def rbm(threadName, data):
n_visible = data.shape[1]
n_hidden = 4
# the rbm type
rbm = rbms.BinaryBinaryRBM(n_visible, n_hidden)
initial_vmap = { rbm.v: T.matrix('v') }
# We use single-step contrastive divergence (CD-1) to train the RBM. For this, we can use
# the CDUpdater. This requires symbolic CD-1 statistics:
s = stats.cd_stats(rbm, initial_vmap, visible_units=[rbm.v], hidden_units=[rbm.h], k=2)
# We create an updater for each parameter variable
umap = {}
for var in rbm.variables:
# the learning rate is 0.001
pu = var + learning_rate * updaters.CDUpdater(rbm, var, s)
umap[var] = pu
# training
t = trainers.MinibatchTrainer(rbm, umap)
mse = monitors.reconstruction_mse(s, rbm.v)
train = t.compile_function(initial_vmap, mb_size=1, monitors=[mse], name='train', mode=mode)
# run for every sample point
h,w = data.shape
# costs for each thread
costs_1 = []
costs_2 = []
costs_3 = []
costs_4 = []
# train the model
for epoch in xrange(epochs):
for i in xrange(h):
# get cost for training the data point
costs = [m for m in train({ rbm.v: data[i,:].reshape(1,n_visible) })]
# print "MSE = %.4f, thread = %s" % (np.mean(costs), threadName)
if threadName == "Thread-1":
costs_1.append(np.mean(costs))
elif threadName == "Thread-2":
costs_2.append(np.mean(costs))
elif threadName == "Thread-3":
costs_3.append(np.mean(costs))
else:
costs_4.append(np.mean(costs))
# all_costs = []
# for i in xrange(h):
# # get cost for training the data point across all epochs
# cost_point = []
# for epoch in xrange(epochs):
# cost = [m for m in train({ rbm.v: data[i,:].reshape(1,40) })]
# print epoch, cost, threadName, i
# cost_point.append(cost)
#
# all_costs.append(cost_point)
return costs_1, costs_2, costs_3, costs_4
rbm.F = factors.Factor(rbm, name='F')
# IMPORTANT: the following parameters instances are associated with the FACTOR rbm.F, and not with the RBM itself.
rbm.Wv = parameters.ProdParameters(rbm.F, [rbm.v, rbm.F], Wv, name='Wv')
rbm.Wh = parameters.ProdParameters(rbm.F, [rbm.h, rbm.F], Wh, name='Wh')
rbm.Wx = parameters.ProdParameters(rbm.F, [rbm.x, rbm.F], Wx, name='Wx')
rbm.F.initialize() # done adding parameters to rbm.F
rbm.bv = parameters.BiasParameters(rbm, rbm.v, theano.shared(value = initial_bv, name='bv'), name='bv') # visible bias
rbm.bh = parameters.BiasParameters(rbm, rbm.h, theano.shared(value = initial_bh, name='bh'), name='bh') # hidden bias
initial_vmap = { rbm.v: T.matrix('v'), rbm.x: T.matrix('x') }
# try to calculate weight updates using CD-1 stats
print ">> Constructing contrastive divergence updaters..."
s = stats.cd_stats(rbm, initial_vmap, visible_units=[rbm.v], hidden_units=[rbm.h], context_units=[rbm.x], k=1, mean_field_for_gibbs=[rbm.v], mean_field_for_stats=[rbm.v])
umap = {}
for var in rbm.variables:
pu = var + 0.0005 * updaters.CDUpdater(rbm, var, s)
umap[var] = pu
print ">> Compiling functions..."
t = trainers.MinibatchTrainer(rbm, umap)
m = monitors.reconstruction_mse(s, rbm.v)
mce = monitors.reconstruction_crossentropy(s, rbm.v)
# train = t.compile_function(initial_vmap, mb_size=32, monitors=[m], name='train', mode=mode)
train = t.compile_function(initial_vmap, mb_size=32, monitors=[m, mce], name='train', mode=mode)
evaluate = t.compile_function(initial_vmap, mb_size=32, monitors=[m, mce], train=False, name='evaluate', mode=mode)
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200) # np.random.randint(2, size=(10000, n_visible))
n_visible = data.shape[1]
n_hidden = 100
rbm = rbms.BinaryBinaryRBM(n_visible, n_hidden)
initial_vmap = { rbm.v: T.matrix('v') }
# try to calculate weight updates using CD-1 stats
s = stats.cd_stats(rbm, initial_vmap, visible_units=[rbm.v], hidden_units=[rbm.h], k=1)
umap = {}
for var, shape in zip([rbm.W.var, rbm.bv.var, rbm.bh.var], [(rbm.n_visible, rbm.n_hidden), (rbm.n_visible,), (rbm.n_hidden,)]):
# pu = 0.001 * (param_updaters.CDParamUpdater(params, sc) + 0.02 * param_updaters.DecayParamUpdater(params))
pu = updaters.CDUpdater(rbm, var, s)
pu = var + 0.0001 * updaters.MomentumUpdater(pu, 0.9, shape)
umap[var] = pu
t = trainers.MinibatchTrainer(rbm, umap)
m = monitors.reconstruction_mse(s, rbm.v)
train = t.compile_function(initial_vmap, mb_size=32, monitors=[m], name='train', mode=mode)
def morbrun1(f1=1, f2=1, v1=1, v2=1, kern = 1):
test_set_x = np.array(eval_print1).flatten(order='C')
valid_set_x = np.array(eval_print3).flatten(order='C')
train_set_x = np.array(eval_print2).flatten(order='C')
train_set_x = train_set_x.reshape(np.array(eval_print2).shape[0]*batchm,kern,v1,v2)
valid_set_x = valid_set_x.reshape(np.array(eval_print3).shape[0]*batchm,kern,v1,v2)
test_set_x = test_set_x.reshape(np.array(eval_print1).shape[0]*batchm,kern,v1,v2)
visible_maps = kern
hidden_maps = neuron
filter_height = f1
filter_width = f2
mb_size = batchm # 1 minibatch
print(">> Constructing RBM...")
fan_in = visible_maps * filter_height * filter_width
"""
initial_W = numpy.asarray(
self.numpy_rng.uniform(
low = - numpy.sqrt(3./fan_in),
high = numpy.sqrt(3./fan_in),
size = self.filter_shape
), dtype=theano.config.floatX)
"""
numpy_rng = np.random.RandomState(123)
initial_W = np.asarray(
numpy_rng.normal(
0, 0.5 / np.sqrt(fan_in),
size = (hidden_maps, visible_maps, filter_height, filter_width)
), dtype=theano.config.floatX)
initial_bv = np.zeros(visible_maps, dtype = theano.config.floatX)
initial_bh = np.zeros(hidden_maps, dtype = theano.config.floatX)
shape_info = {
'hidden_maps': hidden_maps,
'visible_maps': visible_maps,
'filter_height': filter_height,
'filter_width': filter_width,
'visible_height': v1, #45+8,
'visible_width': v2, #30,
'mb_size': mb_size
}
# rbms.SigmoidBinaryRBM(n_visible, n_hidden)
rbm = base.RBM()
rbm.v = units.BinaryUnits(rbm, name='v') # visibles
rbm.h = units.BinaryUnits(rbm, name='h') # hiddens
rbm.W = parameters.Convolutional2DParameters(rbm, [rbm.v, rbm.h], theano.shared(value=initial_W, name='W'), name='W', shape_info=shape_info)
# one bias per map (so shared across width and height):
rbm.bv = parameters.SharedBiasParameters(rbm, rbm.v, 3, 2, theano.shared(value=initial_bv, name='bv'), name='bv')
rbm.bh = parameters.SharedBiasParameters(rbm, rbm.h, 3, 2, theano.shared(value=initial_bh, name='bh'), name='bh')
initial_vmap = { rbm.v: T.tensor4('v') }
# try to calculate weight updates using CD-1 stats
print(">> Constructing contrastive divergence updaters...")
s = stats.cd_stats(rbm, initial_vmap, visible_units=[rbm.v], hidden_units=[rbm.h], k=5, mean_field_for_stats=[rbm.v], mean_field_for_gibbs=[rbm.v])
lr_cd = 0.001
if indk == -1:
lr_cd = 0
umap = {}
for var in rbm.variables:
pu = var + lr_cd * updaters.CDUpdater(rbm, var, s)
umap[var] = pu
print(">> Compiling functions...")
t = trainers.MinibatchTrainer(rbm, umap)
m = monitors.reconstruction_mse(s, rbm.v)
e_data = rbm.energy(s['data']).mean()
e_model = rbm.energy(s['model']).mean()
# train = t.compile_function(initial_vmap, mb_size=32, monitors=[m], name='train', mode=mode)
train = t.compile_function(initial_vmap, mb_size=mb_size, monitors=[m, e_data, e_model], name='train', mode=mode)
# TRAINING
epochs = epoch_cd
print(">> Training for %d epochs..." % epochs)
for epoch in range(epochs):
monitoring_data_train = [(cost, energy_data, energy_model) for cost, energy_data, energy_model in train({ rbm.v: train_set_x })]
mses_train, edata_train_list, emodel_train_list = zip(*monitoring_data_train)
lay1w = rbm.W.var.get_value()
Wl = theano.shared(lay1w)
lay1bh = rbm.bh.var.get_value()
bhl = theano.shared(lay1bh)
return [Wl, bhl]
def rbm(threadName, data):
n_visible = data.shape[1]
n_hidden = 4
# the rbm type
rbm = rbms.BinaryBinaryRBM(n_visible, n_hidden)
initial_vmap = {rbm.v: T.matrix('v')}
# We use single-step contrastive divergence (CD-1) to train the RBM. For this, we can use
# the CDUpdater. This requires symbolic CD-1 statistics:
s = stats.cd_stats(rbm,
initial_vmap,
visible_units=[rbm.v],
hidden_units=[rbm.h],
k=2)
# We create an updater for each parameter variable
umap = {}
for var in rbm.variables:
# the learning rate is 0.001
pu = var + learning_rate * updaters.CDUpdater(rbm, var, s)
umap[var] = pu
# training
t = trainers.MinibatchTrainer(rbm, umap)
mse = monitors.reconstruction_mse(s, rbm.v)
train = t.compile_function(initial_vmap,
mb_size=1,
monitors=[mse],
name='train',
mode=mode)
# run for every sample point
h, w = data.shape
# costs for each thread
costs_1 = []
costs_2 = []
costs_3 = []
costs_4 = []
# train the model
for epoch in xrange(epochs):
for i in xrange(h):
# get cost for training the data point
costs = [
m for m in train({rbm.v: data[i, :].reshape(1, n_visible)})
]
# print "MSE = %.4f, thread = %s" % (np.mean(costs), threadName)
if threadName == "Thread-1":
costs_1.append(np.mean(costs))
elif threadName == "Thread-2":
costs_2.append(np.mean(costs))
elif threadName == "Thread-3":
costs_3.append(np.mean(costs))
else:
costs_4.append(np.mean(costs))
# all_costs = []
# for i in xrange(h):
# # get cost for training the data point across all epochs
# cost_point = []
# for epoch in xrange(epochs):
# cost = [m for m in train({ rbm.v: data[i,:].reshape(1,40) })]
# print epoch, cost, threadName, i
# cost_point.append(cost)
#
# all_costs.append(cost_point)
return costs_1, costs_2, costs_3, costs_4
n_hidden = 500
mb_size = 20
k = 15
learning_rate = 0.1
epochs = 15
print ">> Constructing RBM..."
rbm = rbms.BinaryBinaryRBM(n_visible, n_hidden)
initial_vmap = { rbm.v: T.matrix('v') }
persistent_vmap = { rbm.h: theano.shared(np.zeros((mb_size, n_hidden), dtype=theano.config.floatX)) }
# try to calculate weight updates using CD stats
print ">> Constructing contrastive divergence updaters..."
s = stats.cd_stats(rbm, initial_vmap, visible_units=[rbm.v], hidden_units=[rbm.h], k=k, persistent_vmap=persistent_vmap, mean_field_for_stats=[rbm.v], mean_field_for_gibbs=[rbm.v])
umap = {}
for var in rbm.variables:
pu = var + (learning_rate / float(mb_size)) * updaters.CDUpdater(rbm, var, s)
umap[var] = pu
print ">> Compiling functions..."
t = trainers.MinibatchTrainer(rbm, umap)
m = monitors.reconstruction_mse(s, rbm.v)
m_data = s['data'][rbm.v]
m_model = s['model'][rbm.v]
e_data = rbm.energy(s['data']).mean()
e_model = rbm.energy(s['model']).mean()
# train = t.compile_function(initial_vmap, mb_size=32, monitors=[m], name='train', mode=mode)
