def his_match_list(match_day):
url = url_his_ml % match_day
data = req(url)
if data is None:
print 'request his_match_list error' # raise error
return
item_ptn = re.compile(r'<tr height[^>]+>(.*?)</tr>')
field_ptn = re.compile(r'<td[^>]*>(.*?)</td>')
ret = []
for item in item_ptn.findall(data):
m = field_ptn.findall(item)
ret.append({
"match_id": utils.retrieve_id(m[-1]),
"league": m[0],
"home": utils.drop(m[3]),
"visiting": utils.drop(m[5]),
"match_time": m[1],
"is_betting": utils.is_bet(m[-1]),
"full_score": utils.drop_font(m[4]),
"half_score": utils.drop_font(m[6]),
"status": m[2],
})
return ret
def load_pretrained_layers(self):
# Current state of base
state_dict = self.state_dict()
param_names = list(state_dict.keys())
# Pretrained vgg16
pretrained_state_dict = torchvision.models.vgg16(
pretrained=True).state_dict()
pretrained_param_names = list(pretrained_state_dict.keys())
# Copy weights from pretrained model
for i, param in enumerate(param_names[:-4]):
state_dict[param] = pretrained_state_dict[
pretrained_param_names[i]]
pretrained_fc6_weight = pretrained_state_dict[
'classifier.0.weight'].view(4096, 512, 7, 7)
pretrained_fc6_bias = pretrained_state_dict['classifier.0.bias']
state_dict['conv6.weight'] = drop(pretrained_fc6_weight,
factors=[4, 1, 3,
3]) # (1024, 512, 3, 3)
state_dict['conv6.bias'] = drop(pretrained_fc6_bias,
factors=[4]) # 1024
pretrained_fc7_weight = pretrained_state_dict[
'classifier.3.weight'].view(4096, 4096, 1, 1)
pretrained_fc7_bias = pretrained_state_dict['classifier.3.bias']
state_dict['conv7.weight'] = drop(pretrained_fc7_weight,
factors=[4, 4, 1, 1])
state_dict['conv7.bias'] = drop(pretrained_fc7_bias, factors=[4])
def delete():
if request.method == 'GET':
id = int(request.args.get('id'))
print id
sql = 'delete from taokey where id=%d;' % id
print sql
if drop(id, sql):
return redirect('/userlist/')
def load_from_file(filename, offset=None):
grants_memo = load_memo_from_database(sdb_db.Grant)
author_memo = load_memo_from_database(sdb_db.Author)
institution_memo = load_memo_from_database(sdb_db.Institution)
present_count = 0
added_count = 0
if offset:
print 'starting at row %d' % offset
with open(filename) as f, ManagedSession() as session:
reader = DictReader(f, delimiter=",")
for csv_fields in (reader if offset is None else drop(offset, reader)):
grant = grant_from_csv(csv_fields)
if grant.uuid() in grants_memo:
present_count += 1
continue
update_terms(grant)
# print grant
authors = authors_from_csv(csv_fields)
if authors:
mem_auths = [
memoized_row(author_memo, author) for author in authors
]
# if len(mem_auths) != len(authors):
# print 'fewer memoized authors!'
# import pprint
# print pprint.pprint(csv_fields)
# if len(set(mem_auths)) != len(mem_auths):
# print 'duplicate memoized authors!'
# import pprint
# print pprint.pprint(csv_fields)
for author in set(mem_auths):
grant.authors.append(author)
institution = institution_from_csv(csv_fields)
if institution:
grant.institution = memoized_row(institution_memo, institution)
session.add(grant)
added_count += 1
grants_memo[grant.uuid()] = grant
if (added_count % 1000 == 0):
session.commit()
print '%s more records added' % added_count
session.commit()
print '-----------------------'
print '%s records were added' % added_count
print '%s records already in the db' % present_count
print '%s total records parsed' % (added_count + present_count)
def load_from_file(filename, offset=None):
grants_memo = load_memo_from_database(sdb_db.Grant)
author_memo = load_memo_from_database(sdb_db.Author)
institution_memo = load_memo_from_database(sdb_db.Institution)
present_count = 0
added_count = 0
if offset:
print 'starting at row %d' % offset
with open(filename) as f, ManagedSession() as session:
reader = DictReader(f, delimiter=",")
for csv_fields in (reader if offset is None else drop(offset, reader)):
grant = grant_from_csv(csv_fields)
if grant.uuid() in grants_memo:
present_count += 1
continue
update_terms(grant)
# print grant
authors = authors_from_csv(csv_fields)
if authors:
mem_auths = [memoized_row(author_memo, author) for author in authors]
# if len(mem_auths) != len(authors):
# print 'fewer memoized authors!'
# import pprint
# print pprint.pprint(csv_fields)
# if len(set(mem_auths)) != len(mem_auths):
# print 'duplicate memoized authors!'
# import pprint
# print pprint.pprint(csv_fields)
for author in set(mem_auths):
grant.authors.append(author)
institution = institution_from_csv(csv_fields)
if institution:
grant.institution = memoized_row(institution_memo, institution)
session.add(grant)
added_count += 1
grants_memo[grant.uuid()] = grant
if (added_count % 1000 == 0):
session.commit()
print '%s more records added' % added_count
session.commit()
print '-----------------------'
print '%s records were added' % added_count
print '%s records already in the db' % present_count
print '%s total records parsed' % (added_count + present_count)
def __init__(self,
rng,
is_train,
input_data,
filter_shape,
image_shape,
ssample=(1, 1),
bordermode='valid',
p=0.5,
alpha=0.0):
"""
Allocate a LeNetConvPoolLayer with shared variable internal parameters.
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shape: tuple or list of length 4
:param filter_shape: (number of filters, num input feature maps,
filter height, filter width)
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows, #cols)
"""
assert image_shape[1] == filter_shape[1]
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = numpy.prod(filter_shape[1:])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) //
numpy.prod(ssample))
# initialize weights with random weights
W_bound = numpy.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(numpy.asarray(rng.uniform(low=-W_bound,
high=W_bound,
size=filter_shape),
dtype=theano.config.floatX),
borrow=True)
# the bias is a 1D tensor -- one bias per output feature map
b_values = numpy.zeros((filter_shape[0], ), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
# convolve input feature maps with filters
conv_out = conv2d(input=input_data,
filters=self.W,
filter_shape=filter_shape,
input_shape=image_shape,
subsample=ssample,
border_mode=bordermode)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
activated_output = T.nnet.relu(conv_out +
self.b.dimshuffle('x', 0, 'x', 'x'),
alpha=alpha)
dropped_output = drop(activated_output, p)
self.output = T.switch(T.neq(is_train, 0), dropped_output,
p * activated_output)
# store parameters of this layer
self.params = [self.W, self.b]
# keep track of model input
self.input = input_data
def test_AllCNN_Models_DA_BN(use_bn=False, model='c', learning_rate=0.05, n_epochs=350, batch_size=200, L2_reg=0.001, input_ndo_p=0.8, layer_ndo_p=0.5, save_model=True, save_freq=50, s1=5, s2=5):
"""
: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 batch_size: int
:param batch_size: the number of training examples per batch
"""
rng = numpy.random.RandomState(23455)
datasets = load_data2(theano_shared=False)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
train_set_x = train_set_x.reshape(len(train_set_x),3,32,32)
valid_set_x = valid_set_x.reshape(len(valid_set_x),3,32,32)
test_set_x = test_set_x.reshape(len(test_set_x),3,32,32)
train_set_x = numpy.asarray(train_set_x, dtype=theano.config.floatX)
valid_set_x = numpy.asarray(valid_set_x, dtype=theano.config.floatX)
test_set_x = numpy.asarray(test_set_x, dtype=theano.config.floatX)
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.shape[0]
n_valid_batches = valid_set_x.shape[0]
n_test_batches = test_set_x.shape[0]
n_train_batches //= batch_size
n_valid_batches //= batch_size
n_test_batches //= batch_size
print 'n_train_batches: ', n_train_batches
print 'n_valid_batches: ', n_valid_batches
print 'n_test_batches: ', n_test_batches
learning_rate = numpy.asarray(learning_rate, dtype=numpy.float32)
print 'learning_rate: ', learning_rate
# allocate symbolic variables for the data
#index = T.lscalar() # index to a [mini]batch
lr = T.fscalar()
training_enabled = T.iscalar('training_enabled')
# start-snippet-1
x = T.tensor4('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
#layer0_input = x.reshape((batch_size, 3, 32, 32))
# drop the input only while training, don't drop while testing
#dropout_input = T.switch(T.neq(training_enabled, 0), drop(layer0_input, p=input_ndo_p), input_ndo_p * layer0_input)
dropout_input = T.switch(T.neq(training_enabled, 0), drop(x, p=input_ndo_p), input_ndo_p * x)
classifier = None
Model_Name = None
if use_bn:
if model == 'a':
Model_Name = ModelA_AllCNN_BN
elif model == 'b':
Model_Name = ModelB_AllCNN_BN
elif model == 'c':
Model_Name = ModelC_AllCNN_BN
else:
raise RuntimeError('Invalid model parameter!')
else:
if model == 'a':
Model_Name = ModelA_AllCNN
elif model == 'b':
Model_Name = ModelB_AllCNN
elif model == 'c':
Model_Name = ModelC_AllCNN
else:
raise RuntimeError('Invalid model parameter!')
classifier = Model_Name(rng,
dropout_input,
y,
batch_size,
training_enabled,
layer_ndo_p,
L2_reg
)
print 'Training Model: ', classifier.__class__.__name__
test_model = theano.function(
[x, y],
classifier.errors,
givens={
training_enabled: numpy.cast['int32'](0)
}
)
validate_model = theano.function(
[x, y],
classifier.errors,
givens={
training_enabled: numpy.cast['int32'](0)
}
)
# 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.
momentum =theano.shared(numpy.cast[theano.config.floatX](0.9), name='momentum')
updates = []
for param in classifier.params:
param_update = theano.shared(param.get_value()*numpy.cast[theano.config.floatX](0.))
updates.append((param, param - lr * param_update))
updates.append((param_update, momentum*param_update + (numpy.cast[theano.config.floatX](1.) - momentum)*T.grad(classifier.cost, param)))
train_model = theano.function(
[x, y, lr],
classifier.cost,
updates=updates,
givens={
training_enabled: numpy.cast['int32'](1)
}
)
# end-snippet-1
###############
# 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)
validation_frequency = n_train_batches // 2
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
if use_bn:
updateLRAfter = 100
else:
updateLRAfter = 200
while (epoch < n_epochs) and (not done_looping):
# shuffle data before starting the epoch
epoch = epoch + 1
if(epoch > updateLRAfter):
learning_rate *= 0.1
updateLRAfter += 50
for minibatch_index in range(n_train_batches):
#print 'epoch: {0}, minibatch: {1}'.format(epoch, minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
# if iter % 50 == 0:
# print('training @ iter = ', iter)
train_x = augmentImages(train_set_x[minibatch_index * batch_size: (minibatch_index + 1) * batch_size], shift1=s1, shift2=s2)
train_y = train_set_y[minibatch_index* batch_size: (minibatch_index + 1) * batch_size]
cost_ij = train_model(train_x, train_y, learning_rate)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(valid_set_x[ii * batch_size: (ii + 1) * batch_size], valid_set_y[ii * batch_size: (ii + 1) * batch_size]) for ii
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)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(test_set_x[ii * batch_size: (ii + 1) * batch_size], test_set_y[ii * batch_size: (ii + 1) * batch_size])
for ii 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
if save_model and epoch % save_freq == 0:
# add model name to the file to differentiate different models
with gzip.open('parameters_epoch_{0}.pklz'.format(epoch), 'wb') as fp:
cPickle.dump([param.get_value() for param in classifier.params], fp, protocol=2)
end_time = timeit.default_timer()
print('Optimization complete.')
print('Best validation score of %f %% obtained with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
def test_ModelC_AllCNN(learning_rate=0.05,
n_epochs=350,
batch_size=200,
L2_reg=0.001,
input_ndo_p=0.8,
layer_ndo_p=0.5,
save_model=True,
save_freq=50):
"""
: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 batch_size: int
:param batch_size: the number of training examples per batch
"""
rng = numpy.random.RandomState(23455)
datasets = load_data2()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches //= batch_size
n_valid_batches //= batch_size
n_test_batches //= batch_size
print 'n_train_batches: ', n_train_batches
print 'n_valid_batches: ', n_valid_batches
print 'n_test_batches: ', n_test_batches
learning_rate = numpy.asarray(learning_rate, dtype=numpy.float32)
print 'learning_rate: ', learning_rate
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
lr = T.fscalar()
training_enabled = T.iscalar('training_enabled')
# start-snippet-1
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
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
layer0_input = x.reshape((batch_size, 3, 32, 32))
# drop the input only while training, don't drop while testing
dropout_input = T.switch(T.neq(training_enabled, 0),
drop(layer0_input, p=input_ndo_p),
input_ndo_p * layer0_input)
layer0 = myConvLayer(rng,
is_train=training_enabled,
input_data=dropout_input,
filter_shape=(96, 3, 3, 3),
image_shape=(batch_size, 3, 32, 32),
ssample=(1, 1),
bordermode='half',
p=1.0)
layer1 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer0.output,
filter_shape=(96, 96, 3, 3),
image_shape=(batch_size, 96, 32, 32),
ssample=(1, 1),
bordermode='half',
p=1.0)
layer2 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer1.output,
filter_shape=(96, 96, 3, 3),
image_shape=(batch_size, 96, 32, 32),
ssample=(2, 2),
bordermode='half',
p=layer_ndo_p)
layer3 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer2.output,
filter_shape=(192, 96, 3, 3),
image_shape=(batch_size, 96, 16, 16),
ssample=(1, 1),
bordermode='half',
p=1.0)
layer4 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer3.output,
filter_shape=(192, 192, 3, 3),
image_shape=(batch_size, 192, 16, 16),
ssample=(1, 1),
bordermode='half',
p=1.0)
layer5 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer4.output,
filter_shape=(192, 192, 3, 3),
image_shape=(batch_size, 192, 16, 16),
ssample=(2, 2),
bordermode='half',
p=layer_ndo_p)
layer6 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer5.output,
filter_shape=(192, 192, 3, 3),
image_shape=(batch_size, 192, 8, 8),
ssample=(1, 1),
bordermode='half',
p=1.0)
layer7 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer6.output,
filter_shape=(192, 192, 1, 1),
image_shape=(batch_size, 192, 8, 8),
ssample=(1, 1),
bordermode='half',
p=1.0)
layer8 = myConvLayer(rng,
is_train=training_enabled,
input_data=layer7.output,
filter_shape=(10, 192, 1, 1),
image_shape=(batch_size, 192, 8, 8),
ssample=(1, 1),
bordermode='half',
p=1.0)
# make sure this is what global averaging does
global_average = layer8.output.mean(axis=(2, 3))
softmax_layer = SoftmaxWrapper(input_data=global_average,
n_in=10,
n_out=10)
L2_sqr = ((layer0.W**2).sum() + (layer1.W**2).sum() + (layer2.W**2).sum() +
(layer3.W**2).sum() + (layer4.W**2).sum() + (layer5.W**2).sum() +
(layer6.W**2).sum() + (layer7.W**2).sum() + (layer8.W**2).sum())
# the cost we minimize during training is the NLL of the model
cost = (softmax_layer.negative_log_likelihood(y) + L2_reg * L2_sqr)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
softmax_layer.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],
training_enabled: numpy.cast['int32'](0)
})
validate_model = theano.function(
[index],
softmax_layer.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],
training_enabled: numpy.cast['int32'](0)
})
# create a list of all model parameters to be fit by gradient descent
params = layer8.params + layer7.params + layer6.params + layer5.params + layer4.params + layer3.params + layer2.params + layer1.params + layer0.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.
momentum = theano.shared(numpy.cast[theano.config.floatX](0.9),
name='momentum')
updates = []
for param in params:
param_update = theano.shared(param.get_value() *
numpy.cast[theano.config.floatX](0.))
updates.append((param, param - lr * param_update))
updates.append((param_update, momentum * param_update +
(numpy.cast[theano.config.floatX](1.) - momentum) *
T.grad(cost, param)))
train_model = theano.function(
[index, lr],
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],
training_enabled: numpy.cast['int32'](1)
})
# end-snippet-1
###############
# 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)
validation_frequency = n_train_batches // 2
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
updateLRAfter = 200
while (epoch < n_epochs) and (not done_looping):
# shuffle data before starting the epoch
epoch = epoch + 1
if (epoch > updateLRAfter):
learning_rate *= 0.1
updateLRAfter += 50
print 'epoch: ', epoch
print 'updateLRAfter: ', updateLRAfter
print 'learning_rate: ', learning_rate
for minibatch_index in range(n_train_batches):
#print 'epoch: {0}, minibatch: {1}'.format(epoch, minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 50 == 0:
print('training @ iter = ', iter)
cost_ij = train_model(minibatch_index, learning_rate)
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)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(i) for i in 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
if save_model and epoch % save_freq == 0:
# add model name to the file to differentiate different models
with gzip.open('parameters_epoch_{0}.pklz'.format(epoch),
'wb') as fp:
cPickle.dump([param.get_value() for param in params],
fp,
protocol=2)
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(('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.)), sys.stderr)
def test_drop(self):
self.assertEqual(drop([1,2,3,4,5],3) , [4,5])
self.assertEqual(drop([1,2,3,4,5],6) , [])