameters += h.params
# in order to use Momentum,
# we are to keep track of all the changes (dW's and db's):
dparams = [
theano.shared(np.zeros_like(p.get_value(), dtype=np.float32))
for p in self.parameters
]
# for RMSProp,
# we are to keep track of caches (cache_W's and cache_b's) as well:
caches = [
theano.shared(np.ones_like(p.get_value(), dtype=np.float32))
for p in self.parameters
]
# define theano variables and functions:
thX = T.matrix('X')
thY = T.ivector('Y') # a vector of integers
# since we do dropout, we drop the nodes only on training step,
# when evaluating we just scale them;
# so we need to define two expressions for the output and cost calculations:
pY_train = self.forward_train(thX)
pY_predict = self.forward_predict(thX)
cost = -T.mean(T.log(pY_train[T.arange(thY.shape[0]), thY]))
cost_predict = -T.mean(T.log(pY_predict[T.arange(thY.shape[0]), thY]))
prediction = self.predict(thX) # will do sort of T.argmax(pY, axis=1)
cost_predict_op = theano.function(inputs=[thX, thY],
outputs=[cost_predict, prediction])
# the updates for the train function:
updates = [
(cache, decay * cache +
(np.float32(1.0) - decay) * T.grad(cost, p)**2)
for p, cache in zip(self.parameters, caches)
] + [(dp,
mu * dp - learning_rate * T.grad(cost, p) / T.sqrt(cache + eps))
for dp, p, cache in zip(dparams, self.parameters, caches)
] + [(p, p + dp) for p, dp in zip(self.parameters, dparams)]
#updates = rmsprop(cost, self.parameters, learning_rate, mu, decay, eps)
train_op = theano.function(inputs=[thX, thY], updates=updates)
# batch SGD:
n_batches = N // batch_sz
costs = []
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j + 1) * batch_sz, :]
Ybatch = Y[j * batch_sz:(j + 1) * batch_sz]
train_op(Xbatch, Ybatch)
if j % 20 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print('\ni: %d, j: %d, cost: %.6f, \nerror: %.6f' %
(i, j, c, e))
if display_cost:
plt.plot(costs)
plt.show()
mini_updates = []
micro_updates = []
last_upd = []
update = []
# shared variables
learning_rate = shared(float32(lr.init))
if use.mom:
momentum = shared(float32(mom.momentum))
drop.p_vid = shared(float32(drop.p_vid_val))
drop.p_hidden = shared(float32(drop.p_hidden_val))
idx_mini = T.lscalar(name="idx_mini") # minibatch index
idx_micro = T.lscalar(name="idx_micro") # microbatch index
x = ndtensor(len(tr.in_shape))(name='x') # video input
y = T.ivector(name='y') # labels
x_ = _shared(empty(tr.in_shape))
y_ = _shared(empty(tr.batch_size))
y_int32 = T.cast(y_, 'int32')
# in shape: #frames * gray/depth * body/hand * 4 maps
import cPickle
f = open(os.path.join(load_path, 'SK_normalization.pkl'), 'rb')
SK_normalization = cPickle.load(f)
Mean1 = SK_normalization['Mean1']
Std1 = SK_normalization['Std1']
f = open('CNN_normalization.pkl', 'rb')
CNN_normalization = cPickle.load(f)
Mean_CNN = CNN_normalization['Mean_CNN']
Std_CNN = CNN_normalization['Std_CNN']
def __init__(self, number_samples):
# set up weights and biases
d = 1 # depth of x
n = number_samples
# init
Wx = np.asarray(
rng.uniform(low=-np.sqrt(2. / (d + n_hidden)),
high=np.sqrt(2. / (d + n_hidden)),
size=(d, n_hidden)))
self.Wx = theano.shared(Wx, name='Wx', borrow=True)
Wh = np.asarray(
rng.uniform(low=-np.sqrt(2. / (d + n_hidden)),
high=np.sqrt(2 / (d + n_hidden)),
size=(n_hidden, n_hidden)))
self.Wh = theano.shared(Wh, name='Wh', borrow=True)
bh = np.zeros(n_hidden)
self.bh = theano.shared(bh, name='bh', borrow=True)
ho = np.zeros(n_hidden)
self.ho = theano.shared(ho, name='ho', borrow=True)
Wo = np.asarray(
rng.uniform(low=-np.sqrt(2. / (n_hidden + n_out)),
high=np.sqrt(2. / (n_hidden + n_out)),
size=(n_hidden, n_out)))
self.Wo = theano.shared(Wo, name='Wo', borrow=True)
bo = np.zeros(n_out)
self.bo = theano.shared(bo, name='bo', borrow=True)
# values to adjust with back propagation
self.parameters = [
self.Wx, self.Wh, self.bh, self.ho, self.Wo, self.bo
]
# recurrence functions
thX = T.fmatrix('x')
thY = T.ivector('y')
# feed forward equations
def recurrence(x_t, h_t1):
h_t = T.nnet.relu(
T.dot(x_t, self.Wx) + T.dot(h_t1, self.Wh) + self.bh)
y_t = T.nnet.softmax(T.dot(h_t, self.Wo) + self.bo)
return h_t, y_t
# loop over feed forward equations once for each bit in the sequence
# send previous hidden output back through and collect prediction
[h, y_predicted], _ = theano.scan(
fn=recurrence,
outputs_info=[self.ho, None],
sequences=thX,
n_steps=thX.shape[0],
)
# probability of x given y
py_x = y_predicted[:, 0, :]
prediction = T.argmax(py_x, axis=1) # fetch most likely prediction
# cost functions for gradients and tracking progress
cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]),
thY])) # cross entropy
gradients = T.grad(cost, self.parameters) # derivatives
updates = [(p, p - learning_rate * g)
for p, g in zip(self.parameters, gradients)]
# training and prediction functions
self.predict_op = theano.function(inputs=[thX], outputs=prediction)
self.train_op = theano.function(inputs=[thX, thY],
outputs=cost,
updates=updates)
def build_model(shared_params, options):
trng = RandomStreams(1234)
drop_ratio = options['drop_ratio']
batch_size = options['batch_size']
n_dim = options['n_dim']
w_emb = shared_params['w_emb']
dropout = theano.shared(numpy.float32(0.))
image_feat = T.ftensor3('image_feat')
# batch_size x T
input_idx = T.imatrix('input_idx')
input_mask = T.matrix('input_mask')
# label is the TRUE label
label = T.ivector('label')
empty_word = theano.shared(value=np.zeros((1, options['n_emb']),
dtype='float32'),
name='empty_word')
w_emb_extend = T.concatenate([empty_word, shared_params['w_emb']],
axis=0)
input_emb = w_emb_extend[input_idx]
# a trick here, set the maxpool_h/w to be large
# maxpool_shape = (options['maxpool_h'], options['maxpool_w'])
# turn those appending words into zeros
# batch_size x T x n_emb
input_emb = input_emb * input_mask[:, :, None]
if options['sent_drop']:
input_emb = dropout_layer(input_emb, dropout, trng, drop_ratio)
if options['use_unigram_conv']:
unigram_conv_feat = fflayer(shared_params, input_emb, options,
prefix='conv_unigram',
act_func=options.get('sent_conv_act', 'tanh'))
unigram_pool_feat = unigram_conv_feat.max(axis=1)
if options['use_bigram_conv']:
idx = T.concatenate([T.arange(input_emb.shape[1])[:-1],
T.arange(input_emb.shape[1])[1:]]).reshape((2, input_emb.shape[1] - 1)).transpose().flatten()
bigram_emb = T.reshape(input_emb[:, idx, :], (input_emb.shape[0],
input_emb.shape[1] - 1,
2 * input_emb.shape[2]))
bigram_conv_feat = fflayer(shared_params, bigram_emb,
options, prefix='conv_bigram',
act_func=options.get('sent_conv_act', 'tanh'))
bigram_pool_feat = bigram_conv_feat.max(axis=1)
if options['use_trigram_conv']:
idx = T.concatenate([T.arange(input_emb.shape[1])[:-2],
T.arange(input_emb.shape[1])[1:-1],
T.arange(input_emb.shape[1])[2:]]).reshape((3, input_emb.shape[1] - 2)).transpose().flatten()
trigram_emb = T.reshape(input_emb[:, idx, :], (input_emb.shape[0],
input_emb.shape[1] - 2,
3 * input_emb.shape[2]))
trigram_conv_feat = fflayer(shared_params, trigram_emb,
options, prefix='conv_trigram',
act_func=options.get('sent_conv_act', 'tanh'))
trigram_pool_feat = trigram_conv_feat.max(axis=1) #
pool_feat = T.concatenate([unigram_pool_feat,
bigram_pool_feat,
trigram_pool_feat], axis=1)
image_feat_down = fflayer(shared_params, image_feat, options,
prefix='image_mlp',
act_func=options.get('image_mlp_act',
'tanh'))
if options.get('use_before_attention_drop', False):
image_feat_down = dropout_layer(image_feat_down, dropout, trng, drop_ratio)
pool_feat = dropout_layer(pool_feat, dropout, trng, drop_ratio)
# attention model begins here
# first layer attention model
image_feat_attention_1 = fflayer(shared_params, image_feat_down, options,
prefix='image_att_mlp_1',
act_func=options.get('image_att_mlp_act',
'tanh'))
pool_feat_attention_1 = fflayer(shared_params, pool_feat, options,
prefix='sent_att_mlp_1',
act_func=options.get('sent_att_mlp_act',
'tanh'))
combined_feat_attention_1 = image_feat_attention_1 + \
pool_feat_attention_1[:, None, :]
if options['use_attention_drop']:
combined_feat_attention_1 = dropout_layer(combined_feat_attention_1,
dropout, trng, drop_ratio)
combined_feat_attention_1 = fflayer(shared_params,
combined_feat_attention_1, options,
prefix='combined_att_mlp_1',
act_func=options.get(
'combined_att_mlp_act',
'tanh'))
prob_attention_1 = T.nnet.softmax(combined_feat_attention_1[:, :, 0])
image_feat_ave_1 = (prob_attention_1[:, :, None] * image_feat_down).sum(axis=1)
combined_hidden_1 = image_feat_ave_1 + pool_feat
# second layer attention model
image_feat_attention_2 = fflayer(shared_params, image_feat_down, options,
prefix='image_att_mlp_2',
act_func=options.get('image_att_mlp_act',
'tanh'))
pool_feat_attention_2 = fflayer(shared_params, combined_hidden_1, options,
prefix='sent_att_mlp_2',
act_func=options.get('sent_att_mlp_act',
'tanh'))
combined_feat_attention_2 = image_feat_attention_2 + \
pool_feat_attention_2[:, None, :]
if options['use_attention_drop']:
combined_feat_attention_2 = dropout_layer(combined_feat_attention_2,
dropout, trng, drop_ratio)
combined_feat_attention_2 = fflayer(shared_params,
combined_feat_attention_2, options,
prefix='combined_att_mlp_2',
act_func=options.get(
'combined_att_mlp_act', 'tanh'))
prob_attention_2 = T.nnet.softmax(combined_feat_attention_2[:, :, 0])
image_feat_ave_2 = (prob_attention_2[:, :, None] * image_feat_down).sum(axis=1)
if options.get('use_final_image_feat_only', False):
combined_hidden = image_feat_ave_2 + pool_feat
else:
combined_hidden = image_feat_ave_2 + combined_hidden_1
for i in range(options['combined_num_mlp']):
if options.get('combined_mlp_drop_%d'%(i), False):
combined_hidden = dropout_layer(combined_hidden, dropout, trng,
drop_ratio)
if i == options['combined_num_mlp'] - 1:
combined_hidden = fflayer(shared_params, combined_hidden, options,
prefix='combined_mlp_%d'%(i),
act_func='linear')
else:
combined_hidden = fflayer(shared_params, combined_hidden, options,
prefix='combined_mlp_%d'%(i),
act_func=options.get('combined_mlp_act_%d'%(i),
'tanh'))
# drop the image output
prob = T.nnet.softmax(combined_hidden)
prob_y = prob[T.arange(prob.shape[0]), label]
pred_label = T.argmax(prob, axis=1)
# sum or mean?
cost = -T.mean(T.log(prob_y))
accu = T.mean(T.eq(pred_label, label))
# return image_feat, input_idx, input_mask, \
# label, dropout, cost, accu
return image_feat, input_idx, input_mask, \
label, dropout, cost, accu, pred_label, \
prob_attention_1, prob_attention_2
def test_mlp(learning_rate=0.01,
L1_reg=0.00,
L2_reg=0.0001,
n_epochs=200,
dataset='mnist.pkl.gz',
batch_size=20,
n_hidden=500):
"""
Demonstrate stochastic gradient descent optimization for a multilayer
perceptron
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient
:type L1_reg: float
:param L1_reg: L1-norm's weight when added to the cost (see
regularization)
:type L2_reg: float
:param L2_reg: L2-norm's weight when added to the cost (see
regularization)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets = load_data(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
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.ivector('y') # the labels are presented as 1D vector of
# [int] labels
rng = numpy.random.RandomState(1234)
# construct the MLP class
classifier = MLP(rng=rng,
input=x,
n_in=28 * 28,
n_hidden=n_hidden,
n_out=10)
classifier.hiddenLayer.printWts()
classifier.hiddenLayer2.printWts()
# 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
test_model = theano.function(
inputs=[index],
outputs=classifier.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(
inputs=[index],
outputs=classifier.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]
})
# start-snippet-5
# compute the gradient of cost with respect to theta (sorted in params)
# the resulting gradients will be stored in a list gparams
gparams = [T.grad(cost, param) for param in classifier.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
# given two lists of the same length, A = [a1, a2, a3, a4] and
# B = [b1, b2, b3, b4], zip generates a list C of same size, where each
# element is a pair formed from the two lists :
# C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
updates = [(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, gparams)]
# compiling a Theano function `train_model` that returns the cost, but
# in the same time updates the parameter of the model based on the rules
# defined in `updates`
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]
})
# end-snippet-5
###############
# TRAIN MODEL #
###############
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_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(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:
# compute zero-one loss on validation set
validation_losses = [
validate_model(i) for i in range(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)
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(i) for i in range(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.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
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(
('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' %
((end_time - start_time) / 60.)),
file=sys.stderr)
classifier.hiddenLayer.printWts()
classifier.hiddenLayer2.printWts()
def __init__(self,
num_emb,
emb_dim,
hidden_dim,
output_dim,
degree=2,
dep_types=3,
learning_rate=0.01,
momentum=0.9,
trainable_embeddings=True,
labels_on_nonroot_nodes=False,
eval_on_entities=True,
num_entities=2):
assert emb_dim > 1 and hidden_dim > 1
self.num_emb = num_emb
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.degree = degree
self.learning_rate = learning_rate
self.momentum = momentum
self.num_entities = num_entities
self.arc_type = dep_types
self.params = []
self.embeddings = theano.shared(
self.init_matrix([self.num_emb, self.emb_dim]))
if trainable_embeddings:
self.params.append(self.embeddings)
self.x = T.ivector(name='x') # word indices
if labels_on_nonroot_nodes:
print 'matrix!!!'
self.y = T.fmatrix(
name='y') # output shape [None, self.output_dim]
self.y_exists = T.fvector(name='y_exists') # shape [None]
else:
#print 'vector!!!'
# Modifying this part too for the -log_prob loss
print 'scalar!!!'
self.y = T.iscalar(name='y')
#self.y = T.fvector(name='y') # output shape [self.output_dim]
self.num_words = self.x.shape[
0] # total number of nodes (leaves + internal) in tree
emb_x = self.embeddings[self.x]
#emb_x = emb_x * T.neq(self.x, -1).dimshuffle(0, 'x') # zero-out non-existent embeddings
if labels_on_nonroot_nodes:
self.tree = T.imatrix(name='tree') # shape [None, self.degree]
self.tree_states = self.compute_tree(emb_x, self.tree)
self.output_fn = self.create_output_fn_multi()
self.pred_y = self.output_fn(self.tree_states)
self.loss = self.loss_fn_multi(self.y, self.pred_y, self.y_exists)
elif eval_on_entities:
#self.tree = T.tensor3(name='tree')
self.tree = T.matrix(name='tree')
self.tree_states = self.compute_tree(emb_x, self.tree)
self.output_fn = self.create_entity_output_fn()
self.entities = [
T.ivector(name='entt' + str(i))
for i in range(self.num_entities)
]
self.entity_tv = T.sum(self.tree_states[self.entities[0]], axis=0)
for enidx in self.entities[1:]:
self.entity_tv = T.concatenate(
[self.entity_tv,
T.sum(self.tree_states[enidx], axis=0)])
self.pred_y = self.output_fn(self.entity_tv)
self.loss = self.loss_fn(self.y, self.pred_y)
else:
self.tree = T.imatrix(name='tree') # shape [None, self.degree]
self.tree_states = self.compute_tree(emb_x, self.tree)
self.final_state = self.tree_states[-1]
self.output_fn = self.create_output_fn()
self.pred_y = self.output_fn(self.final_state)
self.loss = self.loss_fn(self.y, self.pred_y)
self.tree_states = None
updates = self.gradient_descent(self.loss)
grads = T.grad(self.loss, self.params)
train_inputs = [self.x, self.tree, self.y]
pred_inputs = [self.x, self.tree]
if labels_on_nonroot_nodes:
train_inputs.append(self.y_exists)
if eval_on_entities:
train_inputs.extend(self.entities)
pred_inputs.extend(self.entities)
print 'train_inputs:', train_inputs
print 'pred_inputs:', pred_inputs
self._train = theano.function(train_inputs, [self.loss, self.pred_y],
updates=updates) #,
#allow_input_downcast=True)
self._predict = theano.function(pred_inputs, self.pred_y) #,
def __init__(self,
n_dim,
n_out,
n_chan=1,
n_superbatch=12800,
opt_alg='adam',
opt_params={
'lr': 1e-3,
'b1': 0.9,
'b2': 0.99
}):
self.numpy_rng = np.random.RandomState(1234)
self.theano_rng = RandomStreams(self.numpy_rng.randint(2**30))
self.n_dim = n_dim
self.n_out = n_out
self.n_superbatch = n_superbatch
self.alg = opt_alg
self.n_class = 10
lr = opt_params.get('lr')
n_batch = opt_params.get('nb')
train_set_x = theano.shared(
np.empty((n_superbatch, n_chan, n_dim, n_dim),
dtype=theano.config.floatX),
borrow=False,
)
val_set_x = theano.shared(
np.empty((n_superbatch, n_chan, n_dim, n_dim),
dtype=theano.config.floatX),
borrow=False,
)
train_set_y = theano.shared(
np.empty((n_superbatch, ), dtype=theano.config.floatX),
borrow=False,
)
val_set_y = theano.shared(
np.empty((n_superbatch, ), dtype=theano.config.floatX),
borrow=False,
)
train_set_y_int = T.cast(train_set_y, 'int32')
val_set_y_int = T.cast(val_set_y, 'int32')
train_rbm_px_mu = theano.shared(
np.empty((n_superbatch, self.n_aux), dtype=theano.config.floatX),
borrow=False,
)
X = T.tensor4(dtype=theano.config.floatX)
S = T.tensor3(dtype=theano.config.floatX)
Y = T.ivector()
px_mu = T.lscalar(dtype=config.floatX)
idx1, idx2 = T.lscalar(), T.lscalar()
alpha = T.scalar(dtype=theano.config.floatX) # learning rate
self.inputs = (X, Y, idx1, idx2, S, px_mu)
# ----------------------------
# Begin RBM-only
self.rbm_network = self.create_rbm_model(n_dim, n_out, n_chan)
persistent_chain = theano.shared(
np.zeros((n_batch, self.n_hidden), dtype=theano.config.floatX),
borrow=True,
)
rbm_cost, rbm_acc, rbm_updates = self.get_rbm_objective_and_updates(
alpha,
lr=lr,
persistent=persistent_chain,
)
self.rbm_objectives = (rbm_cost, rbm_acc)
self.rbm_train = theano.function(
[idx1, idx2, alpha],
[rbm_cost, rbm_acc],
updates=rbm_updates,
givens={
X: train_set_x[idx1:idx2],
Y: train_set_y_int[idx1:idx2]
},
on_unused_input='warn',
)
# End RBM-only
# ----------------------------
# Begin DADGM-only
tau = theano.shared(
np.float32(5.0),
name='temperature',
allow_downcast=True,
borrow=False,
)
self.tau = tau
self.dadgm_network = self.create_dadgm_model(
X,
Y,
n_dim,
n_out,
n_chan,
)
dadgm_loss, dadgm_acc = self.create_dadgm_objectives(False)
self.dadgm_objectives = (dadgm_loss, dadgm_acc)
dadgm_params = self.get_dadgm_params()
dadgm_grads = self.create_dadgm_gradients(dadgm_loss, False)
dadgm_updates = self.create_dadgm_updates(
dadgm_grads,
dadgm_params,
alpha,
opt_alg,
opt_params,
)
self.dadgm_train = theano.function(
[idx1, idx2, alpha],
[dadgm_loss, dadgm_acc],
updates=dadgm_updates,
givens={
X: train_set_x[idx1:idx2],
Y: train_set_y_int[idx1:idx2],
px_mu: train_rbm_px_mu,
},
on_unused_input='warn',
)
self.dadgm_loss = theano.function(
[X, Y],
[dadgm_loss, dadgm_acc],
on_unused_input='warn',
)
# End DADGM-only
# ----------------------------
self.n_batch = n_batch
# parameters for sampling
self.n_chain = 100
# save data variables
self.train_set_x = train_set_x
self.train_set_y = train_set_y
self.val_set_x = val_set_x
self.val_set_y = val_set_y
self.train_rbm_px_mu = train_rbm_px_mu
self.data_loaded = False
from lasagne.layers import InputLayer, DenseLayer import lasagne from lasagne.updates import sgd, total_norm_constraint import theano.tensor as T x = T.matrix() y = T.ivector() l_in = InputLayer((5, 10)) l1 = DenseLayer(l_in, num_units=7, nonlinearity=T.nnet.softmax) output = lasagne.layers.get_output(l1, x) cost = T.mean(T.nnet.categorical_crossentropy(output, y)) all_params = lasagne.layers.get_all_params(l1) all_grads = T.grad(cost, all_params) scaled_grads = total_norm_constraint(all_grads[i], 5) updates = sgd(scaled_grads, all_params, learning_rate=0.1)
def build_and_train(self,
X_train,
y_train,
X_val=None,
y_val=None,
display=False,
save_model=True,
aug_params=None):
"""
Builds the model and runs the training loop.
Parameters
----------
X_train : numpy array
Training data
y_train : numpy array
Training targets.
X_val : numpy array, None, optional
Validation data
y_val : numpy array, None, optional
Validation targets
Display : bool, optional
Display on-fly plots of training and validation results.
Save_model : bool, optional
Save model weights.
aug_params : dict, None, optional
Dict containing the data augmentation parameters.
Returns
-------
Test function of the net.
"""
# ======================================================================
# Model compilation
# ======================================================================
print("Building model and compiling functions...")
# Create Theano variables for input and target minibatch
input_var = T.tensor4(
'X', dtype=theano.config.floatX) # shape (batchsize,3,224,224)
target_var = T.ivector('y') # shape (batchsize,)
# Load model weights and metadata
d = pickle.load(
open(
os.path.join(homedir, 'data', 'pretrained_weights',
'resnet50.pkl')))
# Build the network and fill with pretrained weights except for the last fc layer
net = build_model(input_var, self.output_dim)
lasagne.layers.set_all_param_values(net['pool5'], d['values'][:-2])
# create loss function and accuracy
prediction = lasagne.layers.get_output(net['prob'])
loss = lasagne.objectives.categorical_crossentropy(
prediction, target_var)
loss = loss.mean(
) + self.reg * lasagne.regularization.regularize_network_params(
net['prob'], lasagne.regularization.l2)
train_acc = T.mean(T.eq(T.argmax(prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Create parameter update expressions with fine tuning
updates = {}
for name, layer in net.items():
layer_params = layer.get_params(trainable=True)
if name == 'fc1000' or name == 'prob':
layer_lr = self.lr
else:
layer_lr = self.lr * self.finetuning
layer_updates = lasagne.updates.adam(loss,
layer_params,
learning_rate=layer_lr)
updates.update(layer_updates)
updates = collections.OrderedDict(updates)
# Create a loss expression for validation/testing.
test_prediction = lasagne.layers.get_output(net['prob'],
deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(
test_prediction, target_var)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile training and validation functions
train_fn = theano.function([input_var, target_var], [loss, train_acc],
updates=updates)
val_fn = theano.function([input_var, target_var],
[test_loss, test_acc])
test_fn = theano.function([input_var], test_prediction)
# ======================================================================
# Training routine
# ======================================================================
print("Starting training...")
track = {
'train_err': [],
'train_acc': [],
'val_err': [],
'val_acc': []
}
if display:
fig, (ax1, ax2) = plt.subplots(1, 2)
line1, = ax1.plot([], [], 'r-')
line2, = ax2.plot([], [], 'r-')
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Training loss')
ax1.set_yscale('log')
ax1.set_title('Training loss')
ax2.set_xlabel('Epochs')
ax2.set_ylabel('Validation loss')
ax2.set_yscale('log')
ax2.set_title('Validation loss')
# Batchsize and augmentation parameters
if aug_params is None:
aug_params = {}
train_batchsize = min(len(y_train), self.batchsize)
train_aug_params = aug_params.copy()
train_aug_params.update({'mode': 'standard'})
if X_val is not None:
val_batchsize = min(len(y_val), self.batchsize)
val_aug_params = aug_params.copy()
val_aug_params.update({'mode': 'minimal', 'tags': None})
for epoch in range(self.num_epochs):
start_time = time.time()
# Learning rate schedule decay
if epoch in self.lr_decay_schedule:
self.lr.set_value(self.lr.get_value() * self.lr_decay)
print('############# Leaning rate: {} ####################'
).format(self.lr.get_value())
# Full pass over training data
train_err, train_batches = 0, 0
for batch in iterate_minibatches(X_train,
y_train,
train_batchsize,
shuffle=True,
**train_aug_params):
inputs, targets = batch[0], batch[1]
tmp_train_err, tmp_train_acc = train_fn(inputs, targets)
track['train_err'].append(tmp_train_err)
track['train_acc'].append(tmp_train_acc)
train_err += tmp_train_err
train_batches += 1
print 'Training epoch {} - {:.1f}% completed | Loss: {:.4f} ; Accuracy: {:.1f}%'.format(
epoch,
train_batches * self.batchsize * 100. / len(y_train),
float(tmp_train_err),
float(tmp_train_acc) * 100)
if np.isnan(train_err):
print(
'Your net exploded, try decreasing the learning rate.')
return None
# Full pass over the validation data (if any)
if X_val is not None:
val_err, val_batches = 0, 0
for batch in iterate_minibatches(X_val,
y_val,
val_batchsize,
shuffle=False,
**val_aug_params):
inputs, targets = batch[0], batch[1]
tmp_val_err, tmp_val_acc = val_fn(inputs, targets)
track['val_err'].append(tmp_val_err)
track['val_acc'].append(tmp_val_acc)
val_err += tmp_val_err
val_batches += 1
print 'Validation epoch {} - {:.1f}% completed | Loss: {:.4f} ; Accuracy: {:.1f}%'.format(
epoch,
val_batches * self.batchsize * 100. / len(y_val),
float(tmp_val_err),
float(tmp_val_acc) * 100)
# Print the results for this epoch
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, self.num_epochs,
time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err /
train_batches))
if X_val is not None:
print(" validation loss:\t\t{:.6f}".format(val_err /
val_batches))
# Display training and validation accuracy in plot
if display:
line1.set_xdata(np.append(line1.get_xdata(), epoch))
line1.set_ydata(
np.append(line1.get_ydata(), train_err / train_batches))
ax1.relim(), ax1.autoscale_view()
if X_val is not None:
line2.set_xdata(np.append(line2.get_xdata(), epoch))
line2.set_ydata(
np.append(line2.get_ydata(), val_err / val_batches))
ax2.relim(), ax2.autoscale_view()
fig.canvas.draw()
# Save training information and net parameters
print("Saving the model parameters and training information ...")
train_info = {
'training_params': {
'output_dim': self.output_dim,
'lr_init': self.lr_init,
'lr_decay': float(self.lr_decay),
'lr_schedule': self.lr_decay_schedule.tolist(),
'reg': self.reg,
'num_epochs': self.num_epochs,
'batchsize': self.batchsize,
'finetuning': self.finetuning
}
}
a = inspect.getargspec(data_augmentation)
augmentation_params = dict(
zip(a.args[-len(a.defaults):],
a.defaults)) # default augmentation params
augmentation_params.update(aug_params) # update with user's choice
for k, v in augmentation_params.items():
if type(v) == np.ndarray:
augmentation_params[k] = np.array(v).tolist()
train_info.update({'augmentation_params': augmentation_params})
for k, v in track.items():
track[k] = np.array(v).tolist()
train_info.update(track)
if save_model:
filename = 'resnet50_' + str(self.output_dim) + 'classes_' + str(
self.num_epochs) + 'epochs'
with open(
os.path.join(homedir, 'conus_classification',
'training_info', filename + '.json'),
'w') as outfile:
json.dump(train_info, outfile)
np.savez(
os.path.join(homedir, 'conus_classification',
'training_weights', filename + '.npz'),
*lasagne.layers.get_all_param_values(net['prob']))
return test_fn
def __init__(self, *args, **kwargs):
self.k = TT.ivector('k')
super(Classifier, self).__init__(*args, **kwargs)
self.y = self.softmax(self.y)
def single_layer_lstm(n_in, n_out):
Wxb = theano.shared(np.random.randn(n_in, n_out), )
Whb = theano.shared(np.random.randn(n_out, n_out), )
bb = theano.shared(np.random.randn(n_out))
Wxi = theano.shared(np.random.randn(n_in, n_out), )
Whi = theano.shared(np.random.randn(n_out, n_out), )
bi = theano.shared(np.random.randn(n_out))
Wxf = theano.shared(np.random.randn(n_in, n_out), )
Whf = theano.shared(np.random.randn(n_out, n_out), )
bf = theano.shared(np.random.randn(n_out))
Wxo = theano.shared(np.random.randn(n_in, n_out), )
Who = theano.shared(np.random.randn(n_out, n_out), )
bo = theano.shared(np.random.randn(n_out))
Wo = theano.shared(np.random.randn(n_out, n_out))
bout = theano.shared(np.random.randn(n_out))
params = [Wxb, Whb, bb, Wxi, Whi, bi, Wxf, Whf, bf, Wxo, Who, bo, Wo, bout]
def step(x,htm1,ctm1,Wxb,Whb,bb,\
Wxi,Whi,bi,\
Wxf,Whf,bf,\
Wxo,Who,bo,Wo,bout):
z = T.tanh(T.dot(x, Wxb) + T.dot(htm1, Whb) + bb)
i = T.nnet.sigmoid(T.dot(x, Wxi) + T.dot(htm1, Whi) + bi)
f = T.nnet.sigmoid(T.dot(x, Wxf) + T.dot(htm1, Whf) + bf)
c = i * z + f * ctm1
o = T.nnet.sigmoid(T.dot(x, Wxo) + T.dot(htm1, Who) + bo)
h = o * T.tanh(c)
y = T.dot(h, Wo) + bout
return [h, c, y]
X = T.matrix()
h0 = T.vector()
c0 = T.vector()
yt = T.ivector()
lr = T.scalar()
mom = T.scalar()
[h, c, y], _ = theano.scan(step,
sequences=X,
outputs_info=[h0, c0, None],
non_sequences=[
Wxb, Whb, bb, Wxi, Whi, bi, Wxf, Whf, bf,
Wxo, Who, bo, Wo, bout
])
yout = T.nnet.softmax(y)
L2 = T.scalar()
L2 = 0
for param in params:
L2 += (param**2).sum()
L2 = 0.001 * L2
def loss(y_pred, y_true):
return -T.mean(T.log(y_pred)[T.arange(y_true.shape[0]), y_true])
#oloss = loss(yout,yt)
#cost = theano.function( [X,h0,c0,yt], oloss )
funch = theano.function([X, h0, c0], c)
funcy = theano.function([X, h0, c0], y)
oloss = loss(yout, yt) + L2
cost = loss(yout, yt)
gparams = []
for param in params:
gparams.append(T.grad(oloss, param))
# zip just concatenate two lists
updates_t = {}
for param in params:
updates_t[param] = theano.shared(value=np.zeros(
param.get_value(borrow=True).shape, dtype=theano.config.floatX),
name='updates')
updates = {}
for param, gparam in zip(params, gparams):
weight_update = updates_t[param]
upd = mom * weight_update - lr * gparam
updates[weight_update] = upd
updates[param] = param + upd
"""
for param, gparam in zip(params, gparams):
#mparam = theano.shared(param.get_value()*0.)
upd = -lr*gparam# + mom*mparam# - 0.01*param# +
#updates[mparam] = upd
updates[param] = param + upd
"""
"""
weight_update = updates[param]
upd = -lr * gparam - 0.01*param
updates[weight_update] = upd
updates[param] = param + upd
"""
#gWxo = T.grad(oloss,Wxo)
#fgradwxo = theano.function( [X,h0,c0,yt], gWxo )
trainer = theano.function([X, h0, c0, yt, lr, mom], [cost],
updates=updates)
return funcy, trainer
def trainConvNet(data_xy, inp_dim =10, n_epochs = 3, nkerns=[5, 10], batch_size=500, learning_rate=0.1):
with open("metrics.txt", "a") as f:
f.write("**********\n")
f.write("Learning rate: {0}\n".format(learning_rate))
train_x, train_y, test_x, test_y, valid_x, valid_y = data_xy
n_train_batches = train_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = valid_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_x.get_value(borrow=True).shape[0] / batch_size
print '...building the model'
kern0_dim = 3
kern1_dim = 2
pool0_dim = 2
pool1_dim = 1
if inp_dim==20:
kern0_dim = 3
kern1_dim = 2
pool0_dim = 2
pool1_dim = 1
if inp_dim==24:
kern0_dim = 5
kern1_dim = 3
pool0_dim = 2
pool1_dim = 1
if inp_dim==30:
kern0_dim = 7
kern1_dim = 5
pool0_dim = 2
pool1_dim = 1
index = T.lscalar()
x = T.tensor4('x')
y = T.ivector('y')
rng = numpy.random.RandomState(23455)
layer0_input = x.reshape((batch_size, THREE, inp_dim, inp_dim))
layer0 = LeNetConvPoolLayer(
rng,
input = layer0_input,
image_shape=(batch_size, THREE, inp_dim, inp_dim),
filter_shape=(nkerns[0], 3, kern0_dim, kern0_dim),
poolsize=(pool0_dim, pool0_dim)
)
inp1_dim = (inp_dim-kern0_dim+1)/pool0_dim
layer1 = LeNetConvPoolLayer(
rng,
input = layer0.output,
image_shape=(batch_size, nkerns[0], inp1_dim, inp1_dim),
filter_shape=(nkerns[1], nkerns[0], kern1_dim, kern1_dim),
poolsize=(pool1_dim, pool1_dim)
)
layer2_input = layer1.output.flatten(2)
inp2_dim = (inp1_dim-kern1_dim+1)/pool1_dim
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1]*inp2_dim*inp2_dim,
n_out=300,
activation=T.tanh
)
layer3 = LogisticRegression(input=layer2.output, n_in=300, n_out=10)
cost = layer3.negative_log_likelihood(y)
test_model = theano.function([index], layer3.errors(y), givens={
x: test_x[index*batch_size: (index+1)*batch_size],
y: test_y[index*batch_size: (index+1)*batch_size]
})
validate_model = theano.function([index], layer3.errors(y), givens={
x: valid_x[index*batch_size: (index+1)*batch_size],
y: valid_y[index*batch_size: (index+1)*batch_size]
})
params = layer3.params + layer2.params + layer1.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_x[index*batch_size: (index+1)*batch_size],
y: train_y[index*batch_size: (index+1)*batch_size]
})
print 'training... '
patience = 10000
patience_increase = 2
improvement_threshold = 0.995
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
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:
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 %%\n' %(epoch, minibatch_index + 1, n_train_batches, this_validation_loss * 100.))
f.write("Epoch: {0}\n".format(epoch))
f.write("Validation loss: {0}\n".format(this_validation_loss*100))
f.write("Cost: {0}\n".format(cost_ij))
if this_validation_loss < best_validation_loss:
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
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.))
if patience<=iter:
done_looping=True
break
end_time = timeit.default_timer()
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.))
print ('saving params for patch width: %i...' %(inp_dim))
save_file = open('param'+str(inp_dim)+'.pkl', 'wb')
W0 = layer0.params[0]; b0 = layer0.params[1]
W1 = layer1.params[0]; b1 = layer1.params[1]
cPickle.dump(W0.get_value(borrow=True), save_file, -1)
cPickle.dump(b0.get_value(borrow=True), save_file, -1)
cPickle.dump(W1.get_value(borrow=True), save_file, -1)
cPickle.dump(b1.get_value(borrow=True), save_file, -1)
save_file.close()
import theano
from confusionmatrix import ConfusionMatrix
from lasagne.objectives import *
from lasagne.updates import *
import theano.tensor as T
from theano.tensor import *
from theano.tensor.signal import downsample
import lasagne
import numpy as np
import try_DP as DP
from theano.tensor import nnet
import lasagne.layers.dnn
dtensor5 = TensorType('float32', (False,)*5)
input_var = T.ftensor4('XY')
target_var = T.ivector('Y_train')
x1 = T.matrix('x1')
PS = 29
# Build Neural Network:
# Conv Net XY Plane
input = lasagne.layers.InputLayer((None, 1, PS, PS), input_var=input_var)
l_conv_1 = lasagne.layers.dnn.Conv2DDNNLayer(input, 20, (9,9))
l_maxpool_1 = lasagne.layers.dnn.Pool2DDNNLayer(l_conv_1, (3,3))
l_conv_2 = lasagne.layers.dnn.Conv2DDNNLayer(l_maxpool_1, 20,(5,5))
l_conv_3 = lasagne.layers.dnn.Conv2DDNNLayer(l_conv_2, 20, (3,3))
def train_rep(learning_rate=0.002,
L1_reg=0.0002,
L2_reg=0.005,
n_epochs=200,
nkerns=[20, 50],
batch_size=25):
rng = numpy.random.RandomState(23455)
train_dir = '../out/h5/'
valid_dir = '../out/h5/'
weights_dir = './weights/'
print '... load input data'
filename = train_dir + 'rep_train_data_1.gzip.h5'
datasets = load_initial_data(filename)
train_set_x, train_set_y, shared_train_set_y = datasets
filename = valid_dir + 'rep_valid_data_1.gzip.h5'
datasets = load_initial_data(filename)
valid_set_x, valid_set_y, shared_valid_set_y = datasets
mydatasets = load_initial_test_data()
test_set_x, test_set_y, shared_test_set_y, valid_ds = mydatasets
# compute number of minibatches for training, validation and testing
n_all_train_batches = 30000
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_all_train_batches /= batch_size
n_train_batches /= batch_size
n_valid_batches /= batch_size
# 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
# image size
layer0_w = 50
layer0_h = 50
layer1_w = (layer0_w - 4) / 2
layer1_h = (layer0_h - 4) / 2
layer2_w = (layer1_w - 2) / 2
layer2_h = (layer1_h - 2) / 2
layer3_w = (layer2_w - 2) / 2
layer3_h = (layer2_h - 2) / 2
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# image sizes
batchsize = batch_size
in_channels = 20
in_width = 50
in_height = 50
#filter sizes
flt_channels = 40
flt_time = 20
flt_width = 5
flt_height = 5
signals_shape = (batchsize, in_channels, in_height, in_width)
filters_shape = (flt_channels, in_channels, flt_height, flt_width)
layer0_input = x.reshape(signals_shape)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=signals_shape,
filter_shape=filters_shape,
poolsize=(2, 2))
# TODO: incase of flt_time < in_time the output dimension will be different
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, flt_channels,
layer1_w, layer1_h),
filter_shape=(60, flt_channels, 3, 3),
poolsize=(2, 2))
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(batch_size, 60, layer2_w,
layer2_h),
filter_shape=(90, 60, 3, 3),
poolsize=(2, 2))
layer3_input = layer2.output.flatten(2)
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=90 * layer3_w * layer3_h,
n_out=500,
activation=T.tanh)
layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=8)
classify = theano.function(
[index],
outputs=layer4.get_output_labels(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],
layer4.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 = layer4.params + layer3.params + layer2.params + layer1.params + layer0.params
# symbolic Theano variable that represents the L1 regularization term
L1 = T.sum(abs(layer4.params[0])) + T.sum(abs(layer3.params[0])) + T.sum(
abs(layer2.params[0])) + T.sum(abs(layer1.params[0])) + T.sum(
abs(layer0.params[0]))
# symbolic Theano variable that represents the squared L2 term
L2_sqr = T.sum(layer4.params[0]**2) + T.sum(layer3.params[0]**2) + T.sum(
layer2.params[0]**2) + T.sum(layer1.params[0]**2) + T.sum(
layer0.params[0]**2)
# the loss
cost = layer4.negative_log_likelihood(y) + L1_reg * L1 + L2_reg * L2_sqr
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
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'
start_time = time.clock()
epoch = 0
done_looping = False
cost_ij = 0
train_files_num = 600
val_files_num = 100
startc = time.clock()
while (epoch < n_epochs) and (not done_looping):
endc = time.clock()
print('epoch %i, took %.2f minutes' % \
(epoch, (endc - startc) / 60.))
startc = time.clock()
epoch = epoch + 1
for nTrainSet in xrange(1, train_files_num + 1):
# load next train data
if nTrainSet % 50 == 0:
print 'training @ nTrainSet = ', nTrainSet, ', cost = ', cost_ij
filename = train_dir + 'rep_train_data_' + str(
nTrainSet) + '.gzip.h5'
datasets = load_next_data(filename)
ns_train_set_x, ns_train_set_y = datasets
train_set_x.set_value(ns_train_set_x, borrow=True)
shared_train_set_y.set_value(numpy.asarray(
ns_train_set_y, dtype=theano.config.floatX),
borrow=True)
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
# train
for minibatch_index in xrange(n_train_batches):
# training itself
# --------------------------------------
cost_ij = train_model(minibatch_index)
# -------------------------
# at the end of each epoch run validation
this_validation_loss = 0
for nValSet in xrange(1, val_files_num + 1):
filename = valid_dir + 'rep_valid_data_' + str(
nValSet) + '.gzip.h5'
datasets = load_next_data(filename)
ns_valid_set_x, ns_valid_set_y = datasets
valid_set_x.set_value(ns_valid_set_x, borrow=True)
shared_valid_set_y.set_value(numpy.asarray(
ns_valid_set_y, dtype=theano.config.floatX),
borrow=True)
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_valid_batches /= batch_size
# 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)
this_validation_loss /= (val_files_num)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches, \
this_validation_loss * 100.))
# save snapshots
print 'saving weights state, epoch = ', epoch
f = file(weights_dir + 'weights_epoch' + str(epoch) + '.save', 'wb')
state_L0 = layer0.__getstate__()
cPickle.dump(state_L0, f, protocol=cPickle.HIGHEST_PROTOCOL)
state_L1 = layer1.__getstate__()
cPickle.dump(state_L1, f, protocol=cPickle.HIGHEST_PROTOCOL)
state_L2 = layer2.__getstate__()
cPickle.dump(state_L2, f, protocol=cPickle.HIGHEST_PROTOCOL)
state_L3 = layer3.__getstate__()
cPickle.dump(state_L3, f, protocol=cPickle.HIGHEST_PROTOCOL)
state_L4 = layer4.__getstate__()
cPickle.dump(state_L4, f, protocol=cPickle.HIGHEST_PROTOCOL)
f.close()
end_time = time.clock()
print('Optimization complete.')
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def __init__(self,
d_v,
d_e,
d_t,
optimizer,
optimizer_args,
np_rng,
th_rng,
n_classes=0,
encoder_layers=1,
generator_layers=0,
generator_transform=None,
use_interactions=False,
clip_gradients=False,
init_bias=None,
train_bias=False,
scale=6.0,
encode_labels=False,
l1_inter_factor=1.0,
time_penalty=False,
encoder_shortcut=False,
generator_shortcut=False):
self.d_v = d_v # vocabulary size
self.d_e = d_e # dimensionality of encoder
self.d_t = d_t # number of topics
self.n_classes = n_classes # number of classes
assert encoder_layers == 1 or encoder_layers == 2
self.n_encoder_layers = encoder_layers
assert generator_layers == 0 or generator_layers == 1 or generator_layers == 2 or generator_layers == 4
self.n_generator_layers = generator_layers
# set various options
self.generator_transform = generator_transform # transform to apply after the generator
self.use_interactions = use_interactions # use interactions between topics and labels
self.encode_labels = encode_labels # feed labels into the encoder
self.l1_inter_factor = l1_inter_factor # factor by which to multiply L1 penalty on interactions
self.encoder_shortcut = encoder_shortcut
self.generator_shortcut = generator_shortcut
# create parameter matrices and biases
self.W_encoder_1 = common_theano.init_param('W_encoder_1', (d_e, d_v),
np_rng,
scale=scale)
self.b_encoder_1 = common_theano.init_param('b_encoder_1', (d_e, ),
np_rng,
scale=0.0)
if n_classes > 1:
self.W_encoder_label = common_theano.init_param('W_encoder_label',
(d_e, n_classes),
np_rng,
scale=scale)
else:
self.W_encoder_label = common_theano.init_param(
'W_encoder_label', (d_e, n_classes),
np_rng,
values=np.zeros((d_e, n_classes), dtype=np.float32))
self.W_encoder_2 = common_theano.init_param('W_encoder_2', (d_e, d_e),
np_rng,
scale=scale)
self.b_encoder_2 = common_theano.init_param('b_encoder_2', (d_e, ),
np_rng,
scale=0.0)
self.W_encoder_shortcut = common_theano.init_param(
'W_encoder_shortcut', (d_e, d_v), np_rng, scale=scale)
self.W_mu = common_theano.init_param('W_mu', (d_t, d_e),
np_rng,
scale=scale)
self.b_mu = common_theano.init_param('b_mu', (d_t, ),
np_rng,
scale=0.0)
self.W_sigma = common_theano.init_param('W_sigma', (d_t, d_e),
np_rng,
scale=scale,
values=np.zeros((d_t, d_e)))
self.b_sigma = common_theano.init_param('b_sigma', (d_t, ),
np_rng,
scale=0.0,
values=np.array([-4] * d_t))
self.W_generator_1 = common_theano.init_param('W_generator_1',
(d_t, d_t),
np_rng,
scale=scale)
self.b_generator_1 = common_theano.init_param('b_generator_1', (d_t, ),
np_rng,
scale=0.0)
self.W_generator_2 = common_theano.init_param('W_generator_2',
(d_t, d_t),
np_rng,
scale=scale)
self.b_generator_2 = common_theano.init_param('b_generator_2', (d_t, ),
np_rng,
scale=0.0)
self.W_generator_3 = common_theano.init_param('W_generator_3',
(d_t, d_t),
np_rng,
scale=scale)
self.b_generator_3 = common_theano.init_param('b_generator_3', (d_t, ),
np_rng,
scale=0.0)
self.W_generator_4 = common_theano.init_param('W_generator_4',
(d_t, d_t),
np_rng,
scale=scale)
self.b_generator_4 = common_theano.init_param('b_generator_4', (d_t, ),
np_rng,
scale=0.0)
self.W_decoder = common_theano.init_param('W_decoder', (d_v, d_t),
np_rng,
scale=scale)
self.b_decoder = common_theano.init_param('b_decoder', (d_v, ),
np_rng,
scale=0.0)
self.W_decoder_label = common_theano.init_param('W_decoder_label',
(d_v, n_classes),
np_rng,
scale=scale)
self.W_decoder_inter = common_theano.init_param('W_decoder_inter',
(d_v, d_t * n_classes),
np_rng,
scale=scale)
# set the decoder bias to the background frequency
if init_bias is not None:
self.b_decoder = common_theano.init_param('b_decoder', (d_v, ),
np_rng,
values=init_bias)
# create basic sets of parameters which we will use to tell the model what to update
self.params = [
self.W_encoder_1, self.b_encoder_1, self.W_mu, self.b_mu,
self.W_sigma, self.b_sigma, self.W_decoder
]
self.param_shapes = [(d_e, d_v), (d_e, ), (d_t, d_e), (d_t, ),
(d_t, d_e), (d_t, ), (d_v, d_t)]
self.encoder_params = [
self.W_encoder_1, self.b_encoder_1, self.W_mu, self.b_mu,
self.W_sigma, self.b_sigma
]
self.encoder_param_shapes = [(d_e, d_v), (d_e, ), (d_t, d_e), (d_t, ),
(d_t, d_e), (d_t, )]
self.generator_params = []
self.generator_param_shapes = []
# add additional parameters to sets, depending on configuration
if train_bias:
self.params.append(self.b_decoder)
self.param_shapes.append((d_v, ))
self.decoder_params = [self.W_decoder, self.b_decoder]
self.decoder_param_shapes = [(d_v, d_t), (d_v, )]
else:
self.decoder_params = [self.W_decoder]
self.decoder_param_shapes = [(d_v, d_t)]
# add parameters for labels (covariates)
if self.n_classes > 1:
self.params.append(self.W_decoder_label)
self.param_shapes.append((d_v, n_classes))
self.decoder_params.extend([self.W_decoder_label])
self.decoder_param_shapes.extend([(d_v, n_classes)])
if use_interactions:
self.params.append(self.W_decoder_inter)
self.param_shapes.append((d_v, d_t * n_classes))
self.decoder_params.extend([self.W_decoder_inter])
self.decoder_param_shapes.extend([(d_v, d_t * n_classes)])
if encode_labels:
self.params.append(self.W_encoder_label)
self.param_shapes.append((d_e, n_classes))
self.encoder_params.extend([self.W_encoder_label])
self.encoder_param_shapes.extend([(d_e, n_classes)])
self.label_only_params = [self.W_decoder_label]
self.label_only_param_shapes = [(d_v, n_classes)]
# add encoder parameters depending on number of layers
if self.n_encoder_layers > 1:
self.params.extend([self.W_encoder_2, self.b_encoder_2])
self.param_shapes.extend([(d_e, d_e), (d_e, )])
self.encoder_params.extend([self.W_encoder_2, self.b_encoder_2])
self.encoder_param_shapes.extend([(d_e, d_e), (d_e, )])
if self.encoder_shortcut:
self.params.extend([self.W_encoder_shortcut])
self.param_shapes.extend([(d_e, d_v)])
self.encoder_params.extend([self.W_encoder_shortcut])
self.encoder_param_shapes.extend([(d_e, d_v)])
# add generator parameters depending on number of layers
if self.n_generator_layers > 0:
self.params.extend([self.W_generator_1, self.b_generator_1])
self.param_shapes.extend([(d_t, d_t), (d_t, )])
self.generator_params.extend(
[self.W_generator_1, self.b_generator_1])
self.generator_param_shapes.extend([(d_t, d_t), (d_t, )])
if self.n_generator_layers > 1:
self.params.extend([self.W_generator_2, self.b_generator_2])
self.param_shapes.extend([(d_t, d_t), (d_t, )])
self.generator_params.extend(
[self.W_generator_2, self.b_generator_2])
self.generator_param_shapes.extend([(d_t, d_t), (d_t, )])
if self.n_generator_layers > 2:
self.params.extend([
self.W_generator_3, self.b_generator_3, self.W_generator_4,
self.b_generator_4
])
self.param_shapes.extend([(d_t, d_t), (d_t, ), (d_t, d_t),
(d_t, )])
self.generator_params.extend([
self.W_generator_3, self.b_generator_3, self.W_generator_4,
self.b_generator_4
])
self.generator_param_shapes.extend([(d_t, d_t), (d_t, ),
(d_t, d_t), (d_t, )])
# declare variables that will be given as inputs to functions to be declared below
x = T.vector('x', dtype=theano.config.floatX
) # normalized vector of counts for one item
y = T.vector(
'y', dtype=theano.config.floatX) # vector of labels for one item
indices = T.ivector(
'x') # vector of vocab indices (easier to evaluate log prob)
lr = T.fscalar('lr') # learning rate
l1_strength = T.fscalar('l1_strength') # l1_strength
kl_strength = T.fscalar('kl_strength') # l1_strength
n_words = T.shape(indices)
# the two variables below are just for debugging
n_words_print = theano.printing.Print('n_words')(
T.shape(indices)[0]) # for debugging
x_sum = theano.printing.Print('x_sum')(T.sum(x)) # for debugging
# encode one item to mean and variance vectors
mu, log_sigma_sq = self.encoder(x, y)
# take a random sample from the corresponding multivariate normal
h = self.sampler(mu, log_sigma_sq, th_rng)
# compute the KL divergence from the prior
KLD = -0.5 * T.sum(1 + log_sigma_sq - T.square(mu) -
T.exp(log_sigma_sq))
# generate a document representation of dimensionality == n_topics
r = self.generator(h)
# decode back into a distribution over the vocabulary
p_x_given_h = self.decoder(r, y)
# evaluate the likelihood
nll_term = -T.sum(
T.log(p_x_given_h[T.zeros(n_words, dtype='int32'), indices]) +
1e-32)
# compute the loss
loss = nll_term + KLD * kl_strength
# add an L1 penalty to the decoder terms
if time_penalty and n_classes > 1:
penalty = common_theano.col_diff_L1(l1_strength,
self.W_decoder_label,
n_classes)
else:
penalty = common_theano.L1(l1_strength, self.W_decoder)
if n_classes > 1:
penalty += common_theano.L1(l1_strength, self.W_decoder_label)
if use_interactions:
penalty += common_theano.L1(
l1_strength * self.l1_inter_factor,
self.W_decoder_inter)
# declare some alternate function for decoding from the mean
r_mu = self.generator(mu)
p_x_given_x = self.decoder(r_mu, y)
nll_term_mu = -T.sum(
T.log(p_x_given_x[T.zeros(n_words, dtype='int32'), indices]) +
1e-32)
# declare some alternate functions for pretraining from a fixed document representation (r)
pretrain_r = T.vector('pretrain_r', dtype=theano.config.floatX)
p_x_given_pretrain_h = self.decoder(pretrain_r, y)
pretrain_loss = -T.sum(
T.log(p_x_given_pretrain_h[T.zeros(n_words, dtype='int32'),
indices]) + 1e-32)
# declare some alternate functions for only using labels
p_x_given_y_only = self.decoder_label_only(y)
nll_term_y_only = -T.sum(
T.log(p_x_given_y_only[T.zeros(n_words, dtype='int32'), indices]) +
1e-32)
# compute gradients
gradients = [
T.cast(T.grad(loss + penalty, param, disconnected_inputs='warn'),
dtype=theano.config.floatX) for param in self.params
]
encoder_gradients = [
T.cast(T.grad(loss, param, disconnected_inputs='warn'),
dtype=theano.config.floatX) for param in self.encoder_params
]
generator_gradients = [
T.cast(T.grad(loss, param, disconnected_inputs='warn'),
dtype=theano.config.floatX)
for param in self.generator_params
]
decoder_gradients = [
T.cast(T.grad(loss + penalty, param, disconnected_inputs='warn'),
dtype=theano.config.floatX) for param in self.decoder_params
]
pretrain_gradients = [
T.cast(T.grad(pretrain_loss + penalty,
param,
disconnected_inputs='warn'),
dtype=theano.config.floatX) for param in self.decoder_params
]
label_only_gradients = [
T.cast(T.grad(nll_term_y_only + penalty,
param,
disconnected_inputs='warn'),
dtype=theano.config.floatX)
for param in self.label_only_params
]
# optionally clip gradients
if clip_gradients:
gradients = common_theano.clip_gradients(gradients, 5)
encoder_gradients = common_theano.clip_gradients(
encoder_gradients, 5)
generator_gradients = common_theano.clip_gradients(
generator_gradients, 5)
decoder_gradients = common_theano.clip_gradients(
decoder_gradients, 5)
pretrain_gradients = common_theano.clip_gradients(
pretrain_gradients, 5)
label_only_gradients = common_theano.clip_gradients(
label_only_gradients, 5)
# create the updates for various sets of parameters
updates = optimizer(self.params, self.param_shapes, gradients, lr,
optimizer_args)
encoder_updates = optimizer(self.encoder_params,
self.encoder_param_shapes,
encoder_gradients, lr, optimizer_args)
generator_updates = optimizer(self.generator_params,
self.generator_param_shapes,
generator_gradients, lr, optimizer_args)
decoder_updates = optimizer(self.decoder_params,
self.decoder_param_shapes,
decoder_gradients, lr, optimizer_args)
other_updates = optimizer(
self.encoder_params + self.generator_params,
self.encoder_param_shapes + self.generator_param_shapes,
encoder_gradients + generator_gradients, lr, optimizer_args)
pretrain_updates = optimizer(self.decoder_params,
self.decoder_param_shapes,
pretrain_gradients, lr, optimizer_args)
label_only_updates = optimizer(self.label_only_params,
self.label_only_param_shapes,
label_only_gradients, lr,
optimizer_args)
# declare the available methods for this class
self.test_input = theano.function(inputs=[x, indices],
outputs=[n_words_print, x_sum])
self.train = theano.function(
inputs=[x, indices, y, lr, l1_strength, kl_strength],
outputs=[nll_term, KLD, penalty],
updates=updates,
on_unused_input='ignore')
self.train_encoder = theano.function(
inputs=[x, indices, y, lr, l1_strength, kl_strength],
outputs=[nll_term, KLD, penalty],
updates=encoder_updates,
on_unused_input='ignore')
self.train_generator = theano.function(
inputs=[x, indices, y, lr, l1_strength, kl_strength],
outputs=[nll_term, KLD, penalty],
updates=generator_updates,
on_unused_input='ignore')
self.train_decoder = theano.function(
inputs=[x, indices, y, lr, l1_strength, kl_strength],
outputs=[nll_term, KLD, penalty],
updates=decoder_updates,
on_unused_input='ignore')
self.train_not_decoder = theano.function(
inputs=[x, indices, y, lr, l1_strength, kl_strength],
outputs=[nll_term, KLD, penalty],
updates=other_updates,
on_unused_input='ignore')
self.pretrain_decoder = theano.function(
inputs=[indices, y, pretrain_r, lr, l1_strength, kl_strength],
outputs=[pretrain_loss],
updates=pretrain_updates,
on_unused_input='ignore')
self.encode = theano.function(inputs=[x, y],
outputs=[mu, log_sigma_sq],
on_unused_input='ignore')
self.decode = theano.function(inputs=[pretrain_r, y],
outputs=[p_x_given_pretrain_h],
on_unused_input='ignore')
self.sample = theano.function(inputs=[x, y],
outputs=h,
on_unused_input='ignore')
self.get_mean_doc_rep = theano.function(inputs=[x, y],
outputs=r_mu,
on_unused_input='ignore')
self.encode_and_decode = theano.function(inputs=[x, y],
outputs=p_x_given_x,
on_unused_input='ignore')
self.neg_log_likelihood = theano.function(inputs=[x, indices, y],
outputs=[nll_term, KLD],
on_unused_input='ignore')
self.neg_log_likelihood_mu = theano.function(
inputs=[x, indices, y],
outputs=[nll_term_mu, KLD],
on_unused_input='ignore')
self.train_label_only = theano.function(
inputs=[indices, y, lr, l1_strength],
outputs=[nll_term_y_only, penalty],
updates=label_only_updates)
self.neg_log_likelihood_label_only = theano.function(
inputs=[indices, y], outputs=nll_term_y_only)
def fit(self,
X,
Y,
Xvalid,
Yvalid,
learning_rate=1e-2,
mu=0.9,
decay=0.9,
epochs=10,
batch_sz=100,
show_fig=False):
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid = Xvalid.astype(np.float32)
Yvalid = Yvalid.astype(np.int32)
self.rng = RandomStreams()
# initialize hidden layers
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W = np.random.randn(M1, K) * np.sqrt(2.0 / M1)
b = np.zeros(K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
# set up theano functions and variables
thX = T.matrix('X')
thY = T.ivector('Y')
pY_train = self.forward_train(thX)
# this cost is for training
cost = -T.mean(T.log(pY_train[T.arange(thY.shape[0]), thY]))
updates = momentum_updates(cost, self.params, learning_rate, mu)
train_op = theano.function(inputs=[thX, thY], updates=updates)
# for evaluation and prediction
pY_predict = self.forward_predict(thX)
cost_predict = -T.mean(T.log(pY_predict[T.arange(thY.shape[0]), thY]))
prediction = self.predict(thX)
cost_predict_op = theano.function(inputs=[thX, thY],
outputs=[cost_predict, prediction])
n_batches = N // batch_sz
costs = []
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
train_op(Xbatch, Ybatch)
if j % 50 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c,
"error rate:", e)
if show_fig:
plt.plot(costs)
plt.show()
def train_conv_net(datasets,
U,
lr_decay=0.95,
img_w=300,
filter_hs=[3, 4, 5],
conv_non_linear="relu",
hidden_units=[100, 3],
shuffle_batch=True,
n_epochs=25,
sqr_norm_lim=9,
non_static=True,
batch_size=50,
activations=[Iden],
dropout_rate=[0.5]):
"""
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 = []
print 'starting loop'
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)
print 'defining params'
#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 'sizes: '
print 'test: '
print test_size
print '... training'
print 'n_train_batches: ' + str(n_train_batches)
epoch = 0
best_val_perf = 0
val_perf = 0
test_perf = 0
cost_epoch = 0
while (epoch < n_epochs):
print 'epoch: ' + str(epoch)
start_time = time.time()
epoch = epoch + 1
if shuffle_batch:
for minibatch_index in np.random.permutation(
range(n_train_batches)):
if minibatch_index >= n_train_batches: minibatch_index -= 1
print 'if: minibatch_index: ' + str(minibatch_index)
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
else:
for minibatch_index in xrange(n_train_batches):
if minibatch_index >= n_train_batches: minibatch_index -= 1
print 'else: minibatch_index: ' + str(minibatch_index)
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
def __init__(self,
numpy_rng,
theano_rng=None,
n_ins=183,
hidden_layers_sizes=[250, 250],
n_outs=1,
corruption_levels=[0.1, 0.1],
dropout_rate=0.1,
lambda1=0,
lambda2=0,
non_lin=None):
"""
:type numpy_rng: numpy.random.RandomState
:param numpy_rng: numpy random number generator used to draw initial
weights
:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
:param theano_rng: Theano random generator; if None is given one is
generated based on a seed drawn from `rng`
:type n_ins: int
:param n_ins: dimension of the input to the Model
:type hidden_layers_sizes: list of ints
:param hidden_layers_sizes: sizes of intermediate layers.
:type n_outs: int
:param n_outs: dimension of the output of the network. Always 1 for a
regression problem.
:type corruption_levels: list of float
:param corruption_levels: amount of corruption to use for each layer
:type dropout_rate: float
:param dropout_rate: probability of dropping a hidden unit
:type non_lin: function
:param non_lin: nonlinear activation function used in all layers
"""
# Initializes parameters.
self.hidden_layers = []
self.dA_layers = []
self.params = []
self.dropout_masks = []
self.n_layers = len(hidden_layers_sizes)
self.L1 = 0
self.L2_sqr = 0
self.n_hidden = hidden_layers_sizes[0]
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2**30))
# Allocates symbolic variables for the data.
self.x = T.matrix('x', dtype='float32')
self.o = T.ivector('o')
self.at_risk = T.ivector('at_risk')
self.is_train = T.iscalar('is_train')
self.masks = [
T.lmatrix('mask_' + str(i)) for i in range(self.n_layers)
]
# Linear cox regression with no hidden layers.
if self.n_layers == 0:
self.risk_layer = RiskLayer(input=self.x,
n_in=n_ins,
n_out=n_outs,
rng=numpy_rng)
else:
# Constructs the intermediate layers.
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.hidden_layers[-1].output
if dropout_rate > 0:
hidden_layer = DropoutHiddenLayer(
rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=non_lin,
dropout_rate=dropout_rate,
is_train=self.is_train,
mask=self.masks[i])
else:
hidden_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=non_lin)
# Adds the layer to the stack of layers.
self.hidden_layers.append(hidden_layer)
self.params.extend(hidden_layer.params)
# Constructs an autoencoder that shares weights with this layer.
dA_layer = dA(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
W=hidden_layer.W,
bhid=hidden_layer.b,
non_lin=non_lin)
self.dA_layers.append(dA_layer)
self.L1 += abs(hidden_layer.W).sum()
self.L2_sqr += (hidden_layer.W**2).sum()
# Adds a risk prediction layer on top of the stack.
self.risk_layer = RiskLayer(input=self.hidden_layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_outs,
rng=numpy_rng)
self.L1 += abs(self.risk_layer.W).sum()
self.L2_sqr += (self.risk_layer.W**2).sum()
self.params.extend(self.risk_layer.params)
self.regularizers = lambda1 * self.L1 + lambda2 * self.L2_sqr
def __init__(self,
batch_size,
kernels,
input_dimensions,
convolution_dimensions,
pool_sizes,
stride_sizes,
layer_pattern,
relu_pattern,
dropout_rate,
rng_seed=None,
base_learning_rate=0.05,
momentum=0.8,
learning_decay_per_epoch=0.91,
l2_norm=0,
name="default",
param_index=0,
address='',
n_epochs=200,
batch_normalization_pattern=None,
batch_norm_learning_rate=0.1,
batch_norm_decay_per_epoch=0.95,
batchnorm_vals_filename=None,
batchnorm_slide_percent=0.):
"""
batch_size - int - size of each batch
kernels - int array - number of general units each layer (incl. input/output)
input_dimensions - int array[2] - dimensions of input
convolution_dimensions - int array[2] array - dimensions of each convolution
pool_sizes - int array[2] array - dimensions of pooling for each convolution
stride_sizes - int array - length of strides for each convolutional layer (this overrides aspects of pooling behavior)
layer_pattern - ['I','C',...,'C','F',...,'F','O'] - indicates pattern of layers
relu_pattern - boolean array that describes if convolutional layers should be rectified; doesn't do anything for other types of layers (including input)
dropout_rate - float - rate of dropout for network weights
rng_seed - int - seed for random number generator; None defaults to random
base_learning_rate - floatX - initial learning rate
momentum - floatX - amount that learning rate carries over through iterations
learning_decay_per_epoch - floatX - factor for decreasing learning rate over epochs
name - string that describes the beginning of the filenames of the network pickle
param_index - integer determined a priori to index the param configurations and show it in the filename
batchnorm_vals_filename - has to be constructed by separate file; pre-defines mean and sd of each layer for a nn...might be preferred to use sliding instead, as
batchnorm_slide_percent - sort of like momentum, but for calculations of batch-normalization means and standard deviations
"""
#initialize arrays containing basic information and hyperparameters
self.layers = []
self.uses_batch_normalization = bool(batch_normalization_pattern)
self.batch_norm_pattern = batch_normalization_pattern
self.batchnorm_vals_filename = batchnorm_vals_filename
self.batchnorm_slide_percent = batchnorm_slide_percent
if not self.uses_batch_normalization:
self.batch_norm_pattern = [False for _ in relu_pattern]
self.address = address
#replace future instances of self.kernel
self.kernels = kernels
self.input_dimensions = input_dimensions
self.output_size = kernels[-1:][0]
self.inputs = []
self.batch_size = batch_size
self.x = x = T.ftensor4('x')
self.y = y = T.ivector('y')
self.rng = np.random.RandomState(rng_seed)
self.name = name
self.n_epochs = n_epochs
self.shapes = [(input_dimensions[0], input_dimensions[1])]
print "input shape: " + str(self.shapes)
self.convolution_dimensions = convolution_dimensions
self.rng_seed = rng_seed
self.layer_pattern = layer_pattern
self.current_batch_index = 0
self.batch_size = batch_size
self.pool_sizes = pool_sizes
self.stride_sizes = stride_sizes
self.relu_pattern = relu_pattern
#if the rate is a float, each layer has the same rate
if type(dropout_rate) == type(1.1):
dropout_rate = [dropout_rate for _ in layer_pattern]
self.dropout_rate = dropout_rate
self.learning_decay_per_epoch = learning_decay_per_epoch
self.l2_norm = l2_norm
#get some info from prepare_image_data.py
#files_list, outputs, y_dim = prepare_image_data.get_data()
#self.files_list = files_list
#self.y_dim = y_dim
#self.outputs=outputs
self.fetcher = prepare_image_data.fetcher(self.batch_size)
#indexing information
self.ratios = np.asarray([0.6, 0.2, 0.2])
self.index = index = T.lscalar()
#temporarily hardcoded
self.n_train_batches = 400
self.n_valid_batches = 120
self.n_test_batches = 120
self.cat_labels = self.fetcher.valid_names
self.y_dim = len(self.cat_labels)
self.momentum = theano.shared(np.float32(momentum))
self.base_learning_rate = np.float32(base_learning_rate)
self.learning_rate = theano.shared(
np.float32(base_learning_rate * (1 - momentum)))
self.index = index = T.lscalar()
self.momentum_raw = momentum
self.learning_rate_raw = self.learning_rate.get_value()
if self.uses_batch_normalization:
self.batch_norm_learning_rate_raw = batch_norm_learning_rate
self.batch_norm_learning_rate = theano.shared(
np.float32(self.batch_norm_learning_rate_raw))
self.epoch = 0
#initialize basic file shapes
#recent change: changed kernel_sizes to self.kernels
self.training_x = theano.shared(np.zeros(
shape=(batch_size, self.kernels[0], input_dimensions[0],
input_dimensions[1]),
dtype=theano.config.floatX),
borrow=True)
self.input = self.x.reshape((self.batch_size, self.kernels[0],
self.shapes[0][0], self.shapes[0][1]))
#updated database-based retrieval
self.training_y = theano.shared(np.zeros(shape=self.batch_size,
dtype=np.int32),
borrow=True)
self.testing_x = theano.shared(np.zeros(
shape=(self.batch_size, kernels[0], input_dimensions[0],
input_dimensions[1]),
dtype=theano.config.floatX),
borrow=True)
self.testing_y = theano.shared(np.zeros(shape=self.batch_size,
dtype=np.int32),
borrow=True)
self.validation_x = theano.shared(np.zeros(
shape=(self.batch_size, kernels[0], input_dimensions[0],
input_dimensions[1]),
dtype=theano.config.floatX),
borrow=True)
self.validation_y = theano.shared(np.zeros(shape=self.batch_size,
dtype=np.int32),
borrow=True)
#load fixed mean and sd values if file exists
if self.batchnorm_vals_filename <> None:
self.batchnorm_fixed_values = pickle.load(
self.batchnorm_vals_filename)
else:
self.batchnorm_fixed_values = [
None for _ in range(len(layer_pattern))
]
###begin creation of layers
#I = "input";C = "Convolutional"; F = "Fully-Connected", O = "Output"
for i, pattern in enumerate(layer_pattern):
if pattern == "I":
self.inputs.append(self.input)
print 'inserted input'
elif pattern == "C":
self.layers.append(
NetConvPoolLayer(
self.rng,
input = self.inputs[i-1],
image_shape=(
batch_size,kernels[i-1],
self.shapes[i-1][0],
self.shapes[i-1][1]
),
filter_shape=(
kernels[i],
kernels[i-1],
self.convolution_dimensions[i-1][0],
self.convolution_dimensions[i-1][1]),
poolsize = pool_sizes[i-1],
stride = stride_sizes[i-1],
dropout_percent = self.dropout_rate[i],
batch_norm = self.batch_norm_pattern[i],
batchnorm_slide_percent = self.batchnorm_slide_percent,
precalculated_batchnorm_values = self.\
batchnorm_fixed_values[i-1])
)
x_new = (
self.shapes[i-1][0] - self.convolution_dimensions[i-1][0] + \
1 - (pool_sizes[i-1][0] - stride_sizes[i-1][0]))/\
(stride_sizes[i-1][0]
)
y_new = (
self.shapes[i-1][1] - self.convolution_dimensions[i-1][1] + 1 -\
(pool_sizes[i-1][1] - stride_sizes[i-1][1]))/\
(stride_sizes[i-1][1]
)
self.inputs.append(self.layers[i - 1].output)
self.shapes.append((x_new, y_new))
print "self.shapes: " + str(self.shapes)
print 'added convolution layer'
elif pattern == "F":
if layer_pattern[i - 1] == "C":
next_input = self.inputs[i - 1].flatten(2)
else:
next_input = self.inputs[i - 1]
self.layers.append(
HiddenLayer(self.rng,
input=next_input,
n_in=kernels[i - 1] * self.shapes[i - 1][0] *
self.shapes[i - 1][1],
n_out=kernels[i],
activation=T.tanh,
dropout_rate=self.dropout_rate[i]))
self.inputs.append(self.layers[i - 1].output)
#the shape is only used to determine dimensions of the next layer
self.shapes.append((1, 1)) #see if this fixes issue
print 'added fully-connected hidden layer, shape=%s' %\
str(self.shapes[-1])
else:
if layer_pattern[i - 1] == "C":
next_input = self.inputs[i - 1].flatten(2)
else:
next_input = self.inputs[i - 1]
self.layers.append(
LogisticRegression(input=next_input,
n_in=kernels[i - 1],
n_out=self.output_size,
rng=self.rng,
dropout_rate=self.dropout_rate[i]))
last_index = i - 1
print 'added logistic layer'
zero = np.float32(0.)
self.L2_penalty = theano.shared(np.float32(l2_norm))
self.params = params = [param for layer in self.layers \
for param in layer.params]
self.cost = self.layers[last_index].negative_log_likelihood(self.y) +\
self.L2_penalty * (
T.sum([T.sum(self.layers[q].W * self.layers[q].W)\
for q in range(len(self.layers))]))
#updating functions (incl. momentum)
#update 1 (only used for derivation in update #4)
self.old_updates = [theano.shared(zero * param_i.get_value())\
for param_i in params]
self.current_delta = [theano.shared(np.float32(zero * param_i.get_value()))\
for param_i in params]
self.grads = T.grad(self.cost, params)
#update 2
self.current_change_update = [
(current_delta_i, self.learning_rate * grad_i +\
self.momentum * old_updates_i)\
for current_delta_i,grad_i, old_updates_i in\
zip(self.current_delta,self.grads,self.old_updates)
]
#update 3
updates = [
( param_i,param_i - current_delta_i) for param_i, current_delta_i in\
zip(params,self.current_delta)]
#self.updates = []
#update 4 (derived from update #1)
momentum_updates = [(old_updates_i, current_delta_i)\
for old_updates_i, current_delta_i in\
zip(self.old_updates,self.current_delta)]
#self.momentum_updates = []
#now batch-normalization updates when needed
batchnorm_sliding_updates = []
for layer in self.layers:
if not isinstance(layer, NetConvPoolLayer):
continue
if layer.batchnorm_slide_percent <> 0.:
batchnorm_sliding_updates += [
(layer.sd_input_old, layer.sd_input),
(layer.means_old, layer.sd_input)
]
#combined updates
self.all_updates = self.current_change_update + updates +\
momentum_updates + batchnorm_sliding_updates
#test model function
self.test_model = theano.function([],
self.layers[last_index].errors(
self.y),
givens={
x: self.testing_x,
y: self.testing_y
})
#validation model function
self.validate_model = theano.function([],
self.layers[last_index].errors(
self.y),
givens={
x: self.validation_x,
y: self.validation_y
})
#training function
self.train_model = theano.function([],
self.cost,
updates=self.all_updates,
givens={
x: self.training_x,
y: self.training_y
})
self.patience = 20000
self.patience_increase = 3
self.improvement_threshold = 0.995
self.validation_frequency = min(self.n_train_batches,
self.patience // 2)
self.best_validation_loss = np.inf
self.best_iter = 0
#DEPRECATED
self.itermode = 'train'
self.test_score = 0.
self.start_time = timeit.default_timer()
self.epoch = 0
self.iter_i = 0 # renamed bc `iter` is reserved
self.done_looping = False
self.param_index = param_index
#constant-defined stuff
self.improvement_threshold = 0.995
self.validation_frequency = min(self.n_train_batches,
self.patience // 2)
self.done_looping = False
print 'initialized neural network object'
def solving_logistic_regression(datapath, learning_rate = 0.54,batch = 500,n_epoch = 30):
##for MNIST DATA LOADING PROCESS
print "loading data...."
mnist_data = upload_data(datapath)
train, valid, test = mnist_data
##creating theano buffer for python data
print 'moving to data to shared conversion'
train_x, train_y = to_shared(train)
valid_x, valid_y = to_shared(valid)
test_x, test_y = to_shared(test)
n_train_batch = train[0].shape[0] // batch
n_valid_batch = valid[0].shape[0] // batch
n_test_batch = test[0].shape[0] // batch
x = T.matrix('x')
y = T.ivector('y')
index = T.iscalar('index')
logistic = LogisticRegression(input = x,
n_in = 784,
n_out = 10)
fun_valid = function(inputs = [index],
outputs = logistic.error(y),
givens = [(x,valid_x[index*batch:(index+1)*batch,:]),
(y,valid_y[index*batch:(index+1)*batch])]
)
fun_test = function(inputs = [index],
outputs = logistic.y_pred,
givens = [(x,test_x[index*batch:(index+1)*batch,:])],
)
print "calaculating cost function"
cost = logistic.negative_log_likelihood(y)
g_W = T.grad(cost = cost,wrt = logistic.W)
g_b = T.grad(cost = cost,wrt = logistic.b)
updates = [(logistic.W, logistic.W - g_W*learning_rate),
(logistic.b, logistic.b - g_b*learning_rate)]
fun_train = function(inputs =[index],
outputs = logistic.params,
updates = updates,
givens = [(x,train_x[index*batch:(index+1)*batch,:]),
(y,train_y[index*batch:(index+1)*batch])]
)
################
#TRAINING MODEL#
################..........................................
print 'training starts now -->'
patience = 5000
patience_increase = 2
improvement = 0.96
validation_frequency = min(n_train_batch, patience//2)
least_error = np.Inf
epoch = 0
done_looping = False
print 'EPOCH counting .....'
start_time = timeit.default_timer()
while epoch < n_epoch and (not done_looping):
for current_batch in range(n_train_batch):
total_batches = (epoch*n_train_batch) + current_batch
fun_train(current_batch)
if (total_batches+1) % validation_frequency == 0:
this_error = [fun_valid(n) for n in range(n_valid_batch)]
this_error = np.mean(this_error)
if this_error < least_error*improvement:
least_error = this_error
patience = max(patience,total_batches * patience_increase)
with open('/home/sameer/best_model.pkl', 'wb') as f:
pickle.dump(logistic, f)
if total_batches > patience:
done_looping = True
epoch += 1
if total_batches != 0:
print least_error
print 'the convergence ratio is %f' %(patience/float(total_batches))
end_time = timeit.default_timer()
net_time = end_time - start_time
print 'total time %f' %net_time
print 'time per epoch %f' %(net_time/epoch)
print 'the error is %f' %least_error
print 'the total number of epoch %d' %epoch
import cPickle
f = gzip.open('C:/nnets/mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()
n_train, n_test = map(lambda x: len(x[0]), [train_set, test_set])
dims = train_set[0].shape[1]
n_classes = len(set(train_set[1]))
import numpy
import theano
import theano.tensor as T
X = T.dmatrix()
y = T.ivector()
prepare_data = lambda x: (theano.shared(x[0].astype('float64')),
theano.shared(x[1].astype('int32')))
(training_x, training_y), (test_x, test_y), (validation_x, validation_y) = map(
prepare_data, [train_set, test_set, valid_set])
W = theano.shared(numpy.zeros([dims, n_classes]))
b = theano.shared(numpy.zeros(n_classes))
y_hat = T.nnet.softmax(T.dot(X, W) + b)
y_pred = T.argmax(y_hat, axis=1)
test_error = T.mean(T.neq(y_pred, y))
training_error = -T.mean(T.log(y_hat)[T.arange(y.shape[0]), y])
learning_rate = 0.2
def fit(self,
X,
Y,
learning_rate=1e-2,
mu=0.99,
reg=1e-12,
epochs=400,
batch_sz=20,
print_period=1,
show_fig=False):
# X = X.astype(np.float32)
Y = Y.astype(np.int32)
# initialize hidden layers
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W = init_weight(M1, K)
b = np.zeros(K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
# for momentum
dparams = [
theano.shared(np.zeros(p.get_value().shape)) for p in self.params
]
# for rmsprop
cache = [
theano.shared(np.zeros(p.get_value().shape)) for p in self.params
]
# set up theano functions and variables
thX = T.matrix('X')
thY = T.ivector('Y')
pY = self.forward(thX)
rcost = reg * T.sum([(p * p).sum() for p in self.params])
cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost
prediction = self.predict(thX)
grads = T.grad(cost, self.params)
# momentum only
updates = [(p, p + mu * dp - learning_rate * g)
for p, dp, g in zip(self.params, dparams, grads)
] + [(dp, mu * dp - learning_rate * g)
for dp, g in zip(dparams, grads)]
train_op = theano.function(
inputs=[thX, thY],
outputs=[cost, prediction],
updates=updates,
)
n_batches = N // batch_sz
costs = []
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
c, p = train_op(Xbatch, Ybatch)
if j % print_period == 0:
costs.append(c)
e = np.mean(Ybatch != p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c,
"error rate:", e)
if show_fig:
plt.plot(costs)
plt.show()
def __init__(self,
embeddings,
height,
filter_hs,
conv_activation,
feature_maps,
output_units,
batch_size,
dropout_rates,
activations=[Iden]):
"""
:param embeddings: word embeddings
:param height: sentence length (padded as necessary)
:param filter_hs: filter window sizes
:param conv_activation: activation functin for the convolutional layer
:param feature_maps: the size of feature maps (per filter window)
:param output_units: number of output variables
"""
rng = np.random.RandomState(3435)
self.batch_size = batch_size
# define model architecture
self.index = T.lscalar() # minibatch number
self.x = T.imatrix('x') # a minibatch of words
self.y = T.ivector('y') # corresponding outputs
width = embeddings.shape[1]
self.emb_layer = EmbeddingLayer(embeddings, name='Words')
# inputs to the ConvNet go to all convolutional filters:
image_shape = (batch_size, 1, height, width) # e.g. (50, 1, 66, 300)
layer0_input = self.emb_layer.output(self.x).reshape(image_shape)
#(self.x.shape[0], 1, self.x.shape[1], width))
self.conv_layers = []
# outputs of the convolutional filters
layer1_inputs = []
filter_w = width
for filter_h in filter_hs:
filter_shape = (feature_maps, 1, filter_h, filter_w
) # e.g. (100, 1, 7, 300)
pool_size = (height - filter_h + 1, 1) # e.g. (60, 1)
conv_layer = LeNetConvPoolLayer(rng,
image_shape=image_shape,
filter_shape=filter_shape,
poolsize=pool_size,
non_linear=conv_activation)
layer1_input = conv_layer.output(layer0_input).flatten(2)
self.conv_layers.append(conv_layer)
layer1_inputs.append(layer1_input)
# inputs to the MLP
layer1_input = T.concatenate(layer1_inputs, 1)
layer_sizes = [feature_maps * len(filter_hs), output_units]
# initiailze MLPDropout
MLPDropout.__init__(self,
rng,
input=layer1_input,
layer_sizes=layer_sizes,
activations=activations,
dropout_rates=dropout_rates)
# add embeddings
self.params += self.emb_layer.params
# add parameters from convolutional layers
for conv_layer in self.conv_layers:
self.params += conv_layer.params
