def SCAE_preTrain(batch_size=1, nkerns= [3, 4], dataset=None, n_epochs=70, k_Top=5, learning_rate=1e-1, binary=True):
"""
Stacked Convolutional AutoEncoders.
"""
with open('SCAE_MR_1e-1K34', 'r') as f:
rval = cPickle.load(f)
return rval
if dataset is None:
dataset = Preprocess().load_data(binary)
train_set_x = dataset[0][0]
train_set_z = dataset[0][2]
n_train_batch = train_set_x.get_value(borrow=True).shape[0]
n_train_batch /= batch_size
print '... Building Stacked Conv. AutoEncoders'
rng = numpy.random.RandomState(96813)
index = T.lscalar('index')
x = T.dmatrix('Input Sentence')
z = T.iscalar('Sentence length')
layer0_input = x[:, :z*50].reshape((batch_size, 1, 50, -1))
layer0 = SCAE(rng, input=layer0_input, image_shape=None, filter_shape=(nkerns[0], 1, 1, 8), \
factor=.5, s=z, k_Top=k_Top, do_fold=False)
layer1_input = layer0.get_hidden_values(layer0_input)
layer1 = SCAE(rng, input=layer1_input, filter_shape=(nkerns[1], nkerns[0], 1, 5), \
image_shape=None, factor = .0, s=z, k_Top=k_Top, do_fold=False)
layer1_output = layer1.get_hidden_values(layer1_input)
hidden_input = layer1_output.flatten(2)
layer2 = AE(rng, input=hidden_input, n_visible=layer1.kshp[0]*50*k_Top, n_hidden=100)
Y = layer2.get_hidden_values(hidden_input)
################
# DECODING #
################
decode_hidden_layer = layer2.get_reconstructed_input(Y)
decode_input = decode_hidden_layer.reshape(layer1.shape)
decode_layer1 = layer1.get_reconstructed_input(decode_input)
Z = layer0.get_reconstructed_input(decode_layer1)
params = layer2.params + layer1.params + layer0.params
def get_cost_updates(X, Z, params, learning_rate):
''' Update the Stacked Convolutional Auto-Encoders. '''
L = T.sum((X-Z) ** 2, axis=(1,2,3))
cost = T.mean(L)
gparams = T.grad(cost, params)
rho = 1e-7
G = [(theano.shared(value=numpy.zeros_like(param.get_value()), name="AdaGrad_" + param.name, borrow=True)) for param in params]
G_update = [T.add(g_adag, T.sqr(grad_i)) for g_adag, grad_i in zip(G, gparams)]
updates = []
for param_i, g_update, grad_i, g in zip(params, G_update, gparams, G):
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(g_update) ))
updates.append((g, g_update))
return (cost, updates)
cost, updates = get_cost_updates(layer0_input, Z, params, learning_rate)
train_model = theano.function([index], cost, updates=updates, \
givens={x: train_set_x[index * batch_size: (index + 1) * batch_size],
z: train_set_z[index]})
print '... Pretraining model'
plot_SCAE = []
epoch = 0
while epoch < n_epochs:
epoch += 1
for minibatch in xrange(n_train_batch):
cost_ij = train_model(minibatch)
print '\tepoch %i,\tcost %f' % (epoch, cost_ij)
plot_SCAE.append(cost_ij)
plot('SCAE_Movie Results.', numpy.asarray(plot_SCAE), 74e-2)
# Serialise the learned parameters
with open('SCAE_MR_1e-1K%i%i'%(nkerns[0], nkerns[1]), 'wb') as f:
cPickle.dump(params, f)
return params
class SCAE(object):
def __init__(self, rng, input, filter_shape, image_shape, factor, s, k_Top=5, do_fold=True):
# Input will be image_shape, filter_shape, input, rng
self.kshp = filter_shape
self.imshp = None
self.i_kshp = (self.kshp[1], self.kshp[0], self.kshp[2], self.kshp[3])
self.i_imshp = None
self.do_fold = do_fold
self.k_Top = k_Top
self.factor = factor
self.s = s
self.rng = rng
fan_in = numpy.prod(filter_shape[1:])
fan_out = filter_shape[0] * numpy.prod(filter_shape[2:])
# initialize weights with random weights
W_bound = numpy.sqrt(6.0 / (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 # 2, 1, 1, 3
),
name="conv_W",
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, name="conv_b", borrow=True)
self.c = theano.shared(value=0.0, name="deconv_c")
self.W_tilde = self.W[:, :, ::-1, ::-1].dimshuffle(1, 0, 2, 3)
if input == None:
self.x = T.dmatrix(name="input")
else:
self.x = input
self.params = [self.W, self.b, self.c]
def Fold(self, conv_out, ds=(2, 1)):
"""Fold into two. (Sum up vertical neighbours)"""
imgs = images2neibs(
conv_out, T.as_tensor_variable(ds), mode="ignore_borders"
) # Correct 'mode' if there's a typo!
orig = conv_out.shape
shp = (orig[0], orig[1], T.cast(orig[2] / 2, "int32"), orig[3])
res = T.reshape(T.sum(imgs, axis=-1), shp)
return res
def kmaxPool(self, conv_out, pool_shape, k):
"""
Perform k-max Pooling.
"""
n0, n1, d, size = pool_shape
imgs = images2neibs(conv_out, T.as_tensor_variable((1, size)))
indices = T.argsort(T.mul(imgs, -1))
self.k_max_indices = T.sort(indices[:, :k])
S = T.arange(d * n1 * n0).reshape((d * n1 * n0, 1))
return imgs[S, self.k_max_indices].reshape((n0, n1, d, k))
def unpooling(self, Y_4D, Z, X_4D):
""" This method reverses pooling operation.
"""
Y = images2neibs(Y_4D, T.as_tensor_variable((1, Y_4D.shape[3])))
X = images2neibs(X_4D, T.as_tensor_variable((1, X_4D.shape[3])))
X_z = T.zeros_like(X)
X_ = T.set_subtensor(X_z[T.arange(X.shape[0]).reshape((X.shape[0], 1)), Z], Y)
return X_.reshape(X_4D.shape)
def Output(self):
# Convolve input with trained parameters.
conv_out = conv.conv2d(
input=self.x, filters=self.W, border_mode="full", filter_shape=self.kshp, image_shape=self.imshp
)
# Fold conv result into two.
if self.do_fold:
fold = self.Fold(conv_out)
# k-max pooling.
k = T.cast(T.max((self.k_Top, T.ceil(self.factor * self.s))), "int32")
if self.do_fold:
pool_shape = fold.shape
pooled_out = self.kmaxPool(fold, pool_shape, k)
else:
pool_shape = conv_out.shape
pooled_out = self.kmaxPool(conv_out, pool_shape, k)
return T.tanh(pooled_out + self.b.dimshuffle("x", 0, "x", "x"))
def get_hidden_values(self, input):
# convolve input feature maps with filters
self.conv_out = conv.conv2d(
input=input, filters=self.W, border_mode="full", filter_shape=self.kshp, image_shape=self.imshp
)
# k-max pooling.
k = T.cast(T.max((self.k_Top, T.ceil(self.factor * self.s))), "int32")
pool_shape = self.conv_out.shape
pool = self.kmaxPool(self.conv_out, pool_shape, k)
output = T.tanh(pool + self.b.dimshuffle("x", 0, "x", "x"))
self.shape = output.shape
hidden_input = output.flatten(2)
self.fully_connected = AE(
(self.rng), input=hidden_input, n_visible=self.kshp[0] * 25 * self.k_Top, n_hidden=60
) # nkerns[0] replaced with 8
self.params.extend(self.fully_connected.params)
return self.fully_connected.get_hidden_values(hidden_input)
def get_reconstructed_input(self, hidden, start):
reconstruct_AE = self.fully_connected.get_reconstructed_input(hidden)
hidden_NN = reconstruct_AE.reshape(self.shape)
unpool = self.unpooling(hidden_NN, self.k_max_indices, start)
deconv = conv.conv2d(input=unpool, filters=self.W_tilde, filter_shape=self.i_kshp, image_shape=None)
return T.tanh(deconv + self.c.dimshuffle("x", "x", "x", "x"))
# return val*(val>0)
def get_cost_updates(self, learning_rate):
y = self.get_hidden_values(self.x)
z = self.get_reconstructed_input(y, self.conv_out)
L = T.sum((self.x - z) ** 2, axis=(1, 2, 3))
cost = T.mean(L)
gparams = T.grad(cost, self.params)
rho = 1e-7
G = [
(theano.shared(value=numpy.zeros_like(param.get_value()), name="AdaGrad_" + param.name, borrow=True))
for param in self.params
]
G_update = [T.add(g_adag, T.sqr(grad_i)) for g_adag, grad_i in zip(G, gparams)]
updates = []
for param_i, g_update, grad_i, g in zip(self.params, G_update, gparams, G):
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(g_update)))
updates.append((g, g_update))
return (cost, updates)
