def perform_lcn(saveDir, strl, x, y):
n_channels = (0, 1, 2)
dim_input = (32, 32)
colorImg = True
x = x.astype(np.float32)
print x.shape
print x.max()
print x.min()
print np.max(np.mean(x, axis=1))
print np.min(np.mean(x, axis=1))
print strl
print y[:10]
print y[40:50]
image = paramgraphics.mat_to_img(x[:100, :].T,
dim_input,
colorImg=colorImg,
scale=True)
image.save(saveDir + 'svhn_before_lcn_gcn_norm_' + strl + '.png', 'PNG')
#flatten->'b,c,0,1'->'b,0,1,c'
x = x.reshape(-1, 3, 32, 32)
x = np.swapaxes(x, 1, 2)
x = np.swapaxes(x, 2, 3)
lcn.transform(x=x, channels=n_channels, img_shape=dim_input)
#'b,0,1,c'->'b,c,0,1'->flatten
print x.shape
x = np.swapaxes(x, 2, 3)
x = np.swapaxes(x, 1, 2)
x = x.reshape((-1, 32 * 32 * 3))
print x.max()
print x.min()
print np.max(np.mean(x, axis=1))
print np.min(np.mean(x, axis=1))
image = paramgraphics.mat_to_img(x[:100, :].T,
dim_input,
colorImg=colorImg,
scale=True)
image.save(saveDir + 'svhn_after_lcn_gcn_norm_' + strl + '.png', 'PNG')
return x
def perform_lcn(saveDir,strl,x, y):
n_channels=(0,1,2)
dim_input = (32, 32)
colorImg = True
x = x.astype(np.float32)
print x.shape
print x.max()
print x.min()
print np.max(np.mean(x, axis=1))
print np.min(np.mean(x, axis=1))
print strl
print y[:10]
print y[40:50]
image = paramgraphics.mat_to_img(x[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_before_lcn_gcn_norm_'+strl+'.png', 'PNG')
#flatten->'b,c,0,1'->'b,0,1,c'
x = x.reshape(-1,3,32,32)
x = np.swapaxes(x, 1, 2)
x = np.swapaxes(x, 2, 3)
lcn.transform(x=x,channels=n_channels,img_shape=dim_input)
#'b,0,1,c'->'b,c,0,1'->flatten
print x.shape
x = np.swapaxes(x, 2, 3)
x = np.swapaxes(x, 1, 2)
x = x.reshape((-1,32*32*3))
print x.max()
print x.min()
print np.max(np.mean(x, axis=1))
print np.min(np.mean(x, axis=1))
image = paramgraphics.mat_to_img(x[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_after_lcn_gcn_norm_'+strl+'.png', 'PNG')
return x
data = zz['z_test_original'].T
print data.shape
data_perturbed = zz['z_test'].T
print data_perturbed.shape
pertub_label = zz['pertub_label'].astype(np.float32).T
print pertub_label.shape
pertub_number = float(np.sum(1-pertub_label))
print pertub_number
denoise_epochs = 100
visualization_epochs = 20
num_vis = 100
num_vis1 = 64
images = data[:num_vis,:]
image = paramgraphics.mat_to_img(data[:num_vis1,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(os.path.join(res_out, 'data.png'), 'PNG')
image = paramgraphics.mat_to_img(data_perturbed[:num_vis1,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(os.path.join(res_out, 'before_denoise.png'), 'PNG')
for i in xrange(denoise_epochs):
data_perturbed = data_perturbed.astype(np.float32)
if i < visualization_epochs:
images = np.vstack((images, data_perturbed[:num_vis,:]))
data_perturbed, mse = test_denoise(data_perturbed, pertub_label, 1000)
print mse / pertub_number
with open(logfile,'a') as f:
f.write(str(mse / pertub_number) + "\n")
#tile_shape = (visualization_epochs+1, num_vis)
start = int(pertub_prob)
end = int(pertub_prob1)
data_perturbed = np.zeros(data.shape)
tmp_a = np.ones(width)
tmp_a[start:end] = 0
#print tmp_a.shape
#print tmp_a
tmp_b = np.tile(tmp_a, height)
print tmp_b.shape
print pertub_label.shape
pertub_label = (pertub_label.T*tmp_b).T
data_perturbed = pertub_label*data+(1-pertub_label)*data_perturbed
if pertub_type == 4:
sio.savemat('data_imputation/type_'+str(pertub_type)+'_params_'+str(int(pertub_prob*100))+'_noise_rawdata.mat', {'z_train' : x_train.T, 'z_test_original' : data, 'z_test' : data_perturbed, 'pertub_label' : pertub_label})
#print data_perturbed[:,:25].shape
image = paramgraphics.mat_to_img(data_perturbed[:,:25], (28,28), colorImg=False, scale=True)
image.save('data_imputation/test_noise_4_'+str(pertub_prob)+'.png', 'PNG')
elif pertub_type == 3:
sio.savemat('data_imputation/type_'+str(pertub_type)+'_params_'+str(pertub_prob)+'_noise_rawdata.mat', {'z_train' : x_train.T, 'z_test_original' : data, 'z_test' : data_perturbed, 'pertub_label' : pertub_label})
#print data_perturbed[:,:25].shape
image = paramgraphics.mat_to_img(data_perturbed[:,:25], (28,28), colorImg=False, scale=True)
image.save('data_imputation/test_noise_3_'+str(pertub_prob)+'.png', 'PNG')
elif pertub_type == 5:
sio.savemat('data_imputation/type_'+str(pertub_type)+'_params_'+str(start)+'_'+str(end)+'_noise_rawdata.mat', {'z_train' : x_train.T, 'z_test_original' : data, 'z_test' : data_perturbed, 'pertub_label' : pertub_label})
#print data_perturbed[:,:25].shape
image = paramgraphics.mat_to_img(data_perturbed[:,:25], (28,28), colorImg=False, scale=True)
image.save('data_imputation/test_noise_5_'+str(start)+'_'+str(end)+'.png', 'PNG')
batch3_labels = []
for i in np.arange(len(y)):
batch3_labels.append(y[i])
batch3_data = np.asarray(batch3_data).T
batch3_labels = np.asarray(batch3_labels)
print 'Check n x f'
print batch1_data.shape
print batch1_labels.shape
print batch2_data.shape
print batch2_labels.shape
print batch3_data.shape
print batch3_labels.shape
image = paramgraphics.mat_to_img(batch1_data[:100, :].T,
dim_input,
colorImg=colorImg,
scale=True)
image.save(saveDir + 'svhn_train.png', 'PNG')
image = paramgraphics.mat_to_img(batch2_data[:100, :].T,
dim_input,
colorImg=colorImg,
scale=True)
image.save(saveDir + 'svhn_valid.png', 'PNG')
image = paramgraphics.mat_to_img(batch3_data[:100, :].T,
dim_input,
colorImg=colorImg,
scale=True)
image.save(saveDir + 'svhn_test.png', 'PNG')
if preprocessing == 'gcn_var':
batch1_data = pypp.global_contrast_normalize(batch1_data,
def cmmva_6layer_svhn(learning_rate=0.01,
n_epochs=600,
dataset='svhngcn_var',
batch_size=500,
dropout_flag=1,
seed=0,
predir=None,
activation=None,
n_batch=625,
weight_decay=1e-4,
super_predir=None,
super_preepoch=None):
"""
Implementation of convolutional MMVA
"""
'''
svhn
'''
n_channels = 3
colorImg = True
dim_w = 32
dim_h = 32
dim_input=(dim_h, dim_w)
n_classes = 10
D = 1.0
C = 1.0
if os.environ.has_key('C'):
C = np.cast['float32'](float((os.environ['C'])))
if os.environ.has_key('D'):
D = np.cast['float32'](float((os.environ['D'])))
color.printRed('D '+str(D)+' C '+str(C))
first_drop=0.5
if os.environ.has_key('first_drop'):
first_drop = float(os.environ['first_drop'])
last_drop=1
if os.environ.has_key('last_drop'):
last_drop = float(os.environ['last_drop'])
nkerns_1=96
if os.environ.has_key('nkerns_1'):
nkerns_1 = int(os.environ['nkerns_1'])
nkerns_2=96
if os.environ.has_key('nkerns_2'):
nkerns_2 = int(os.environ['nkerns_2'])
n_z=512
if os.environ.has_key('n_z'):
n_z = int(os.environ['n_z'])
opt_med='adam'
if os.environ.has_key('opt_med'):
opt_med = os.environ['opt_med']
train_logvar=True
if os.environ.has_key('train_logvar'):
train_logvar = bool(int(os.environ['train_logvar']))
std = 2e-2
if os.environ.has_key('std'):
std = os.environ['std']
Loss_L = 1
if os.environ.has_key('Loss_L'):
Loss_L = int(os.environ['Loss_L'])
pattern = 'hinge'
if os.environ.has_key('pattern'):
pattern = os.environ['pattern']
#cp->cd->cpd->cd->c
nkerns=[nkerns_1, nkerns_1, nkerns_1, nkerns_2, nkerns_2]
drops=[0, 1, 1, 1, 0, 1]
drop_p=[1, first_drop, first_drop, first_drop, 1, last_drop]
n_hidden=[n_z]
logdir = 'results/supervised/cmmva/svhn/cmmva_6layer_'+dataset+pattern+'_D_'+str(D)+'_C_'+str(C)+'_'#+str(nkerns)+str(n_hidden)+'_'+str(weight_decay)+'_'+str(learning_rate)+'_'
#if predir is not None:
# logdir +='pre_'
#if dropout_flag == 1:
# logdir += ('dropout_'+str(drops)+'_')
# logdir += ('drop_p_'+str(drop_p)+'_')
#logdir += ('trainvar_'+str(train_logvar)+'_')
#logdir += (opt_med+'_')
#logdir += (str(Loss_L)+'_')
#if super_predir is not None:
# logdir += (str(super_preepoch)+'_')
logdir += str(int(time.time()))+'/'
if not os.path.exists(logdir): os.makedirs(logdir)
print 'logdir:', logdir, 'predir', predir
print 'cmmva_6layer_svhn_fix', nkerns, n_hidden, seed, dropout_flag, drops, drop_p
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'logdir:', logdir, 'predir', predir
print >>f, 'cmmva_6layer_svhn_fix', nkerns, n_hidden, seed, dropout_flag, drops, drop_p
color.printRed('dataset '+dataset)
datasets = datapy.load_data_svhn(dataset, have_matrix=True)
train_set_x, train_set_y, train_y_matrix = datasets[0]
test_set_x, test_set_y, test_y_matrix = datasets[1]
valid_set_x, valid_set_y, valid_y_matrix = datasets[2]
#datasets = datapy.load_data_svhn(dataset, have_matrix=False)
#train_set_x, train_set_y = datasets[0]
#test_set_x, test_set_y = datasets[1]
#valid_set_x, valid_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
random_z = T.matrix('random_z')
y_matrix = T.imatrix('y_matrix')
drop = T.iscalar('drop')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x.reshape((batch_size, n_channels, dim_h, dim_w))
recg_layer = []
cnn_output = []
l = []
d = []
#1
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, n_channels, dim_h, dim_w),
filter_shape=(nkerns[0], n_channels, 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=activation,
std=std
))
if drops[0]==1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x, drop=drop, rng=rng_share, p=drop_p[0]))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
l+=[1, 2]
d+=[1, 0]
#2
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[0], 16, 16),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation,
std=std
))
if drops[1]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[1]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 0]
#3
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[1], 16, 16),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation,
std=std
))
if drops[2]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[2]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 0]
#4
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[2], 8, 8),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation,
std=std
))
if drops[3]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[3]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 0]
#5
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[3], 8, 8),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation,
std=std
))
if drops[4]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[4]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 0]
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
activations.append(mlp_input_x)
classifier = Pegasos.Pegasos(
input= activations[-1],
rng=rng,
n_in=nkerns[-1]*4*4,
n_out=n_classes,
weight_decay=0,
loss=Loss_L,
pattern=pattern
)
l+=[1, 2]
d+=[1, 0]
#stochastic layer
recg_layer.append(GaussianHidden.GaussianHidden(
rng=rng,
input=mlp_input_x,
n_in=4*4*nkerns[-1],
n_out=n_hidden[0],
activation=None
))
l+=[1, 2]
d+=[1, 0]
l+=[1, 2]
d+=[1, 0]
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[-1],
n_out=4*4*nkerns[-1],
activation=activation
))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
l+=[1, 2]
d+=[1, 0]
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 4, 4))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 4, 4))
#1
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-1], 4, 4),
filter_shape=(nkerns[-2], nkerns[-1], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 0]
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-2], 8, 8),
filter_shape=(nkerns[-3], nkerns[-2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 0]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-3], 8, 8),
filter_shape=(nkerns[-4], nkerns[-3], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 0]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-4], 16, 16),
filter_shape=(nkerns[-5], nkerns[-4], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 0]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#5-1 stochastic layer
# for this layer, the activation is None to get a Guassian mean
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-5], 16, 16),
filter_shape=(n_channels, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=None
))
l+=[1, 2]
d+=[1, 0]
x_mean=gene_layer[-1].output(input=z_output[-1])
random_x_mean=gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch)
#5-2 stochastic layer
# for this layer, the activation is None to get logvar
if train_logvar:
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-5], 16, 16),
filter_shape=(n_channels, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=None
))
l+=[1, 2]
d+=[1, 0]
x_logvar=gene_layer[-1].output(input=z_output[-1])
random_x_logvar=gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch)
else:
x_logvar = theano.shared(np.ones((batch_size, n_channels, dim_h, dim_w), dtype='float32'))
random_x_logvar = theano.shared(np.ones((n_batch, n_channels, dim_h, dim_w), dtype='float32'))
gene_layer.append(NoParamsGaussianVisiable.NoParamsGaussianVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=x_mean, logvar=x_logvar, data=input_x)
random_x = gene_layer[-1].sample_x(rng_share=rng_share, mean=random_x_mean, logvar=random_x_logvar)
#L = (logpx + logpz - logqz).sum()
lowerbound = (
(logpx + recg_layer[-1].logpz - recg_layer[-1].logqz).mean()
)
hinge_loss = classifier.hinge_loss(10, y, y_matrix)
cost = D * lowerbound - C * hinge_loss
px = (logpx.mean())
pz = (recg_layer[-1].logpz.mean())
qz = (- recg_layer[-1].logqz.mean())
super_params=[]
for r in recg_layer[:-1]:
super_params+=r.params
super_params+=classifier.params
params=[]
for g in gene_layer:
params+=g.params
for r in recg_layer:
params+=r.params
params+=classifier.params
grads = [T.grad(cost, param) for param in params]
l_r = theano.shared(np.asarray(learning_rate, dtype=np.float32))
#get_optimizer = optimizer.get_adam_optimizer(learning_rate=learning_rate)
if opt_med=='adam':
get_optimizer = optimizer_separated.get_adam_optimizer_max(learning_rate=l_r, decay1 = 0.1, decay2 = 0.001, weight_decay=weight_decay)
elif opt_med=='mom':
get_optimizer = optimizer_separated.get_momentum_optimizer_max(learning_rate=l_r, weight_decay=weight_decay)
updates = get_optimizer(w=params,g=grads, l=l, d=d)
# 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), lowerbound, hinge_loss, cost],
#outputs=layer[-1].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],
y_matrix: test_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
valid_model = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, hinge_loss, cost],
#outputs=layer[-1].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],
y_matrix: valid_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
valid_error = theano.function(
inputs=[index],
outputs=classifier.errors(y),
#outputs=layer[-1].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],
#y_matrix: valid_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
'''
Save parameters and activations
'''
pog = []
for (p,g) in zip(params, grads):
pog.append(p.max())
pog.append((p**2).mean())
pog.append((g**2).mean())
pog.append((T.sqrt(pog[-2] / pog[-1]))/ 1e3)
paramovergrad = theano.function(
inputs=[index],
outputs=pog,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size],
y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag)
}
)
parameters = theano.function(
inputs=[],
outputs=params,
)
generation_check = theano.function(
inputs=[index],
outputs=[x, x_mean.flatten(2), x_logvar.flatten(2)],
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
#y: train_set_y[index * batch_size: (index + 1) * batch_size],
#y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
test_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# 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`
debug_model = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, px, pz, qz, hinge_loss, 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],
y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag)
}
)
random_generation = theano.function(
inputs=[random_z],
outputs=[random_x_mean.flatten(2), random_x.flatten(2)],
givens={
#drop: np.cast['int32'](0)
}
)
train_bound_without_dropout = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, hinge_loss, cost],
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size],
y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
train_model = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, hinge_loss, cost, px, pz, qz, z],
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],
y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag),
}
)
##################
# Pretrain MODEL #
##################
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
pre_train = np.load(predir+'model.npz')
pre_train = pre_train['model']
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0)
print '------------------', tmp
if super_predir is not None:
color.printBlue('... setting parameters')
color.printBlue(super_predir)
pre_train = np.load(super_predir+'svhn_model-'+str(super_preepoch)+'.npz')
pre_train = pre_train['model']
for (para, pre) in zip(super_params, pre_train):
para.set_value(pre)
this_test_losses = [test_model(i) for i in xrange(n_test_batches)]
this_test_score = np.mean(this_test_losses, axis=0)
#print predir
print 'preepoch', super_preepoch, 'pre_test_score', this_test_score
with open(logdir+'hook.txt', 'a') as f:
print >>f, predir
print >>f, 'preepoch', super_preepoch, 'pre_test_score', this_test_score
###############
# TRAIN MODEL #
###############
print '... training'
validation_frequency = n_train_batches
predy_valid_stats = [1, 1, 0]
start_time = time.clock()
NaN_count = 0
epoch = 0
threshold = 0
generatition_frequency = 1
if predir is not None:
threshold = 0
color.printRed('threshold, '+str(threshold) +
' generatition_frequency, '+str(generatition_frequency)
+' validation_frequency, '+str(validation_frequency))
done_looping = False
n_epochs = 80
decay_epochs = 40
record = 0
'''
print 'test initialization...'
pre_model = parameters()
for i in xrange(len(pre_model)):
pre_model[i] = np.asarray(pre_model[i])
print pre_model[i].shape, np.mean(pre_model[i]), np.var(pre_model[i])
print 'end test...'
'''
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
minibatch_avg_cost = 0
train_error = 0
train_lowerbound = 0
train_hinge_loss = 0
_____z = 0
pxx = 0
pzz = 0
qzz = 0
preW = None
currentW = None
tmp_start1 = time.clock()
if epoch == 30:
validation_frequency = n_train_batches/5
if epoch == 50:
validation_frequency = n_train_batches/10
if epoch == 30 or epoch == 50 or epoch == 70 or epoch == 90:
record = epoch
l_r.set_value(np.cast['float32'](l_r.get_value()/3.0))
print '---------', epoch, l_r.get_value()
with open(logdir+'hook.txt', 'a') as f:
print >>f,'---------', epoch, l_r.get_value()
'''
test_epoch = epoch - decay_epochs
if test_epoch > 0 and test_epoch % 5 == 0:
l_r.set_value(np.cast['float32'](l_r.get_value()/3.0))
print '---------------', l_r.get_value()
with open(logdir+'hook.txt', 'a') as f:
print >>f, '---------------', l_r.get_value()
'''
for minibatch_index in xrange(n_train_batches):
e, l, h, ttt, tpx, tpz, tqz, _z = train_model(minibatch_index)
pxx+=tpx
pzz+=tpz
qzz+=tqz
#_____z += (np.asarray(_z)**2).sum() / (n_hidden[-1] * batch_size)
train_error += e
train_lowerbound += l
train_hinge_loss += h
minibatch_avg_cost += ttt
'''
llll = debug_model(minibatch_index)
with open(logdir+'hook.txt', 'a') as f:
print >>f,'[]', llll
'''
if math.isnan(ttt):
color.printRed('--------'+str(epoch)+'--------'+str(minibatch_index))
exit()
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
'''
if (minibatch_index <11):
preW = currentW
currentW = parameters()
for i in xrange(len(currentW)):
currentW[i] = np.asarray(currentW[i]).astype(np.float32)
if preW is not None:
for (c,p) in zip(currentW, preW):
#print minibatch_index, (c**2).mean(), ((c-p)**2).mean(), np.sqrt((c**2).mean()/((c-p)**2).mean())
with open(logdir+'delta_w.txt', 'a') as f:
print >>f,minibatch_index, (c**2).mean(), ((c-p)**2).mean(), np.sqrt((c**2).mean()/((c-p)**2).mean())
'''
# check valid error only, to speed up
'''
if (iter + 1) % validation_frequency != 0 and (iter + 1) %(validation_frequency/10) == 0:
vt = [valid_error(i) for i in xrange(n_valid_batches)]
vt = np.mean(vt)
print 'quick valid error', vt
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'quick valid error', vt
print 'So far best model', predy_valid_stats
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'So far best model', predy_valid_stats
'''
if (iter + 1) % validation_frequency == 0:
print minibatch_index, 'stochastic training error', train_error/float(minibatch_index), train_lowerbound/float(minibatch_index), train_hinge_loss/float(minibatch_index), minibatch_avg_cost /float(minibatch_index), pxx/float(minibatch_index), pzz/float(minibatch_index), qzz/float(minibatch_index)#, 'z_norm', _____z/float(minibatch_index)
with open(logdir+'hook.txt', 'a') as f:
print >>f, minibatch_index, 'stochastic training error', train_error/float(minibatch_index), train_lowerbound/float(minibatch_index), train_hinge_loss/float(minibatch_index), minibatch_avg_cost /float(minibatch_index), pxx/float(minibatch_index), pzz/float(minibatch_index), qzz/float(minibatch_index)#, 'z_norm', _____z/float(minibatch_index)
valid_stats = [valid_model(i) for i in xrange(n_valid_batches)]
this_valid_stats = np.mean(valid_stats, axis=0)
print epoch, minibatch_index, 'validation stats', this_valid_stats
#print tmp
with open(logdir+'hook.txt', 'a') as f:
print >>f, epoch, minibatch_index, 'validation stats', this_valid_stats
print 'So far best model', predy_valid_stats
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'So far best model', predy_valid_stats
if this_valid_stats[0] < predy_valid_stats[0]:
test_stats = [test_model(i) for i in xrange(n_test_batches)]
this_test_stats = np.mean(test_stats, axis=0)
predy_valid_stats[0] = this_valid_stats[0]
predy_valid_stats[1] = this_test_stats[0]
predy_valid_stats[2] = epoch
record = epoch
print 'Update best model', this_test_stats
with open(logdir+'hook.txt', 'a') as f:
print >>f,'Update best model', this_test_stats
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
#print model[i].shape, np.mean(model[i]), np.var(model[i])
np.savez(logdir+'best-model', model=model)
genezero = generation_check(0)
with open(logdir+'gene_check.txt', 'a') as f:
print >>f, 'epoch-----------------------', epoch
print >>f, 'x', 'x_mean', 'x_logvar'
'''
for i in xrange(len(genezero)):
genezero[i] = np.asarray(genezero[i])
with open(logdir+'gene_check.txt', 'a') as f:
print >>f, genezero[i].max(), genezero[i].min(), genezero[i].mean()
with open(logdir+'gene_check.txt', 'a') as f:
print >>f, 'norm', np.sqrt(((genezero[0]- genezero[1])**2).sum())
'''
if epoch==1:
xxx = genezero[0]
image = paramgraphics.mat_to_img(xxx.T, dim_input, colorImg=colorImg, scale=True)
image.save(logdir+'data.png', 'PNG')
if epoch%1==0:
tail='-'+str(epoch)+'.png'
xxx_now = genezero[1]
image = paramgraphics.mat_to_img(xxx_now.T, dim_input, colorImg=colorImg, scale=True)
image.save(logdir+'data_re'+tail, 'PNG')
if math.isnan(minibatch_avg_cost):
NaN_count+=1
color.printRed("NaN detected. Reverting to saved best parameters")
print '---------------NaN_count:', NaN_count
with open(logdir+'hook.txt', 'a') as f:
print >>f, '---------------NaN_count:', NaN_count
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0)
print '------------------NaN check:', tmp
with open(logdir+'hook.txt', 'a') as f:
print >>f, '------------------NaN check:', tmp
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
print model[i].shape, np.mean(model[i]), np.var(model[i])
print np.max(model[i]), np.min(model[i])
print np.all(np.isfinite(model[i])), np.any(np.isnan(model[i]))
with open(logdir+'hook.txt', 'a') as f:
print >>f, model[i].shape, np.mean(model[i]), np.var(model[i])
print >>f, np.max(model[i]), np.min(model[i])
print >>f, np.all(np.isfinite(model[i])), np.any(np.isnan(model[i]))
best_before = np.load(logdir+'model.npz')
best_before = best_before['model']
for (para, pre) in zip(params, best_before):
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0)
print '------------------', tmp
return
if epoch%1==0:
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
np.savez(logdir+'model-'+str(epoch), model=model)
tmp_start4=time.clock()
if epoch % generatition_frequency == 0:
tail='-'+str(epoch)+'.png'
random_z = np.random.standard_normal((n_batch, n_hidden[-1])).astype(np.float32)
_x_mean, _x = random_generation(random_z)
#print _x.shape
#print _x_mean.shape
image = paramgraphics.mat_to_img(_x.T, dim_input, colorImg=colorImg, scale=True)
image.save(logdir+'samples'+tail, 'PNG')
image = paramgraphics.mat_to_img(_x_mean.T, dim_input, colorImg=colorImg, scale=True)
image.save(logdir+'mean_samples'+tail, 'PNG')
#print 'generation_time', time.clock() - tmp_start4
#print 'one epoch time', time.clock() - tmp_start1
end_time = time.clock()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
if NaN_count > 0:
print '---------------NaN_count:', NaN_count
with open(logdir+'hook.txt', 'a') as f:
print >>f, '---------------NaN_count:', NaN_count
def cva_6layer_dropout_mnist_60000(seed=0,
dropout_flag=1,
drop_inverses_flag=0,
learning_rate=3e-4,
predir=None,
n_batch=144,
dataset='mnist.pkl.gz',
batch_size=500,
nkerns=[20, 50],
n_hidden=[500, 50]):
"""
Implementation of convolutional VA
"""
#cp->cd->cpd->cd->c
nkerns = [32, 32, 64, 64, 64]
drops = [1, 0, 1, 0, 0]
#skerns=[5, 3, 3, 3, 3]
#pools=[2, 1, 1, 2, 1]
#modes=['same']*5
n_hidden = [500, 50]
drop_inverses = [
1,
]
# 28->12->12->5->5/5*5*64->500->50->500->5*5*64/5->5->12->12->28
if dataset == 'mnist.pkl.gz':
dim_input = (28, 28)
colorImg = False
logdir = 'results/supervised/cva/mnist/cva_6layer_mnist_60000' + str(
nkerns) + str(n_hidden) + '_' + str(learning_rate) + '_'
if predir is not None:
logdir += 'pre_'
if dropout_flag == 1:
logdir += ('dropout_' + str(drops) + '_')
if drop_inverses_flag == 1:
logdir += ('inversedropout_' + str(drop_inverses) + '_')
logdir += str(int(time.time())) + '/'
if not os.path.exists(logdir): os.makedirs(logdir)
print 'logdir:', logdir, 'predir', predir
print 'cva_6layer_mnist_60000', nkerns, n_hidden, seed, drops, drop_inverses, dropout_flag, drop_inverses_flag
with open(logdir + 'hook.txt', 'a') as f:
print >> f, 'logdir:', logdir, 'predir', predir
print >> f, 'cva_6layer_mnist_60000', nkerns, n_hidden, seed, drops, drop_inverses, dropout_flag, drop_inverses_flag
datasets = datapy.load_data_gpu_60000(dataset, have_matrix=True)
train_set_x, train_set_y, train_y_matrix = datasets[0]
valid_set_x, valid_set_y, valid_y_matrix = datasets[1]
test_set_x, test_set_y, test_y_matrix = 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
random_z = T.matrix('random_z')
drop = T.iscalar('drop')
drop_inverse = T.iscalar('drop_inverse')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x.reshape((batch_size, 1, 28, 28))
recg_layer = []
cnn_output = []
#1
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
border_mode='valid',
activation=activation))
if drops[0] == 1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x,
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
#2
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[1] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#3
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[1], 12, 12),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='valid',
activation=activation))
if drops[2] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#4
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[2], 5, 5),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[3] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#5
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[3], 5, 5),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[4] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
#1
recg_layer.append(
FullyConnected.FullyConnected(rng=rng,
n_in=5 * 5 * nkerns[-1],
n_out=n_hidden[0],
activation=activation))
if drops[-1] == 1:
activations.append(recg_layer[-1].drop_output(input=mlp_input_x,
drop=drop,
rng=rng_share))
else:
activations.append(recg_layer[-1].output(input=mlp_input_x))
#stochastic layer
recg_layer.append(
GaussianHidden.GaussianHidden(rng=rng,
input=activations[-1],
n_in=n_hidden[0],
n_out=n_hidden[1],
activation=None))
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(
FullyConnected.FullyConnected(rng=rng,
n_in=n_hidden[1],
n_out=n_hidden[0],
activation=activation))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
#2
gene_layer.append(
FullyConnected.FullyConnected(rng=rng,
n_in=n_hidden[0],
n_out=5 * 5 * nkerns[-1],
activation=activation))
if drop_inverses[0] == 1:
z_output.append(gene_layer[-1].drop_output(input=z_output[-1],
drop=drop_inverse,
rng=rng_share))
random_z_output.append(gene_layer[-1].drop_output(
input=random_z_output[-1], drop=drop_inverse, rng=rng_share))
else:
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(
gene_layer[-1].output(input=random_z_output[-1]))
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 5, 5))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 5, 5))
#1
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-1], 5, 5),
filter_shape=(nkerns[-2], nkerns[-1], 3,
3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(
input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-2], 5, 5),
filter_shape=(nkerns[-3], nkerns[-2], 3,
3),
poolsize=(2, 2),
border_mode='full',
activation=activation))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-3], 12,
12),
filter_shape=(nkerns[-4], nkerns[-3], 3,
3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-4], 12,
12),
filter_shape=(nkerns[-5], nkerns[-4], 3,
3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#5 stochastic layer
# for the last layer, the nonliearity should be sigmoid to achieve mean of Bernoulli
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-5], 12,
12),
filter_shape=(1, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='full',
activation=nonlinearity.sigmoid))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
gene_layer.append(
NoParamsBernoulliVisiable.NoParamsBernoulliVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=z_output[-1], data=input_x)
# 4-D tensor of random generation
random_x_mean = random_z_output[-1]
random_x = gene_layer[-1].sample_x(rng_share, random_x_mean)
#L = (logpx + logpz - logqz).sum()
cost = ((logpx + recg_layer[-1].logpz - recg_layer[-1].logqz).sum())
px = (logpx.sum())
pz = (recg_layer[-1].logpz.sum())
qz = (-recg_layer[-1].logqz.sum())
params = []
for g in gene_layer:
params += g.params
for r in recg_layer:
params += r.params
gparams = [T.grad(cost, param) for param in params]
weight_decay = 1.0 / n_train_batches
l_r = theano.shared(np.asarray(learning_rate, dtype=np.float32))
#get_optimizer = optimizer.get_adam_optimizer(learning_rate=learning_rate)
get_optimizer = optimizer.get_adam_optimizer_max(learning_rate=l_r,
decay1=0.1,
decay2=0.001,
weight_decay=weight_decay,
epsilon=1e-8)
with open(logdir + 'hook.txt', 'a') as f:
print >> f, 'AdaM', learning_rate, weight_decay
updates = get_optimizer(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=cost,
#outputs=layer[-1].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],
#y_matrix: test_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
})
validate_model = theano.function(
inputs=[index],
outputs=cost,
#outputs=layer[-1].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],
#y_matrix: valid_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
})
'''
Save parameters and activations
'''
parameters = theano.function(
inputs=[],
outputs=params,
)
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: train_set_y[index * batch_size: (index + 1) * batch_size]
})
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: valid_set_y[index * batch_size: (index + 1) * batch_size]
})
test_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: test_set_y[index * batch_size: (index + 1) * batch_size]
})
# 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`
debug_model = theano.function(
inputs=[index],
outputs=[cost, px, pz, qz],
#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],
#y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag),
drop_inverse: np.cast['int32'](drop_inverses_flag)
})
random_generation = theano.function(
inputs=[random_z],
outputs=[random_x_mean.flatten(2),
random_x.flatten(2)],
givens={
#drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
})
train_bound_without_dropout = theano.function(
inputs=[index],
outputs=cost,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
#y: train_set_y[index * batch_size: (index + 1) * batch_size],
#y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
})
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],
#y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag),
drop_inverse: np.cast['int32'](drop_inverses_flag)
})
##################
# Pretrain MODEL #
##################
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
pre_train = np.load(predir + 'model.npz')
pre_train = pre_train['model']
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print '------------------', tmp
###############
# 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_bound = -1000000.0
best_iter = 0
test_score = 0.
start_time = time.clock()
NaN_count = 0
epoch = 0
threshold = 0
validation_frequency = 1
generatition_frequency = 10
if predir is not None:
threshold = 0
color.printRed('threshold, ' + str(threshold) +
' generatition_frequency, ' + str(generatition_frequency) +
' validation_frequency, ' + str(validation_frequency))
done_looping = False
n_epochs = 600
decay_epochs = 500
'''
print 'test initialization...'
pre_model = parameters()
for i in xrange(len(pre_model)):
pre_model[i] = np.asarray(pre_model[i])
print pre_model[i].shape, np.mean(pre_model[i]), np.var(pre_model[i])
print 'end test...'
'''
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
minibatch_avg_cost = 0
tmp_start1 = time.clock()
test_epoch = epoch - decay_epochs
if test_epoch > 0 and test_epoch % 10 == 0:
print l_r.get_value()
with open(logdir + 'hook.txt', 'a') as f:
print >> f, l_r.get_value()
l_r.set_value(np.cast['float32'](l_r.get_value() / 3.0))
for minibatch_index in xrange(n_train_batches):
#print minibatch_index
'''
color.printRed('lalala')
xxx = dims(minibatch_index)
print xxx.shape
'''
#print n_train_batches
minibatch_avg_cost += train_model(minibatch_index)
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if math.isnan(minibatch_avg_cost):
NaN_count += 1
color.printRed("NaN detected. Reverting to saved best parameters")
print '---------------NaN_count:', NaN_count
with open(logdir + 'hook.txt', 'a') as f:
print >> f, '---------------NaN_count:', NaN_count
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print '------------------NaN check:', tmp
with open(logdir + 'hook.txt', 'a') as f:
print >> f, '------------------NaN check:', tmp
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
print model[i].shape, np.mean(model[i]), np.var(model[i])
print np.max(model[i]), np.min(model[i])
print np.all(np.isfinite(model[i])), np.any(np.isnan(model[i]))
with open(logdir + 'hook.txt', 'a') as f:
print >> f, model[i].shape, np.mean(model[i]), np.var(
model[i])
print >> f, np.max(model[i]), np.min(model[i])
print >> f, np.all(np.isfinite(model[i])), np.any(
np.isnan(model[i]))
best_before = np.load(logdir + 'model.npz')
best_before = best_before['model']
for (para, pre) in zip(params, best_before):
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print '------------------', tmp
return
#print 'optimization_time', time.clock() - tmp_start1
print epoch, 'stochastic training error', minibatch_avg_cost / float(
n_train_batches * batch_size)
with open(logdir + 'hook.txt', 'a') as f:
print >> f, epoch, 'stochastic training error', minibatch_avg_cost / float(
n_train_batches * batch_size)
if epoch % validation_frequency == 0:
tmp_start2 = time.clock()
test_losses = [test_model(i) for i in xrange(n_test_batches)]
this_test_bound = np.mean(test_losses) / float(batch_size)
#tmp = [debug_model(i) for i
# in xrange(n_train_batches)]
#tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print epoch, 'test bound', this_test_bound
#print tmp
with open(logdir + 'hook.txt', 'a') as f:
print >> f, epoch, 'test bound', this_test_bound
if epoch % 100 == 0:
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
np.savez(logdir + 'model-' + str(epoch), model=model)
for i in xrange(n_train_batches):
if i == 0:
train_features = np.asarray(train_activations(i))
else:
train_features = np.vstack(
(train_features, np.asarray(train_activations(i))))
for i in xrange(n_valid_batches):
if i == 0:
valid_features = np.asarray(valid_activations(i))
else:
valid_features = np.vstack(
(valid_features, np.asarray(valid_activations(i))))
for i in xrange(n_test_batches):
if i == 0:
test_features = np.asarray(test_activations(i))
else:
test_features = np.vstack(
(test_features, np.asarray(test_activations(i))))
np.save(logdir + 'train_features', train_features)
np.save(logdir + 'valid_features', valid_features)
np.save(logdir + 'test_features', test_features)
tmp_start4 = time.clock()
if epoch % generatition_frequency == 0:
tail = '-' + str(epoch) + '.png'
random_z = np.random.standard_normal(
(n_batch, n_hidden[-1])).astype(np.float32)
_x_mean, _x = random_generation(random_z)
#print _x.shape
#print _x_mean.shape
image = paramgraphics.mat_to_img(_x.T,
dim_input,
colorImg=colorImg)
image.save(logdir + 'samples' + tail, 'PNG')
image = paramgraphics.mat_to_img(_x_mean.T,
dim_input,
colorImg=colorImg)
image.save(logdir + 'mean_samples' + tail, 'PNG')
#print 'generation_time', time.clock() - tmp_start4
end_time = time.clock()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
if NaN_count > 0:
print '---------------NaN_count:', NaN_count
with open(logdir + 'hook.txt', 'a') as f:
print >> f, '---------------NaN_count:', NaN_count
batch3_data = x
batch3_labels = []
for i in np.arange(len(y)):
batch3_labels.append(y[i])
batch3_data = np.asarray(batch3_data).T
batch3_labels = np.asarray(batch3_labels)
print 'Check n x f'
print batch1_data.shape
print batch1_labels.shape
print batch2_data.shape
print batch2_labels.shape
print batch3_data.shape
print batch3_labels.shape
image = paramgraphics.mat_to_img(batch1_data[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_train.png', 'PNG')
image = paramgraphics.mat_to_img(batch2_data[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_valid.png', 'PNG')
image = paramgraphics.mat_to_img(batch3_data[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_test.png', 'PNG')
if preprocessing == 'gcn_var':
batch1_data = pypp.global_contrast_normalize(batch1_data, subtract_mean=True, use_std=True)
batch2_data = pypp.global_contrast_normalize(batch2_data, subtract_mean=True, use_std=True)
batch3_data = pypp.global_contrast_normalize(batch3_data, subtract_mean=True, use_std=True)
elif preprocessing == 'gcn_norm':
batch1_data = pypp.global_contrast_normalize(batch1_data, subtract_mean=True)
batch2_data = pypp.global_contrast_normalize(batch2_data, subtract_mean=True)
batch3_data = pypp.global_contrast_normalize(batch3_data, subtract_mean=True)
print batch1_data.shape
print line
with open(logfile, 'a') as f:
f.write(line + "\n")
# random generation for visualization
import util.paramgraphics as paramgraphics
import scipy.io as sio
tail = '-' + str(epoch) + '.png'
_x_mean, _x = generate_model(num_generation)
_x_mean = _x_mean.reshape((num_generation, -1))
_x = _x.reshape((num_generation, -1))
sio.savemat(
os.path.join(res_out, 'array_images-' + str(epoch) + '.mat'),
{'data': _x_mean})
image = paramgraphics.mat_to_img(_x.T,
dim_input,
colorImg=colorImg,
scale=generation_scale)
image.save(os.path.join(res_out, 'samples' + tail), 'PNG')
image = paramgraphics.mat_to_img(_x_mean.T,
dim_input,
colorImg=colorImg,
scale=generation_scale)
image.save(os.path.join(res_out, 'mean_samples' + tail), 'PNG')
'''
if dataset in ['norb_48', 'norb_96']:
image = paramgraphics.mat_to_img(_x_mean.T, dim_input, colorImg=colorImg, scale=True)
image.save(os.path.join(res_out, 'mean_samples_scale'+tail), 'PNG')
import nn_search
if epoch % 250 == 0:
nn = nn_search.nn_search(_x_mean, train_x)
image = paramgraphics.mat_to_img(nn.T, dim_input, colorImg=colorImg, scale=True)
def c_6layer_svhn_imputation(seed=0, ctype='cva',
pertub_type=5, pertub_prob=0, pertub_prob1=16, visualization_times=20,
denoise_times=200, predir=None, n_batch=900, batch_size=500):
"""
Missing data imputation
"""
'''
svhn
'''
n_channels = 3
colorImg = True
dim_w = 32
dim_h = 32
dim_input=(dim_h, dim_w)
n_classes = 10
first_drop=0.6
if os.environ.has_key('first_drop'):
first_drop = float(os.environ['first_drop'])
last_drop=1
if os.environ.has_key('last_drop'):
last_drop = float(os.environ['last_drop'])
nkerns_1=96
if os.environ.has_key('nkerns_1'):
nkerns_1 = int(os.environ['nkerns_1'])
nkerns_2=96
if os.environ.has_key('nkerns_2'):
nkerns_2 = int(os.environ['nkerns_2'])
opt_med='mom'
if os.environ.has_key('opt_med'):
opt_med = os.environ['opt_med']
train_logvar=True
if os.environ.has_key('train_logvar'):
train_logvar = bool(int(os.environ['train_logvar']))
dataset='svhnlcn'
if os.environ.has_key('dataset'):
dataset = os.environ['dataset']
n_z=256
if os.environ.has_key('n_z'):
n_z = int(os.environ['n_z'])
#cp->cd->cpd->cd->c
nkerns=[nkerns_1, nkerns_1, nkerns_1, nkerns_2, nkerns_2]
drops=[0, 1, 1, 1, 0, 1]
drop_p=[1, first_drop, first_drop, first_drop, 1, last_drop]
n_hidden=[n_z]
logdir = 'results/imputation/'+ctype+'/svhn/'+ctype+'_6layer_'+dataset+'_'
logdir += str(int(time.time()))+'/'
if not os.path.exists(logdir): os.makedirs(logdir)
print predir
with open(logdir+'hook.txt', 'a') as f:
print >>f, predir
color.printRed('dataset '+dataset)
test_set_x, test_set_x_pertub, pertub_label, pertub_number = datapy.load_pertub_data_svhn(dirs='data_imputation/', dataset=dataset, pertub_type=pertub_type, pertub_prob=pertub_prob, pertub_prob1=pertub_prob1)
pixel_max, pixel_min = datapy.load_max_min(dirs='data_imputation/', dataset=dataset, pertub_prob=pertub_prob)
# 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
random_z = T.matrix('random_z')
x_pertub = T.matrix('x_pertub') # the data is presented as rasterized images
p_label = T.matrix('p_label')
drop = T.iscalar('drop')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x_pertub.reshape((batch_size, n_channels, dim_h, dim_w))
recg_layer = []
cnn_output = []
l = []
d = []
#1
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, n_channels, dim_h, dim_w),
filter_shape=(nkerns[0], n_channels, 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=activation
))
if drops[0]==1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x, drop=drop, rng=rng_share, p=drop_p[0]))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
l+=[1, 2]
d+=[1, 1]
#2
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[0], 16, 16),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[1]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[1]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 1]
#3
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[1], 16, 16),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation
))
if drops[2]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[2]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 1]
#4
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[2], 8, 8),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[3]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[3]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 1]
#5
'''
--------------------- (2,2) or (4,4)
'''
recg_layer.append(ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[3], 8, 8),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation
))
if drops[4]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share, p=drop_p[4]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l+=[1, 2]
d+=[1, 1]
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
activations.append(mlp_input_x)
#1
'''
---------------------No MLP
'''
'''
recg_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in= 4 * 4 * nkerns[-1],
n_out=n_hidden[0],
activation=activation
))
if drops[-1]==1:
activations.append(recg_layer[-1].drop_output(input=mlp_input_x, drop=drop, rng=rng_share, p=drop_p[-1]))
else:
activations.append(recg_layer[-1].output(input=mlp_input_x))
'''
#stochastic layer
recg_layer.append(GaussianHidden.GaussianHidden(
rng=rng,
input=activations[-1],
n_in=4 * 4 * nkerns[-1],
n_out=n_hidden[0],
activation=None
))
l+=[1, 2]
d+=[1, 1]
l+=[1, 2]
d+=[1, 1]
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[0],
n_out = 4*4*nkerns[-1],
activation=activation
))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
l+=[1, 2]
d+=[1, 1]
#2
'''
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[0],
n_out = 4*4*nkerns[-1],
activation=activation
))
if drop_inverses[0]==1:
z_output.append(gene_layer[-1].drop_output(input=z_output[-1], drop=drop_inverse, rng=rng_share))
random_z_output.append(gene_layer[-1].drop_output(input=random_z_output[-1], drop=drop_inverse, rng=rng_share))
else:
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output(input=random_z_output[-1]))
'''
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 4, 4))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 4, 4))
#1
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-1], 4, 4),
filter_shape=(nkerns[-2], nkerns[-1], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 1]
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-2], 8, 8),
filter_shape=(nkerns[-3], nkerns[-2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 1]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-3], 8, 8),
filter_shape=(nkerns[-4], nkerns[-3], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 1]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-4], 16, 16),
filter_shape=(nkerns[-5], nkerns[-4], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
l+=[1, 2]
d+=[1, 1]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#5-1 stochastic layer
# for this layer, the activation is None to get a Guassian mean
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-5], 16, 16),
filter_shape=(n_channels, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=None
))
l+=[1, 2]
d+=[1, 1]
x_mean=gene_layer[-1].output(input=z_output[-1])
random_x_mean=gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch)
#5-2 stochastic layer
# for this layer, the activation is None to get logvar
if train_logvar:
gene_layer.append(UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-5], 16, 16),
filter_shape=(n_channels, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=None
))
l+=[1, 2]
d+=[1, 1]
x_logvar=gene_layer[-1].output(input=z_output[-1])
random_x_logvar=gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch)
else:
x_logvar = theano.shared(np.ones((batch_size, n_channels, dim_h, dim_w), dtype='float32'))
random_x_logvar = theano.shared(np.ones((n_batch, n_channels, dim_h, dim_w), dtype='float32'))
gene_layer.append(NoParamsGaussianVisiable.NoParamsGaussianVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=x_mean, logvar=x_logvar, data=input_x)
random_x = gene_layer[-1].sample_x(rng_share=rng_share, mean=random_x_mean, logvar=random_x_logvar)
x_denoised = p_label*x+(1-p_label)*x_mean.flatten(2)
mse = ((x - x_denoised)**2).sum() / pertub_number
params=[]
for g in gene_layer:
params+=g.params
for r in recg_layer:
params+=r.params
'''
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: train_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
'''
'''
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: valid_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
'''
test_activations = theano.function(
inputs=[x_pertub],
outputs=T.concatenate(activations, axis=1),
givens={
drop: np.cast['int32'](0)
}
)
imputation_model = theano.function(
inputs=[index, x_pertub],
outputs=[x_denoised, mse],
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
p_label:pertub_label[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
}
)
##################
# Pretrain MODEL #
##################
model_epoch = 100
if os.environ.has_key('model_epoch'):
model_epoch = int(os.environ['model_epoch'])
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
if model_epoch == -1:
pre_train = np.load(predir+'best-model.npz')
else:
pre_train = np.load(predir+'model-'+str(model_epoch)+'.npz')
pre_train = pre_train['model']
if ctype == 'cva':
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
elif ctype == 'cmmva':
for (para, pre) in zip(params, pre_train[:-2]):
para.set_value(pre)
else:
exit()
else:
exit()
###############
# TRAIN MODEL #
###############
print '... training'
scale = False
epoch = 0
n_visualization = 900
pixel_max = pixel_max[:n_visualization]
pixel_min = pixel_min[:n_visualization]
output = np.ones((n_visualization, visualization_times+2, n_channels*dim_input[0]*dim_input[1]))
output[:,0,:] = test_set_x.get_value()[:n_visualization,:]
output[:,1,:] = test_set_x_pertub.get_value()[:n_visualization,:]
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(output[:,0,:].T,pixel_max,pixel_min), dim_input, colorImg=colorImg, scale=scale)
image.save(logdir+'data.png', 'PNG')
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(output[:,1,:].T,pixel_max,pixel_min), dim_input, colorImg=colorImg, scale=scale)
image.save(logdir+'data_pertub.png', 'PNG')
tmp = test_set_x_pertub.get_value()
while epoch < denoise_times:
epoch = epoch + 1
for i in xrange(n_test_batches):
d, m = imputation_model(i, tmp[i * batch_size: (i + 1) * batch_size])
tmp[i * batch_size: (i + 1) * batch_size] = np.asarray(d)
if epoch<=visualization_times:
output[:,epoch+1,:] = tmp[:n_visualization,:]
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(tmp[:n_visualization,:].T,pixel_max,pixel_min), dim_input, colorImg=colorImg, scale=scale)
image.save(logdir+'procedure-'+str(epoch)+'.png', 'PNG')
np.savez(logdir+'procedure-'+str(epoch), tmp=tmp)
'''
image = paramgraphics.mat_to_img((output.reshape(-1,32*32*3)).T, dim_input, colorImg=colorImg, tile_shape=(n_visualization,22), scale=scale)
image.save(logdir+'output.png', 'PNG')
np.savez(logdir+'output', output=output)
'''
'''
tmp_a[start:end] = 0
#print tmp_a.shape
#print tmp_a
tmp_b = np.tile(tmp_a, height*3)
print tmp_b.shape
print pertub_label.shape
pertub_label = (pertub_label.T*tmp_b).T
data_perturbed = pertub_label*data+(1-pertub_label)*data_perturbed
h1,b1= np.histogram(data, bins=10)
h2,b2= np.histogram(data_perturbed, bins=10)
print h1,b1
print h2,b2
if pertub_type == 4:
sio.savemat('data_imputation/'+dataset+'_type_'+str(pertub_type)+'_params_'+str(int(pertub_prob*100))+'_noise_rawdata.mat', {'z_test_original' : data, 'z_test' : data_perturbed, 'pertub_label' : pertub_label})
elif pertub_type == 3:
sio.savemat('data_imputation/'+dataset+'_type_'+str(pertub_type)+'_params_'+str(pertub_prob)+'_noise_rawdata.mat', {'z_test_original' : data, 'z_test' : data_perturbed, 'pertub_label' : pertub_label})
elif pertub_type == 5:
sio.savemat('data_imputation/'+dataset+'_type_'+str(pertub_type)+'_params_'+str(start)+'_'+str(end)+'_noise_rawdata.mat', {'z_test_original' : data, 'z_test' : data_perturbed, 'pertub_label' : pertub_label})
sio.savemat('data_imputation/'+dataset+'_params_'+str(int(pertub_prob))+'_max_min_pixel.mat', {'pixel_max':pixel_max, 'pixel_min':pixel_min})
print data_perturbed[:,:25].shape
scale = False
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(data_perturbed[:,:25],pixel_max,pixel_min), (32,32), colorImg=True, scale=scale)
image.save('data_imputation/'+dataset+'_'+'test_noise_type_'+str(pertub_type)+'_params_'+str(pertub_prob)+'.png', 'PNG')
print data[:,:25].shape
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(data[:,:25],pixel_max,pixel_min), (32,32), colorImg=True, scale=scale)
image.save('data_imputation/'+dataset+'_'+'test_original_type_'+str(pertub_type)+'_params_'+str(pertub_prob)+'.png', 'PNG')
def cmmva_6layer_dropout_mnist_60000(seed=0, start_layer=0, end_layer=1, dropout_flag=1, drop_inverses_flag=0, learning_rate=3e-5, predir=None, n_batch=144,
dataset='mnist.pkl.gz', batch_size=500, nkerns=[20, 50], n_hidden=[500, 50]):
"""
Implementation of convolutional MMVA
"""
#cp->cd->cpd->cd->c
nkerns=[32, 32, 64, 64, 64]
drops=[1, 0, 1, 0, 0, 1]
#skerns=[5, 3, 3, 3, 3]
#pools=[2, 1, 1, 2, 1]
#modes=['same']*5
n_hidden=[500, 50]
drop_inverses=[1,]
# 28->12->12->5->5/5*5*64->500->50->500->5*5*64/5->5->12->12->28
if dataset=='mnist.pkl.gz':
dim_input=(28, 28)
colorImg=False
D = 1.0
C = 1.0
if os.environ.has_key('C'):
C = np.cast['float32'](float((os.environ['C'])))
if os.environ.has_key('D'):
D = np.cast['float32'](float((os.environ['D'])))
color.printRed('D '+str(D)+' C '+str(C))
logdir = 'results/supervised/cmmva/mnist/cmmva_6layer_60000_'+str(nkerns)+str(n_hidden)+'_D_'+str(D)+'_C_'+str(C)+'_'+str(learning_rate)+'_'
if predir is not None:
logdir +='pre_'
if dropout_flag == 1:
logdir += ('dropout_'+str(drops)+'_')
if drop_inverses_flag==1:
logdir += ('inversedropout_'+str(drop_inverses)+'_')
logdir += str(int(time.time()))+'/'
if not os.path.exists(logdir): os.makedirs(logdir)
print 'logdir:', logdir, 'predir', predir
print 'cmmva_6layer_mnist_60000', nkerns, n_hidden, seed, drops, drop_inverses, dropout_flag, drop_inverses_flag
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'logdir:', logdir, 'predir', predir
print >>f, 'cmmva_6layer_mnist_60000', nkerns, n_hidden, seed, drops, drop_inverses, dropout_flag, drop_inverses_flag
datasets = datapy.load_data_gpu_60000(dataset, have_matrix=True)
train_set_x, train_set_y, train_y_matrix = datasets[0]
valid_set_x, valid_set_y, valid_y_matrix = datasets[1]
test_set_x, test_set_y, test_y_matrix = 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
y_matrix = T.imatrix('y_matrix')
random_z = T.matrix('random_z')
drop = T.iscalar('drop')
drop_inverse = T.iscalar('drop_inverse')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x.reshape((batch_size, 1, 28, 28))
recg_layer = []
cnn_output = []
#1
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
border_mode='valid',
activation=activation
))
if drops[0]==1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x, drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
#2
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[1]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#3
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[1], 12, 12),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='valid',
activation=activation
))
if drops[2]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#4
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[2], 5, 5),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[3]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#5
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[3], 5, 5),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[4]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
#1
recg_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in= 5 * 5 * nkerns[-1],
n_out=n_hidden[0],
activation=activation
))
if drops[-1]==1:
activations.append(recg_layer[-1].drop_output(input=mlp_input_x, drop=drop, rng=rng_share))
else:
activations.append(recg_layer[-1].output(input=mlp_input_x))
features = T.concatenate(activations[start_layer:end_layer], axis=1)
color.printRed('feature dimension: '+str(np.sum(n_hidden[start_layer:end_layer])))
classifier = Pegasos.Pegasos(
input= features,
rng=rng,
n_in=np.sum(n_hidden[start_layer:end_layer]),
n_out=10,
weight_decay=0,
loss=1,
std=1e-2
)
recg_layer.append(GaussianHidden.GaussianHidden(
rng=rng,
input=activations[-1],
n_in=n_hidden[0],
n_out = n_hidden[1],
activation=None
))
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[1],
n_out = n_hidden[0],
activation=activation
))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
#2
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[0],
n_out = 5*5*nkerns[-1],
activation=activation
))
if drop_inverses[0]==1:
z_output.append(gene_layer[-1].drop_output(input=z_output[-1], drop=drop_inverse, rng=rng_share))
random_z_output.append(gene_layer[-1].drop_output(input=random_z_output[-1], drop=drop_inverse, rng=rng_share))
else:
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output(input=random_z_output[-1]))
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 5, 5))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 5, 5))
#1
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-1], 5, 5),
filter_shape=(nkerns[-2], nkerns[-1], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-2], 5, 5),
filter_shape=(nkerns[-3], nkerns[-2], 3, 3),
poolsize=(2, 2),
border_mode='full',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-3], 12, 12),
filter_shape=(nkerns[-4], nkerns[-3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-4], 12, 12),
filter_shape=(nkerns[-5], nkerns[-4], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#5 stochastic layer
# for the last layer, the nonliearity should be sigmoid to achieve mean of Bernoulli
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-5], 12, 12),
filter_shape=(1, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='full',
activation=nonlinearity.sigmoid
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
gene_layer.append(NoParamsBernoulliVisiable.NoParamsBernoulliVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=z_output[-1], data=input_x)
# 4-D tensor of random generation
random_x_mean = random_z_output[-1]
random_x = gene_layer[-1].sample_x(rng_share, random_x_mean)
#L = (logpx + logpz - logqz).sum()
lowerbound = (
(logpx + recg_layer[-1].logpz - recg_layer[-1].logqz).sum()
)
hinge_loss = classifier.hinge_loss(10, y, y_matrix) * batch_size
#
# D is redundent, you could just set D = 1 and tune C and weight decay parameters
# beacuse AdaM is scale-invariant
#
cost = D * lowerbound - C * hinge_loss #- classifier.L2_reg
px = (logpx.sum())
pz = (recg_layer[-1].logpz.sum())
qz = (- recg_layer[-1].logqz.sum())
params=[]
for g in gene_layer:
params+=g.params
for r in recg_layer:
params+=r.params
params+=classifier.params
gparams = [T.grad(cost, param) for param in params]
weight_decay=1.0/n_train_batches
epsilon=1e-8
#get_optimizer = optimizer.get_adam_optimizer(learning_rate=learning_rate)
l_r = theano.shared(np.asarray(learning_rate, dtype=np.float32))
get_optimizer = optimizer.get_adam_optimizer_max(learning_rate=l_r,
decay1=0.1, decay2=0.001, weight_decay=weight_decay, epsilon=epsilon)
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'AdaM', learning_rate, weight_decay, epsilon
updates = get_optimizer(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), lowerbound, hinge_loss, cost],
#outputs=layer[-1].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],
y_matrix: test_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
validate_model = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, hinge_loss, cost],
#outputs=layer[-1].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],
y_matrix: valid_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
'''
Save parameters and activations
'''
parameters = theano.function(
inputs=[],
outputs=params,
)
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
test_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# 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`
debug_model = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, px, pz, qz, hinge_loss, 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],
y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag),
drop_inverse: np.cast['int32'](drop_inverses_flag)
}
)
random_generation = theano.function(
inputs=[random_z],
outputs=[random_x_mean.flatten(2), random_x.flatten(2)],
givens={
#drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
train_bound_without_dropout = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, hinge_loss, cost],
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size],
y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
train_model = theano.function(
inputs=[index],
outputs=[classifier.errors(y), lowerbound, hinge_loss, 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],
y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag),
drop_inverse: np.cast['int32'](drop_inverses_flag)
}
)
# end-snippet-5
##################
# Pretrain MODEL #
##################
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
pre_train = np.load(predir+'model.npz')
pre_train = pre_train['model']
# params include w and b, exclude it
for (para, pre) in zip(params[:-2], pre_train):
#print pre.shape
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print '------------------', tmp[1:5]
# valid_error test_error epochs
predy_test_stats = [1, 1, 0]
predy_valid_stats = [1, 1, 0]
best_validation_bound = -1000000.0
best_iter = 0
test_score = 0.
start_time = time.clock()
NaN_count = 0
epoch = 0
threshold = 0
validation_frequency = 1
generatition_frequency = 10
if predir is not None:
threshold = 0
color.printRed('threshold, '+str(threshold) +
' generatition_frequency, '+str(generatition_frequency)
+' validation_frequency, '+str(validation_frequency))
done_looping = False
decay_epochs=500
n_epochs=600
'''
print 'test initialization...'
pre_model = parameters()
for i in xrange(len(pre_model)):
pre_model[i] = np.asarray(pre_model[i])
print pre_model[i].shape, np.mean(pre_model[i]), np.var(pre_model[i])
print 'end test...'
'''
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
train_error = 0
train_lowerbound = 0
train_hinge_loss = 0
train_obj = 0
test_epoch = epoch - decay_epochs
if test_epoch > 0 and test_epoch % 10 == 0:
print l_r.get_value()
with open(logdir+'hook.txt', 'a') as f:
print >>f,l_r.get_value()
l_r.set_value(np.cast['float32'](l_r.get_value()/3.0))
tmp_start1 = time.clock()
for minibatch_index in xrange(n_train_batches):
#print n_train_batches
e, l, h, o = train_model(minibatch_index)
train_error += e
train_lowerbound += l
train_hinge_loss += h
train_obj += o
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if math.isnan(train_lowerbound):
NaN_count+=1
color.printRed("NaN detected. Reverting to saved best parameters")
print '---------------NaN_count:', NaN_count
with open(logdir+'hook.txt', 'a') as f:
print >>f, '---------------NaN_count:', NaN_count
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
tmp[0]*=batch_size
print '------------------NaN check:', tmp
with open(logdir+'hook.txt', 'a') as f:
print >>f, '------------------NaN check:', tmp
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
print model[i].shape, np.mean(model[i]), np.var(model[i])
print np.max(model[i]), np.min(model[i])
print np.all(np.isfinite(model[i])), np.any(np.isnan(model[i]))
with open(logdir+'hook.txt', 'a') as f:
print >>f, model[i].shape, np.mean(model[i]), np.var(model[i])
print >>f, np.max(model[i]), np.min(model[i])
print >>f, np.all(np.isfinite(model[i])), np.any(np.isnan(model[i]))
best_before = np.load(logdir+'model.npz')
best_before = best_before['model']
for (para, pre) in zip(params, best_before):
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
tmp[0]*=batch_size
print '------------------', tmp
continue
n_train=n_train_batches*batch_size
#print 'optimization_time', time.clock() - tmp_start1
print epoch, 'stochastic training error', train_error / float(batch_size), train_lowerbound / float(n_train), train_hinge_loss / float(n_train), train_obj / float(n_train)
with open(logdir+'hook.txt', 'a') as f:
print >>f, epoch, 'stochastic training error', train_error / float(batch_size), train_lowerbound / float(n_train), train_hinge_loss / float(n_train), train_obj / float(n_train)
if epoch % validation_frequency == 0:
tmp_start2 = time.clock()
# compute zero-one loss on validation set
#train_stats = [train_bound_without_dropout(i) for i
# in xrange(n_train_batches)]
#this_train_stats = np.mean(train_stats, axis=0)
#this_train_stats[1:] = this_train_stats[1:]/ float(batch_size)
test_stats = [test_model(i) for i in xrange(n_test_batches)]
this_test_stats = np.mean(test_stats, axis=0)
this_test_stats[1:] = this_test_stats[1:]/ float(batch_size)
print epoch, 'test error', this_test_stats
with open(logdir+'hook.txt', 'a') as f:
print >>f, epoch, 'test error', this_test_stats
if epoch%100==0:
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
#print model[i].shape, np.mean(model[i]), np.var(model[i])
np.savez(logdir+'model-'+str(epoch), model=model)
tmp_start4=time.clock()
if epoch % generatition_frequency == 0:
tail='-'+str(epoch)+'.png'
random_z = np.random.standard_normal((n_batch, n_hidden[-1])).astype(np.float32)
_x_mean, _x = random_generation(random_z)
#print _x.shape
#print _x_mean.shape
image = paramgraphics.mat_to_img(_x.T, dim_input, colorImg=colorImg)
image.save(logdir+'samples'+tail, 'PNG')
image = paramgraphics.mat_to_img(_x_mean.T, dim_input, colorImg=colorImg)
image.save(logdir+'mean_samples'+tail, 'PNG')
#print 'generation_time', time.clock() - tmp_start4
end_time = time.clock()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
if NaN_count > 0:
print '---------------NaN_count:', NaN_count
with open(logdir+'hook.txt', 'a') as f:
print >>f, '---------------NaN_count:', NaN_count
" TRAIN:\tCost=%.5f\tlogq(z|x)=%.5f\tlogp(z)=%.5f\tlogp(x|z)=%.5f\n" %(costs_train[-1], log_qz_given_x_train[-1], log_pz_train[-1], log_px_given_z_train[-1]) + \
" EVAL-L1:\tCost=%.5f\tlogq(z|x)=%.5f\tlogp(z)=%.5f\tlogp(x|z)=%.5f" %(LL_test1[-1], log_qz_given_x_test1[-1], log_pz_test1[-1], log_px_given_z_test1[-1])
print line
with open(logfile,'a') as f:
f.write(line + "\n")
# random generation for visualization
import util.paramgraphics as paramgraphics
import scipy.io as sio
tail='-'+str(epoch)+'.png'
_x_mean, _x = generate_model(num_generation)
_x_mean = _x_mean.reshape((num_generation,-1))
_x = _x.reshape((num_generation,-1))
sio.savemat(os.path.join(res_out,'array_images-'+str(epoch)+'.mat'), {'data':_x_mean})
image = paramgraphics.mat_to_img(_x.T, dim_input, colorImg=colorImg, scale=generation_scale)
image.save(os.path.join(res_out, 'samples'+tail), 'PNG')
image = paramgraphics.mat_to_img(_x_mean.T, dim_input, colorImg=colorImg, scale=generation_scale)
image.save(os.path.join(res_out, 'mean_samples'+tail), 'PNG')
'''
if dataset in ['norb_48', 'norb_96']:
image = paramgraphics.mat_to_img(_x_mean.T, dim_input, colorImg=colorImg, scale=True)
image.save(os.path.join(res_out, 'mean_samples_scale'+tail), 'PNG')
import nn_search
if epoch % 250 == 0:
nn = nn_search.nn_search(_x_mean, train_x)
image = paramgraphics.mat_to_img(nn.T, dim_input, colorImg=colorImg, scale=True)
image.save(os.path.join(res_out, 'mean_samples_nn'+tail), 'PNG')
'''
#save model every 100'th epochs
def cva_6layer_dropout_mnist_60000(seed=0, dropout_flag=1, drop_inverses_flag=0, learning_rate=3e-4, predir=None, n_batch=144,
dataset='mnist.pkl.gz', batch_size=500, nkerns=[20, 50], n_hidden=[500, 50]):
"""
Implementation of convolutional VA
"""
#cp->cd->cpd->cd->c
nkerns=[32, 32, 64, 64, 64]
drops=[1, 0, 1, 0, 0]
#skerns=[5, 3, 3, 3, 3]
#pools=[2, 1, 1, 2, 1]
#modes=['same']*5
n_hidden=[500, 50]
drop_inverses=[1,]
# 28->12->12->5->5/5*5*64->500->50->500->5*5*64/5->5->12->12->28
if dataset=='mnist.pkl.gz':
dim_input=(28, 28)
colorImg=False
logdir = 'results/supervised/cva/mnist/cva_6layer_mnist_60000'+str(nkerns)+str(n_hidden)+'_'+str(learning_rate)+'_'
if predir is not None:
logdir +='pre_'
if dropout_flag == 1:
logdir += ('dropout_'+str(drops)+'_')
if drop_inverses_flag==1:
logdir += ('inversedropout_'+str(drop_inverses)+'_')
logdir += str(int(time.time()))+'/'
if not os.path.exists(logdir): os.makedirs(logdir)
print 'logdir:', logdir, 'predir', predir
print 'cva_6layer_mnist_60000', nkerns, n_hidden, seed, drops, drop_inverses, dropout_flag, drop_inverses_flag
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'logdir:', logdir, 'predir', predir
print >>f, 'cva_6layer_mnist_60000', nkerns, n_hidden, seed, drops, drop_inverses, dropout_flag, drop_inverses_flag
datasets = datapy.load_data_gpu_60000(dataset, have_matrix=True)
train_set_x, train_set_y, train_y_matrix = datasets[0]
valid_set_x, valid_set_y, valid_y_matrix = datasets[1]
test_set_x, test_set_y, test_y_matrix = 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
random_z = T.matrix('random_z')
drop = T.iscalar('drop')
drop_inverse = T.iscalar('drop_inverse')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x.reshape((batch_size, 1, 28, 28))
recg_layer = []
cnn_output = []
#1
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
border_mode='valid',
activation=activation
))
if drops[0]==1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x, drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
#2
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[1]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#3
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[1], 12, 12),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='valid',
activation=activation
))
if drops[2]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#4
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[2], 5, 5),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[3]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#5
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[3], 5, 5),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[4]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
#1
recg_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in= 5 * 5 * nkerns[-1],
n_out=n_hidden[0],
activation=activation
))
if drops[-1]==1:
activations.append(recg_layer[-1].drop_output(input=mlp_input_x, drop=drop, rng=rng_share))
else:
activations.append(recg_layer[-1].output(input=mlp_input_x))
#stochastic layer
recg_layer.append(GaussianHidden.GaussianHidden(
rng=rng,
input=activations[-1],
n_in=n_hidden[0],
n_out = n_hidden[1],
activation=None
))
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[1],
n_out = n_hidden[0],
activation=activation
))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
#2
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[0],
n_out = 5*5*nkerns[-1],
activation=activation
))
if drop_inverses[0]==1:
z_output.append(gene_layer[-1].drop_output(input=z_output[-1], drop=drop_inverse, rng=rng_share))
random_z_output.append(gene_layer[-1].drop_output(input=random_z_output[-1], drop=drop_inverse, rng=rng_share))
else:
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output(input=random_z_output[-1]))
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 5, 5))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 5, 5))
#1
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-1], 5, 5),
filter_shape=(nkerns[-2], nkerns[-1], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-2], 5, 5),
filter_shape=(nkerns[-3], nkerns[-2], 3, 3),
poolsize=(2, 2),
border_mode='full',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-3], 12, 12),
filter_shape=(nkerns[-4], nkerns[-3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-4], 12, 12),
filter_shape=(nkerns[-5], nkerns[-4], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#5 stochastic layer
# for the last layer, the nonliearity should be sigmoid to achieve mean of Bernoulli
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-5], 12, 12),
filter_shape=(1, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='full',
activation=nonlinearity.sigmoid
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
gene_layer.append(NoParamsBernoulliVisiable.NoParamsBernoulliVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=z_output[-1], data=input_x)
# 4-D tensor of random generation
random_x_mean = random_z_output[-1]
random_x = gene_layer[-1].sample_x(rng_share, random_x_mean)
#L = (logpx + logpz - logqz).sum()
cost = (
(logpx + recg_layer[-1].logpz - recg_layer[-1].logqz).sum()
)
px = (logpx.sum())
pz = (recg_layer[-1].logpz.sum())
qz = (- recg_layer[-1].logqz.sum())
params=[]
for g in gene_layer:
params+=g.params
for r in recg_layer:
params+=r.params
gparams = [T.grad(cost, param) for param in params]
weight_decay=1.0/n_train_batches
l_r = theano.shared(np.asarray(learning_rate, dtype=np.float32))
#get_optimizer = optimizer.get_adam_optimizer(learning_rate=learning_rate)
get_optimizer = optimizer.get_adam_optimizer_max(learning_rate=l_r,
decay1=0.1, decay2=0.001, weight_decay=weight_decay, epsilon=1e-8)
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'AdaM', learning_rate, weight_decay
updates = get_optimizer(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=cost,
#outputs=layer[-1].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],
#y_matrix: test_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
validate_model = theano.function(
inputs=[index],
outputs=cost,
#outputs=layer[-1].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],
#y_matrix: valid_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
'''
Save parameters and activations
'''
parameters = theano.function(
inputs=[],
outputs=params,
)
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
test_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
#y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# 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`
debug_model = theano.function(
inputs=[index],
outputs=[cost, px, pz, qz],
#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],
#y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag),
drop_inverse: np.cast['int32'](drop_inverses_flag)
}
)
random_generation = theano.function(
inputs=[random_z],
outputs=[random_x_mean.flatten(2), random_x.flatten(2)],
givens={
#drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
train_bound_without_dropout = theano.function(
inputs=[index],
outputs=cost,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
#y: train_set_y[index * batch_size: (index + 1) * batch_size],
#y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
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],
#y_matrix: train_y_matrix[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](dropout_flag),
drop_inverse: np.cast['int32'](drop_inverses_flag)
}
)
##################
# Pretrain MODEL #
##################
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
pre_train = np.load(predir+'model.npz')
pre_train = pre_train['model']
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print '------------------', tmp
###############
# 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_bound = -1000000.0
best_iter = 0
test_score = 0.
start_time = time.clock()
NaN_count = 0
epoch = 0
threshold = 0
validation_frequency = 1
generatition_frequency = 10
if predir is not None:
threshold = 0
color.printRed('threshold, '+str(threshold) +
' generatition_frequency, '+str(generatition_frequency)
+' validation_frequency, '+str(validation_frequency))
done_looping = False
n_epochs = 600
decay_epochs = 500
'''
print 'test initialization...'
pre_model = parameters()
for i in xrange(len(pre_model)):
pre_model[i] = np.asarray(pre_model[i])
print pre_model[i].shape, np.mean(pre_model[i]), np.var(pre_model[i])
print 'end test...'
'''
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
minibatch_avg_cost = 0
tmp_start1 = time.clock()
test_epoch = epoch - decay_epochs
if test_epoch > 0 and test_epoch % 10 == 0:
print l_r.get_value()
with open(logdir+'hook.txt', 'a') as f:
print >>f,l_r.get_value()
l_r.set_value(np.cast['float32'](l_r.get_value()/3.0))
for minibatch_index in xrange(n_train_batches):
#print minibatch_index
'''
color.printRed('lalala')
xxx = dims(minibatch_index)
print xxx.shape
'''
#print n_train_batches
minibatch_avg_cost += train_model(minibatch_index)
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if math.isnan(minibatch_avg_cost):
NaN_count+=1
color.printRed("NaN detected. Reverting to saved best parameters")
print '---------------NaN_count:', NaN_count
with open(logdir+'hook.txt', 'a') as f:
print >>f, '---------------NaN_count:', NaN_count
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print '------------------NaN check:', tmp
with open(logdir+'hook.txt', 'a') as f:
print >>f, '------------------NaN check:', tmp
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
print model[i].shape, np.mean(model[i]), np.var(model[i])
print np.max(model[i]), np.min(model[i])
print np.all(np.isfinite(model[i])), np.any(np.isnan(model[i]))
with open(logdir+'hook.txt', 'a') as f:
print >>f, model[i].shape, np.mean(model[i]), np.var(model[i])
print >>f, np.max(model[i]), np.min(model[i])
print >>f, np.all(np.isfinite(model[i])), np.any(np.isnan(model[i]))
best_before = np.load(logdir+'model.npz')
best_before = best_before['model']
for (para, pre) in zip(params, best_before):
para.set_value(pre)
tmp = [debug_model(i) for i in xrange(n_train_batches)]
tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print '------------------', tmp
return
#print 'optimization_time', time.clock() - tmp_start1
print epoch, 'stochastic training error', minibatch_avg_cost / float(n_train_batches*batch_size)
with open(logdir+'hook.txt', 'a') as f:
print >>f, epoch, 'stochastic training error', minibatch_avg_cost / float(n_train_batches*batch_size)
if epoch % validation_frequency == 0:
tmp_start2 = time.clock()
test_losses = [test_model(i) for i
in xrange(n_test_batches)]
this_test_bound = np.mean(test_losses)/float(batch_size)
#tmp = [debug_model(i) for i
# in xrange(n_train_batches)]
#tmp = (np.asarray(tmp)).mean(axis=0) / float(batch_size)
print epoch, 'test bound', this_test_bound
#print tmp
with open(logdir+'hook.txt', 'a') as f:
print >>f, epoch, 'test bound', this_test_bound
if epoch%100==0:
model = parameters()
for i in xrange(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
np.savez(logdir+'model-'+str(epoch), model=model)
for i in xrange(n_train_batches):
if i == 0:
train_features = np.asarray(train_activations(i))
else:
train_features = np.vstack((train_features, np.asarray(train_activations(i))))
for i in xrange(n_valid_batches):
if i == 0:
valid_features = np.asarray(valid_activations(i))
else:
valid_features = np.vstack((valid_features, np.asarray(valid_activations(i))))
for i in xrange(n_test_batches):
if i == 0:
test_features = np.asarray(test_activations(i))
else:
test_features = np.vstack((test_features, np.asarray(test_activations(i))))
np.save(logdir+'train_features', train_features)
np.save(logdir+'valid_features', valid_features)
np.save(logdir+'test_features', test_features)
tmp_start4=time.clock()
if epoch % generatition_frequency == 0:
tail='-'+str(epoch)+'.png'
random_z = np.random.standard_normal((n_batch, n_hidden[-1])).astype(np.float32)
_x_mean, _x = random_generation(random_z)
#print _x.shape
#print _x_mean.shape
image = paramgraphics.mat_to_img(_x.T, dim_input, colorImg=colorImg)
image.save(logdir+'samples'+tail, 'PNG')
image = paramgraphics.mat_to_img(_x_mean.T, dim_input, colorImg=colorImg)
image.save(logdir+'mean_samples'+tail, 'PNG')
#print 'generation_time', time.clock() - tmp_start4
end_time = time.clock()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
if NaN_count > 0:
print '---------------NaN_count:', NaN_count
with open(logdir+'hook.txt', 'a') as f:
print >>f, '---------------NaN_count:', NaN_count
def c_6layer_mnist_imputation(seed=0,
ctype='cva',
pertub_type=3,
pertub_prob=6,
pertub_prob1=14,
visualization_times=20,
denoise_times=200,
predir=None,
n_batch=144,
dataset='mnist.pkl.gz',
batch_size=500):
"""
Missing data imputation
"""
#cp->cd->cpd->cd->c
nkerns = [32, 32, 64, 64, 64]
drops = [0, 0, 0, 0, 0, 1]
#skerns=[5, 3, 3, 3, 3]
#pools=[2, 1, 1, 2, 1]
#modes=['same']*5
n_hidden = [500, 50]
drop_inverses = [
1,
]
# 28->12->12->5->5/5*5*64->500->50->500->5*5*64/5->5->12->12->28
if dataset == 'mnist.pkl.gz':
dim_input = (28, 28)
colorImg = False
logdir = 'results/imputation/' + ctype + '/mnist/' + ctype + '_6layer_mnist_' + str(
pertub_type) + '_' + str(pertub_prob) + '_' + str(
pertub_prob1) + '_' + str(denoise_times) + '_'
logdir += str(int(time.time())) + '/'
if not os.path.exists(logdir): os.makedirs(logdir)
print predir
with open(logdir + 'hook.txt', 'a') as f:
print >> f, predir
train_set_x, test_set_x, test_set_x_pertub, pertub_label, pertub_number = datapy.load_pertub_data(
dirs='data_imputation/',
pertub_type=pertub_type,
pertub_prob=pertub_prob,
pertub_prob1=pertub_prob1)
datasets = datapy.load_data_gpu(dataset, have_matrix=True)
_, _, _ = datasets[0]
valid_set_x, _, _ = datasets[1]
_, _, _ = datasets[2]
# compute number of minibatches for training, validation and testing
n_test_batches = test_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_train_batches = train_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')
x_pertub = T.matrix(
'x_pertub') # the data is presented as rasterized images
p_label = T.matrix('p_label')
random_z = T.matrix('random_z')
drop = T.iscalar('drop')
drop_inverse = T.iscalar('drop_inverse')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x_pertub.reshape((batch_size, 1, 28, 28))
recg_layer = []
cnn_output = []
#1
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
border_mode='valid',
activation=activation))
if drops[0] == 1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x,
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
#2
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[1] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#3
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[1], 12, 12),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='valid',
activation=activation))
if drops[2] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#4
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[2], 5, 5),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[3] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#5
recg_layer.append(
ConvMaxPool.ConvMaxPool(rng,
image_shape=(batch_size, nkerns[3], 5, 5),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[4] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
#1
recg_layer.append(
FullyConnected.FullyConnected(rng=rng,
n_in=5 * 5 * nkerns[-1],
n_out=n_hidden[0],
activation=activation))
if drops[-1] == 1:
activations.append(recg_layer[-1].drop_output(input=mlp_input_x,
drop=drop,
rng=rng_share))
else:
activations.append(recg_layer[-1].output(input=mlp_input_x))
#stochastic layer
recg_layer.append(
GaussianHidden.GaussianHidden(rng=rng,
input=activations[-1],
n_in=n_hidden[0],
n_out=n_hidden[1],
activation=None))
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(
FullyConnected.FullyConnected(rng=rng,
n_in=n_hidden[1],
n_out=n_hidden[0],
activation=activation))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
#2
gene_layer.append(
FullyConnected.FullyConnected(rng=rng,
n_in=n_hidden[0],
n_out=5 * 5 * nkerns[-1],
activation=activation))
if drop_inverses[0] == 1:
z_output.append(gene_layer[-1].drop_output(input=z_output[-1],
drop=drop_inverse,
rng=rng_share))
random_z_output.append(gene_layer[-1].drop_output(
input=random_z_output[-1], drop=drop_inverse, rng=rng_share))
else:
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(
gene_layer[-1].output(input=random_z_output[-1]))
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 5, 5))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 5, 5))
#1
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-1], 5, 5),
filter_shape=(nkerns[-2], nkerns[-1], 3,
3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(
input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-2], 5, 5),
filter_shape=(nkerns[-3], nkerns[-2], 3,
3),
poolsize=(2, 2),
border_mode='full',
activation=activation))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-3], 12,
12),
filter_shape=(nkerns[-4], nkerns[-3], 3,
3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-4], 12,
12),
filter_shape=(nkerns[-5], nkerns[-4], 3,
3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#5 stochastic layer
# for the last layer, the nonliearity should be sigmoid to achieve mean of Bernoulli
gene_layer.append(
UnpoolConvNon.UnpoolConvNon(rng,
image_shape=(batch_size, nkerns[-5], 12,
12),
filter_shape=(1, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='full',
activation=nonlinearity.sigmoid))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
gene_layer.append(
NoParamsBernoulliVisiable.NoParamsBernoulliVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=z_output[-1], data=input_x)
# 4-D tensor of random generation
random_x_mean = random_z_output[-1]
random_x = gene_layer[-1].sample_x(rng_share, random_x_mean)
x_denoised = z_output[-1].flatten(2)
x_denoised = p_label * x + (1 - p_label) * x_denoised
mse = ((x - x_denoised)**2).sum() / pertub_number
params = []
for g in gene_layer:
params += g.params
for r in recg_layer:
params += r.params
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: train_set_x[index * batch_size:(index + 1) * batch_size],
drop: np.cast['int32'](0)
})
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: valid_set_x[index * batch_size:(index + 1) * batch_size],
drop: np.cast['int32'](0)
})
test_activations = theano.function(inputs=[x_pertub],
outputs=T.concatenate(activations,
axis=1),
givens={drop: np.cast['int32'](0)})
imputation_model = theano.function(
inputs=[index, x_pertub],
outputs=[x_denoised, mse],
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
p_label: pertub_label[index * batch_size:(index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
})
##################
# Pretrain MODEL #
##################
model_epoch = 600
if os.environ.has_key('model_epoch'):
model_epoch = int(os.environ['model_epoch'])
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
if model_epoch == -1:
pre_train = np.load(predir + 'best-model.npz')
else:
pre_train = np.load(predir + 'model-' + str(model_epoch) + '.npz')
pre_train = pre_train['model']
if ctype == 'cva':
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
elif ctype == 'cmmva':
for (para, pre) in zip(params, pre_train[:-2]):
para.set_value(pre)
else:
exit()
else:
exit()
###############
# TRAIN MODEL #
###############
print '... training'
epoch = 0
n_visualization = 100
output = np.ones((n_visualization, visualization_times + 2, 784))
output[:, 0, :] = test_set_x.get_value()[:n_visualization, :]
output[:, 1, :] = test_set_x_pertub.get_value()[:n_visualization, :]
image = paramgraphics.mat_to_img(output[:, 0, :].T,
dim_input,
colorImg=colorImg)
image.save(logdir + 'data.png', 'PNG')
image = paramgraphics.mat_to_img(output[:, 1, :].T,
dim_input,
colorImg=colorImg)
image.save(logdir + 'data_pertub.png', 'PNG')
tmp = test_set_x_pertub.get_value()
while epoch < denoise_times:
epoch = epoch + 1
this_mse = 0
for i in xrange(n_test_batches):
d, m = imputation_model(i,
tmp[i * batch_size:(i + 1) * batch_size])
tmp[i * batch_size:(i + 1) * batch_size] = np.asarray(d)
this_mse += m
if epoch <= visualization_times:
output[:, epoch + 1, :] = tmp[:n_visualization, :]
print epoch, this_mse
with open(logdir + 'hook.txt', 'a') as f:
print >> f, epoch, this_mse
image = paramgraphics.mat_to_img(tmp[:n_visualization, :].T,
dim_input,
colorImg=colorImg)
image.save(logdir + 'procedure-' + str(epoch) + '.png', 'PNG')
np.savez(logdir + 'procedure-' + str(epoch), tmp=tmp)
image = paramgraphics.mat_to_img((output.reshape(-1, 784)).T,
dim_input,
colorImg=colorImg,
tile_shape=(n_visualization, 22))
image.save(logdir + 'output.png', 'PNG')
np.savez(logdir + 'output', output=output)
# save original train features and denoise test features
for i in xrange(n_train_batches):
if i == 0:
train_features = np.asarray(train_activations(i))
else:
train_features = np.vstack(
(train_features, np.asarray(train_activations(i))))
for i in xrange(n_valid_batches):
if i == 0:
valid_features = np.asarray(valid_activations(i))
else:
valid_features = np.vstack(
(valid_features, np.asarray(valid_activations(i))))
for i in xrange(n_test_batches):
if i == 0:
test_features = np.asarray(
test_activations(tmp[i * batch_size:(i + 1) * batch_size]))
else:
test_features = np.vstack(
(test_features,
np.asarray(
test_activations(tmp[i * batch_size:(i + 1) *
batch_size]))))
np.save(logdir + 'train_features', train_features)
np.save(logdir + 'valid_features', valid_features)
np.save(logdir + 'test_features', test_features)
def cmmd(dataset='mnist.pkl.gz',
batch_size=100,
layer_num=3,
hidden_dim=5,
seed=0,
layer_size=[64, 256, 256, 512]):
validation_frequency = 1
test_frequency = 1
pre_train = 1
dim_input = (28, 28)
colorImg = False
print "Loading data ......."
#datasets = datapy.load_data_gpu_60000_with_noise(dataset, have_matrix = True)
datasets = datapy.load_data_gpu_60000(dataset, have_matrix=True)
train_set_x, train_set_y, train_y_matrix = datasets[0]
valid_set_x, valid_set_y, valid_y_matrix = datasets[1]
test_set_x, test_set_y, test_y_matrix = datasets[2]
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
n_train_batches = train_set_x.get_value().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
aImage = paramgraphics.mat_to_img(train_set_x.get_value()[0:169].T,
dim_input,
colorImg=colorImg)
aImage.save('mnist_sample', 'PNG')
################################
## build model ##
################################
print "Building model ......."
index = T.lscalar()
x = T.matrix('x') ##### batch_size * 28^2
y = T.vector('y')
y_matrix = T.matrix('y_matrix')
random_z = T.matrix('random_z') ### batch_size * hidden_dim
Inv_K_d = T.matrix('Inv_K_d')
layers = []
layer_output = []
activation = nonlinearity.relu
#activation = Tnn.sigmoid
#### first layer
layers.append(
FullyConnected.FullyConnected(
rng=rng,
n_in=10 + hidden_dim,
#n_in = 10,
n_out=layer_size[0],
activation=activation))
layer_output.append(layers[-1].output_mix(input=[y_matrix, random_z]))
#layer_output.append(layers[-1].output_mix2(input=[y_matrix,random_z]))
#layer_output.append(layers[-1].output(input=x))
#layer_output.append(layers[-1].output(input=random_z))
#### middle layer
for i in range(layer_num):
layers.append(
FullyConnected.FullyConnected(rng=rng,
n_in=layer_size[i],
n_out=layer_size[i + 1],
activation=activation))
layer_output.append(layers[-1].output(input=layer_output[-1]))
#### last layer
activation = Tnn.sigmoid
#activation = nonlinearity.relu
layers.append(
FullyConnected.FullyConnected(rng=rng,
n_in=layer_size[-1],
n_out=28 * 28,
activation=activation))
x_gen = layers[-1].output(input=layer_output[-1])
lambda1_ = 100
lambda_ = theano.shared(np.asarray(lambda1_, dtype=np.float32))
K_d = kernel_gram_for_y(y_matrix, y_matrix, batch_size, 10)
K_s = K_d
K_sd = K_d
Invv_1 = T.sum(y_matrix, axis=0) / batch_size
Invv = NL.alloc_diag(1 / Invv_1)
Inv_K_d = Invv
#Inv_K_d = NL.matrix_inverse(K_d +lambda_ * T.identity_like(K_d))
Inv_K_s = Inv_K_d
L_d = kernel_gram_for_x(x, x, batch_size, 28 * 28)
L_s = kernel_gram_for_x(x_gen, x_gen, batch_size, 28 * 28)
L_ds = kernel_gram_for_x(x, x_gen, batch_size, 28 * 28)
'''
cost = -(NL.trace(T.dot(T.dot(T.dot(K_d, Inv_K_d), L_d), Inv_K_d)) +\
NL.trace(T.dot(T.dot(T.dot(K_s, Inv_K_s), L_s),Inv_K_s))- \
2 * NL.trace(T.dot(T.dot(T.dot(K_sd, Inv_K_d) ,L_ds ), Inv_K_s)))
'''
'''
cost = -(NL.trace(T.dot(L_d, T.ones_like(L_d) )) +\
NL.trace(T.dot(L_s,T.ones_like(L_s)))- \
2 * NL.trace(T.dot(L_ds,T.ones_like(L_ds) )))
cost2 = 2 * T.sum(L_ds) - T.sum(L_s) + NL.trace(T.dot(L_s, T.ones_like(L_s)))\
- 2 * NL.trace( T.dot(L_ds , T.ones_like(L_ds)))
cost2 = T.dot(T.dot(Inv_K_d, K_d),Inv_K_d)
'''
cost2 = K_d
#cost2 = T.dot(T.dot(Inv_K_d,K_d),Inv_K_d)
#cost = - T.sum(L_d) +2 * T.sum(L_ds) - T.sum(L_s)
cost2 = K_d
cost2 = T.dot(T.dot(T.dot(y_matrix, Inv_K_d), Inv_K_d), y_matrix.T)
cost = -(NL.trace(T.dot(T.dot(T.dot(T.dot(L_d, y_matrix),Inv_K_d), Inv_K_d),y_matrix.T)) +\
NL.trace(T.dot(T.dot(T.dot(T.dot(L_s, y_matrix),Inv_K_s), Inv_K_s),y_matrix.T))- \
2 * NL.trace(T.dot(T.dot(T.dot(T.dot(L_ds, y_matrix),Inv_K_d), Inv_K_s),y_matrix.T)))
'''
cost = - T.sum(L_d) +2 * T.sum(L_ds) - T.sum(L_s)
cost = - NL.trace(K_s * Inv_K_s * L_s * Inv_K_s)+ \
2 * NL.trace(K_sd * Inv_K_d * L_ds * Inv_K_s)
'''
################################
## updates ##
################################
params = []
for aLayer in layers:
params += aLayer.params
gparams = [T.grad(cost, param) for param in params]
learning_rate = 3e-4
weight_decay = 1.0 / n_train_batches
epsilon = 1e-8
l_r = theano.shared(np.asarray(learning_rate, dtype=np.float32))
get_optimizer = optimizer.get_adam_optimizer_max(learning_rate=l_r,
decay1=0.1,
decay2=0.001,
weight_decay=weight_decay,
epsilon=epsilon)
updates = get_optimizer(params, gparams)
################################
## pretrain model ##
################################
parameters = theano.function(
inputs=[],
outputs=params,
)
gen_fig = theano.function(
inputs=[y_matrix, random_z],
outputs=x_gen,
on_unused_input='warn',
)
if pre_train == 1:
print "pre-training model....."
pre_train = np.load('./result/MMD-100-5-64-256-256-512.npz')['model']
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
s = 8
for jj in range(10):
a = np.zeros((s, 10), dtype=np.float32)
for ii in range(s):
kk = random.randint(0, 9)
a[ii, kk] = 1
x_gen = gen_fig(a, gen_random_z(s, hidden_dim))
ttt = train_set_x.get_value()
for ll in range(s):
minn = 1000000
ss = 0
for kk in range(ttt.shape[0]):
tt = np.linalg.norm(x_gen[ll] - ttt[kk])
if tt < minn:
minn = tt
ss = kk
#np.concatenate(x_gen,ttt[ss])
x_gen = np.vstack((x_gen, ttt[ss]))
aImage = paramgraphics.mat_to_img(x_gen.T,
dim_input,
colorImg=colorImg)
aImage.save('samples_' + str(jj) + '_similar', 'PNG')
################################
## prepare data ##
################################
#### compute matrix inverse
#print "Preparing data ...."
#Invv = NL.matrix_inverse(K_d +lambda_ * T.identity_like(K_d))
'''
Invv_1 = T.sum(y_matrix,axis=0)/batch_size
Invv = NL.alloc_diag(1/Invv_1)
Inv_K_d = Invv
prepare_data = theano.function(
inputs = [index],
outputs = [Invv,K_d],
givens = {
#x:train_set_x[index * batch_size:(index + 1) * batch_size],
y_matrix:train_y_matrix[index * batch_size:(index + 1) * batch_size],
}
)
Inv_K_d_l, K_d_l = prepare_data(0)
print Inv_K_d_l
for minibatch_index in range(1, n_train_batches):
if minibatch_index % 10 == 0:
print 'minibatch_index:', minibatch_index
Inv_pre_mini, K_d_pre_mini = prepare_data(minibatch_index)
Inv_K_d_l = np.vstack((Inv_K_d_l,Inv_pre_mini))
K_d_l = np.vstack((K_d_l,K_d_pre_mini))
Inv_K_d_g = theano.shared(Inv_K_d_l,borrow=True)
K_d_g = theano.shared(K_d_l, borrow=True)
'''
################################
## train model ##
################################
train_model = theano.function(
inputs=[index, random_z],
outputs=[cost, x_gen, cost2],
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],
y_matrix:
train_y_matrix[index * batch_size:(index + 1) * batch_size],
#K_d:K_d_g[index * batch_size:(index + 1) * batch_size],
#Inv_K_d:Inv_K_d_g[index * batch_size:(index + 1) * batch_size],
},
on_unused_input='warn')
n_epochs = 500
cur_epoch = 0
print "Training model ......"
while (cur_epoch < n_epochs):
cur_epoch = cur_epoch + 1
cor = 0
for minibatch_index in xrange(n_train_batches):
print minibatch_index,
print " : ",
cost, x_gen, cost2 = train_model(
minibatch_index, gen_random_z(batch_size, hidden_dim))
print 'cost: ', cost
print 'cost2: ', cost2
if minibatch_index % 30 == 0:
aImage = paramgraphics.mat_to_img(x_gen[0:1].T,
dim_input,
colorImg=colorImg)
aImage.save(
'samples_epoch_' + str(cur_epoch) + '_mini_' +
str(minibatch_index), 'PNG')
if cur_epoch % 1 == 0:
model = parameters()
for i in range(len(model)):
model[i] = np.asarray(model[i]).astype(np.float32)
np.savez('model-' + str(cur_epoch), model=model)
def c_6layer_mnist_imputation(seed=0, ctype='cva',
pertub_type=3, pertub_prob=6, pertub_prob1=14, visualization_times=20,
denoise_times=200, predir=None, n_batch=144,
dataset='mnist.pkl.gz', batch_size=500):
"""
Missing data imputation
"""
#cp->cd->cpd->cd->c
nkerns=[32, 32, 64, 64, 64]
drops=[0, 0, 0, 0, 0, 1]
#skerns=[5, 3, 3, 3, 3]
#pools=[2, 1, 1, 2, 1]
#modes=['same']*5
n_hidden=[500, 50]
drop_inverses=[1,]
# 28->12->12->5->5/5*5*64->500->50->500->5*5*64/5->5->12->12->28
if dataset=='mnist.pkl.gz':
dim_input=(28, 28)
colorImg=False
logdir = 'results/imputation/'+ctype+'/mnist/'+ctype+'_6layer_mnist_'+str(pertub_type)+'_'+str(pertub_prob)+'_'+str(pertub_prob1)+'_'+str(denoise_times)+'_'
logdir += str(int(time.time()))+'/'
if not os.path.exists(logdir): os.makedirs(logdir)
print predir
with open(logdir+'hook.txt', 'a') as f:
print >>f, predir
train_set_x, test_set_x, test_set_x_pertub, pertub_label, pertub_number = datapy.load_pertub_data(dirs='data_imputation/', pertub_type=pertub_type, pertub_prob=pertub_prob,pertub_prob1=pertub_prob1)
datasets = datapy.load_data_gpu(dataset, have_matrix=True)
_, _, _ = datasets[0]
valid_set_x, _, _ = datasets[1]
_, _, _ = datasets[2]
# compute number of minibatches for training, validation and testing
n_test_batches = test_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_train_batches = train_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')
x_pertub = T.matrix('x_pertub') # the data is presented as rasterized images
p_label = T.matrix('p_label')
random_z = T.matrix('random_z')
drop = T.iscalar('drop')
drop_inverse = T.iscalar('drop_inverse')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x_pertub.reshape((batch_size, 1, 28, 28))
recg_layer = []
cnn_output = []
#1
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
border_mode='valid',
activation=activation
))
if drops[0]==1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x, drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
#2
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[1]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#3
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[1], 12, 12),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='valid',
activation=activation
))
if drops[2]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#4
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[2], 5, 5),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[3]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
#5
recg_layer.append(ConvMaxPool.ConvMaxPool(
rng,
image_shape=(batch_size, nkerns[3], 5, 5),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
if drops[4]==1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1], drop=drop, rng=rng_share))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
#1
recg_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in= 5 * 5 * nkerns[-1],
n_out=n_hidden[0],
activation=activation
))
if drops[-1]==1:
activations.append(recg_layer[-1].drop_output(input=mlp_input_x, drop=drop, rng=rng_share))
else:
activations.append(recg_layer[-1].output(input=mlp_input_x))
#stochastic layer
recg_layer.append(GaussianHidden.GaussianHidden(
rng=rng,
input=activations[-1],
n_in=n_hidden[0],
n_out = n_hidden[1],
activation=None
))
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[1],
n_out = n_hidden[0],
activation=activation
))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
#2
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[0],
n_out = 5*5*nkerns[-1],
activation=activation
))
if drop_inverses[0]==1:
z_output.append(gene_layer[-1].drop_output(input=z_output[-1], drop=drop_inverse, rng=rng_share))
random_z_output.append(gene_layer[-1].drop_output(input=random_z_output[-1], drop=drop_inverse, rng=rng_share))
else:
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output(input=random_z_output[-1]))
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 5, 5))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 5, 5))
#1
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-1], 5, 5),
filter_shape=(nkerns[-2], nkerns[-1], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-2], 5, 5),
filter_shape=(nkerns[-3], nkerns[-2], 3, 3),
poolsize=(2, 2),
border_mode='full',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-3], 12, 12),
filter_shape=(nkerns[-4], nkerns[-3], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-4], 12, 12),
filter_shape=(nkerns[-5], nkerns[-4], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
#5 stochastic layer
# for the last layer, the nonliearity should be sigmoid to achieve mean of Bernoulli
gene_layer.append(UnpoolConvNon.UnpoolConvNon(
rng,
image_shape=(batch_size, nkerns[-5], 12, 12),
filter_shape=(1, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='full',
activation=nonlinearity.sigmoid
))
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(input=random_z_output[-1], n_batch=n_batch))
gene_layer.append(NoParamsBernoulliVisiable.NoParamsBernoulliVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=z_output[-1], data=input_x)
# 4-D tensor of random generation
random_x_mean = random_z_output[-1]
random_x = gene_layer[-1].sample_x(rng_share, random_x_mean)
x_denoised = z_output[-1].flatten(2)
x_denoised = p_label*x+(1-p_label)*x_denoised
mse = ((x - x_denoised)**2).sum() / pertub_number
params=[]
for g in gene_layer:
params+=g.params
for r in recg_layer:
params+=r.params
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: train_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: valid_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
test_activations = theano.function(
inputs=[x_pertub],
outputs=T.concatenate(activations, axis=1),
givens={
drop: np.cast['int32'](0)
}
)
imputation_model = theano.function(
inputs=[index, x_pertub],
outputs=[x_denoised, mse],
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
p_label:pertub_label[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0),
drop_inverse: np.cast['int32'](0)
}
)
##################
# Pretrain MODEL #
##################
model_epoch = 600
if os.environ.has_key('model_epoch'):
model_epoch = int(os.environ['model_epoch'])
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
if model_epoch == -1:
pre_train = np.load(predir+'best-model.npz')
else:
pre_train = np.load(predir+'model-'+str(model_epoch)+'.npz')
pre_train = pre_train['model']
if ctype == 'cva':
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
elif ctype == 'cmmva':
for (para, pre) in zip(params, pre_train[:-2]):
para.set_value(pre)
else:
exit()
else:
exit()
###############
# TRAIN MODEL #
###############
print '... training'
epoch = 0
n_visualization = 100
output = np.ones((n_visualization, visualization_times+2, 784))
output[:,0,:] = test_set_x.get_value()[:n_visualization,:]
output[:,1,:] = test_set_x_pertub.get_value()[:n_visualization,:]
image = paramgraphics.mat_to_img(output[:,0,:].T, dim_input, colorImg=colorImg)
image.save(logdir+'data.png', 'PNG')
image = paramgraphics.mat_to_img(output[:,1,:].T, dim_input, colorImg=colorImg)
image.save(logdir+'data_pertub.png', 'PNG')
tmp = test_set_x_pertub.get_value()
while epoch < denoise_times:
epoch = epoch + 1
this_mse=0
for i in xrange(n_test_batches):
d, m = imputation_model(i, tmp[i * batch_size: (i + 1) * batch_size])
tmp[i * batch_size: (i + 1) * batch_size] = np.asarray(d)
this_mse+=m
if epoch<=visualization_times:
output[:,epoch+1,:] = tmp[:n_visualization,:]
print epoch, this_mse
with open(logdir+'hook.txt', 'a') as f:
print >>f, epoch, this_mse
image = paramgraphics.mat_to_img(tmp[:n_visualization,:].T, dim_input, colorImg=colorImg)
image.save(logdir+'procedure-'+str(epoch)+'.png', 'PNG')
np.savez(logdir+'procedure-'+str(epoch), tmp=tmp)
image = paramgraphics.mat_to_img((output.reshape(-1,784)).T, dim_input, colorImg=colorImg, tile_shape=(n_visualization,22))
image.save(logdir+'output.png', 'PNG')
np.savez(logdir+'output', output=output)
# save original train features and denoise test features
for i in xrange(n_train_batches):
if i == 0:
train_features = np.asarray(train_activations(i))
else:
train_features = np.vstack((train_features, np.asarray(train_activations(i))))
for i in xrange(n_valid_batches):
if i == 0:
valid_features = np.asarray(valid_activations(i))
else:
valid_features = np.vstack((valid_features, np.asarray(valid_activations(i))))
for i in xrange(n_test_batches):
if i == 0:
test_features = np.asarray(test_activations(tmp[i * batch_size: (i + 1) * batch_size]))
else:
test_features = np.vstack((test_features, np.asarray(test_activations(tmp[i * batch_size: (i + 1) * batch_size]))))
np.save(logdir+'train_features', train_features)
np.save(logdir+'valid_features', valid_features)
np.save(logdir+'test_features', test_features)
print pertub_label.shape
pertub_label = (pertub_label.T * tmp_b).T
data_perturbed = pertub_label * data + (1 - pertub_label) * data_perturbed
if pertub_type == 4:
sio.savemat(
'data_imputation/type_' + str(pertub_type) + '_params_' +
str(int(pertub_prob * 100)) + '_noise_rawdata.mat', {
'z_train': x_train.T,
'z_test_original': data,
'z_test': data_perturbed,
'pertub_label': pertub_label
})
#print data_perturbed[:,:25].shape
image = paramgraphics.mat_to_img(data_perturbed[:, :25], (28, 28),
colorImg=False,
scale=True)
image.save('data_imputation/test_noise_4_' + str(pertub_prob) + '.png',
'PNG')
elif pertub_type == 3:
sio.savemat(
'data_imputation/type_' + str(pertub_type) + '_params_' +
str(pertub_prob) + '_noise_rawdata.mat', {
'z_train': x_train.T,
'z_test_original': data,
'z_test': data_perturbed,
'pertub_label': pertub_label
})
#print data_perturbed[:,:25].shape
image = paramgraphics.mat_to_img(data_perturbed[:, :25], (28, 28),
colorImg=False,
def c_6layer_svhn_imputation(seed=0,
ctype='cva',
pertub_type=5,
pertub_prob=0,
pertub_prob1=16,
visualization_times=20,
denoise_times=200,
predir=None,
n_batch=900,
batch_size=500):
"""
Missing data imputation
"""
'''
svhn
'''
n_channels = 3
colorImg = True
dim_w = 32
dim_h = 32
dim_input = (dim_h, dim_w)
n_classes = 10
first_drop = 0.6
if os.environ.has_key('first_drop'):
first_drop = float(os.environ['first_drop'])
last_drop = 1
if os.environ.has_key('last_drop'):
last_drop = float(os.environ['last_drop'])
nkerns_1 = 96
if os.environ.has_key('nkerns_1'):
nkerns_1 = int(os.environ['nkerns_1'])
nkerns_2 = 96
if os.environ.has_key('nkerns_2'):
nkerns_2 = int(os.environ['nkerns_2'])
opt_med = 'mom'
if os.environ.has_key('opt_med'):
opt_med = os.environ['opt_med']
train_logvar = True
if os.environ.has_key('train_logvar'):
train_logvar = bool(int(os.environ['train_logvar']))
dataset = 'svhnlcn'
if os.environ.has_key('dataset'):
dataset = os.environ['dataset']
n_z = 256
if os.environ.has_key('n_z'):
n_z = int(os.environ['n_z'])
#cp->cd->cpd->cd->c
nkerns = [nkerns_1, nkerns_1, nkerns_1, nkerns_2, nkerns_2]
drops = [0, 1, 1, 1, 0, 1]
drop_p = [1, first_drop, first_drop, first_drop, 1, last_drop]
n_hidden = [n_z]
logdir = 'results/imputation/' + ctype + '/svhn/' + ctype + '_6layer_' + dataset + '_'
logdir += str(int(time.time())) + '/'
if not os.path.exists(logdir): os.makedirs(logdir)
print predir
with open(logdir + 'hook.txt', 'a') as f:
print >> f, predir
color.printRed('dataset ' + dataset)
test_set_x, test_set_x_pertub, pertub_label, pertub_number = datapy.load_pertub_data_svhn(
dirs='data_imputation/',
dataset=dataset,
pertub_type=pertub_type,
pertub_prob=pertub_prob,
pertub_prob1=pertub_prob1)
pixel_max, pixel_min = datapy.load_max_min(dirs='data_imputation/',
dataset=dataset,
pertub_prob=pertub_prob)
# 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
random_z = T.matrix('random_z')
x_pertub = T.matrix(
'x_pertub') # the data is presented as rasterized images
p_label = T.matrix('p_label')
drop = T.iscalar('drop')
activation = nonlinearity.relu
rng = np.random.RandomState(seed)
rng_share = theano.tensor.shared_randomstreams.RandomStreams(0)
input_x = x_pertub.reshape((batch_size, n_channels, dim_h, dim_w))
recg_layer = []
cnn_output = []
l = []
d = []
#1
recg_layer.append(
ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, n_channels, dim_h, dim_w),
filter_shape=(nkerns[0], n_channels, 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=activation))
if drops[0] == 1:
cnn_output.append(recg_layer[-1].drop_output(input=input_x,
drop=drop,
rng=rng_share,
p=drop_p[0]))
else:
cnn_output.append(recg_layer[-1].output(input=input_x))
l += [1, 2]
d += [1, 1]
#2
recg_layer.append(
ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[0], 16, 16),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[1] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share,
p=drop_p[1]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l += [1, 2]
d += [1, 1]
#3
recg_layer.append(
ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[1], 16, 16),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation))
if drops[2] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share,
p=drop_p[2]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l += [1, 2]
d += [1, 1]
#4
recg_layer.append(
ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[2], 8, 8),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
if drops[3] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share,
p=drop_p[3]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l += [1, 2]
d += [1, 1]
#5
'''
--------------------- (2,2) or (4,4)
'''
recg_layer.append(
ConvMaxPool_GauInit_DNN.ConvMaxPool_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[3], 8, 8),
filter_shape=(nkerns[4], nkerns[3], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation))
if drops[4] == 1:
cnn_output.append(recg_layer[-1].drop_output(cnn_output[-1],
drop=drop,
rng=rng_share,
p=drop_p[4]))
else:
cnn_output.append(recg_layer[-1].output(cnn_output[-1]))
l += [1, 2]
d += [1, 1]
mlp_input_x = cnn_output[-1].flatten(2)
activations = []
activations.append(mlp_input_x)
#1
'''
---------------------No MLP
'''
'''
recg_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in= 4 * 4 * nkerns[-1],
n_out=n_hidden[0],
activation=activation
))
if drops[-1]==1:
activations.append(recg_layer[-1].drop_output(input=mlp_input_x, drop=drop, rng=rng_share, p=drop_p[-1]))
else:
activations.append(recg_layer[-1].output(input=mlp_input_x))
'''
#stochastic layer
recg_layer.append(
GaussianHidden.GaussianHidden(rng=rng,
input=activations[-1],
n_in=4 * 4 * nkerns[-1],
n_out=n_hidden[0],
activation=None))
l += [1, 2]
d += [1, 1]
l += [1, 2]
d += [1, 1]
z = recg_layer[-1].sample_z(rng_share)
gene_layer = []
z_output = []
random_z_output = []
#1
gene_layer.append(
FullyConnected.FullyConnected(rng=rng,
n_in=n_hidden[0],
n_out=4 * 4 * nkerns[-1],
activation=activation))
z_output.append(gene_layer[-1].output(input=z))
random_z_output.append(gene_layer[-1].output(input=random_z))
l += [1, 2]
d += [1, 1]
#2
'''
gene_layer.append(FullyConnected.FullyConnected(
rng=rng,
n_in=n_hidden[0],
n_out = 4*4*nkerns[-1],
activation=activation
))
if drop_inverses[0]==1:
z_output.append(gene_layer[-1].drop_output(input=z_output[-1], drop=drop_inverse, rng=rng_share))
random_z_output.append(gene_layer[-1].drop_output(input=random_z_output[-1], drop=drop_inverse, rng=rng_share))
else:
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output(input=random_z_output[-1]))
'''
input_z = z_output[-1].reshape((batch_size, nkerns[-1], 4, 4))
input_random_z = random_z_output[-1].reshape((n_batch, nkerns[-1], 4, 4))
#1
gene_layer.append(
UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-1], 4, 4),
filter_shape=(nkerns[-2], nkerns[-1], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation))
l += [1, 2]
d += [1, 1]
z_output.append(gene_layer[-1].output(input=input_z))
random_z_output.append(gene_layer[-1].output_random_generation(
input=input_random_z, n_batch=n_batch))
#2
gene_layer.append(
UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-2], 8, 8),
filter_shape=(nkerns[-3], nkerns[-2], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
l += [1, 2]
d += [1, 1]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#3
gene_layer.append(
UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-3], 8, 8),
filter_shape=(nkerns[-4], nkerns[-3], 3, 3),
poolsize=(2, 2),
border_mode='same',
activation=activation))
l += [1, 2]
d += [1, 1]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#4
gene_layer.append(
UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-4], 16, 16),
filter_shape=(nkerns[-5], nkerns[-4], 3, 3),
poolsize=(1, 1),
border_mode='same',
activation=activation))
l += [1, 2]
d += [1, 1]
z_output.append(gene_layer[-1].output(input=z_output[-1]))
random_z_output.append(gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch))
#5-1 stochastic layer
# for this layer, the activation is None to get a Guassian mean
gene_layer.append(
UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-5], 16, 16),
filter_shape=(n_channels, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=None))
l += [1, 2]
d += [1, 1]
x_mean = gene_layer[-1].output(input=z_output[-1])
random_x_mean = gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch)
#5-2 stochastic layer
# for this layer, the activation is None to get logvar
if train_logvar:
gene_layer.append(
UnpoolConvNon_GauInit_DNN.UnpoolConvNon_GauInit_DNN(
rng,
image_shape=(batch_size, nkerns[-5], 16, 16),
filter_shape=(n_channels, nkerns[-5], 5, 5),
poolsize=(2, 2),
border_mode='same',
activation=None))
l += [1, 2]
d += [1, 1]
x_logvar = gene_layer[-1].output(input=z_output[-1])
random_x_logvar = gene_layer[-1].output_random_generation(
input=random_z_output[-1], n_batch=n_batch)
else:
x_logvar = theano.shared(
np.ones((batch_size, n_channels, dim_h, dim_w), dtype='float32'))
random_x_logvar = theano.shared(
np.ones((n_batch, n_channels, dim_h, dim_w), dtype='float32'))
gene_layer.append(
NoParamsGaussianVisiable.NoParamsGaussianVisiable(
#rng=rng,
#mean=z_output[-1],
#data=input_x,
))
logpx = gene_layer[-1].logpx(mean=x_mean, logvar=x_logvar, data=input_x)
random_x = gene_layer[-1].sample_x(rng_share=rng_share,
mean=random_x_mean,
logvar=random_x_logvar)
x_denoised = p_label * x + (1 - p_label) * x_mean.flatten(2)
mse = ((x - x_denoised)**2).sum() / pertub_number
params = []
for g in gene_layer:
params += g.params
for r in recg_layer:
params += r.params
'''
train_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: train_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
'''
'''
valid_activations = theano.function(
inputs=[index],
outputs=T.concatenate(activations, axis=1),
givens={
x_pertub: valid_set_x[index * batch_size: (index + 1) * batch_size],
drop: np.cast['int32'](0)
}
)
'''
test_activations = theano.function(inputs=[x_pertub],
outputs=T.concatenate(activations,
axis=1),
givens={drop: np.cast['int32'](0)})
imputation_model = theano.function(
inputs=[index, x_pertub],
outputs=[x_denoised, mse],
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
p_label: pertub_label[index * batch_size:(index + 1) * batch_size],
drop: np.cast['int32'](0),
#drop_inverse: np.cast['int32'](0)
})
##################
# Pretrain MODEL #
##################
model_epoch = 100
if os.environ.has_key('model_epoch'):
model_epoch = int(os.environ['model_epoch'])
if predir is not None:
color.printBlue('... setting parameters')
color.printBlue(predir)
if model_epoch == -1:
pre_train = np.load(predir + 'best-model.npz')
else:
pre_train = np.load(predir + 'model-' + str(model_epoch) + '.npz')
pre_train = pre_train['model']
if ctype == 'cva':
for (para, pre) in zip(params, pre_train):
para.set_value(pre)
elif ctype == 'cmmva':
for (para, pre) in zip(params, pre_train[:-2]):
para.set_value(pre)
else:
exit()
else:
exit()
###############
# TRAIN MODEL #
###############
print '... training'
scale = False
epoch = 0
n_visualization = 900
pixel_max = pixel_max[:n_visualization]
pixel_min = pixel_min[:n_visualization]
output = np.ones((n_visualization, visualization_times + 2,
n_channels * dim_input[0] * dim_input[1]))
output[:, 0, :] = test_set_x.get_value()[:n_visualization, :]
output[:, 1, :] = test_set_x_pertub.get_value()[:n_visualization, :]
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(
output[:, 0, :].T, pixel_max, pixel_min),
dim_input,
colorImg=colorImg,
scale=scale)
image.save(logdir + 'data.png', 'PNG')
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(
output[:, 1, :].T, pixel_max, pixel_min),
dim_input,
colorImg=colorImg,
scale=scale)
image.save(logdir + 'data_pertub.png', 'PNG')
tmp = test_set_x_pertub.get_value()
while epoch < denoise_times:
epoch = epoch + 1
for i in xrange(n_test_batches):
d, m = imputation_model(i,
tmp[i * batch_size:(i + 1) * batch_size])
tmp[i * batch_size:(i + 1) * batch_size] = np.asarray(d)
if epoch <= visualization_times:
output[:, epoch + 1, :] = tmp[:n_visualization, :]
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(
tmp[:n_visualization, :].T, pixel_max, pixel_min),
dim_input,
colorImg=colorImg,
scale=scale)
image.save(logdir + 'procedure-' + str(epoch) + '.png', 'PNG')
np.savez(logdir + 'procedure-' + str(epoch), tmp=tmp)
'''
image = paramgraphics.mat_to_img((output.reshape(-1,32*32*3)).T, dim_input, colorImg=colorImg, tile_shape=(n_visualization,22), scale=scale)
image.save(logdir+'output.png', 'PNG')
np.savez(logdir+'output', output=output)
'''
'''
'_params_' + str(start) + '_' + str(end) + '_noise_rawdata.mat', {
'z_test_original': data,
'z_test': data_perturbed,
'pertub_label': pertub_label
})
sio.savemat(
'data_imputation/' + dataset + '_params_' + str(int(pertub_prob)) +
'_max_min_pixel.mat', {
'pixel_max': pixel_max,
'pixel_min': pixel_min
})
print data_perturbed[:, :25].shape
scale = False
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(
data_perturbed[:, :25], pixel_max, pixel_min), (32, 32),
colorImg=True,
scale=scale)
image.save(
'data_imputation/' + dataset + '_' + 'test_noise_type_' +
str(pertub_type) + '_params_' + str(pertub_prob) + '.png', 'PNG')
print data[:, :25].shape
image = paramgraphics.mat_to_img(paramgraphics.scale_max_min(
data[:, :25], pixel_max, pixel_min), (32, 32),
colorImg=True,
scale=scale)
image.save(
'data_imputation/' + dataset + '_' + 'test_original_type_' +
str(pertub_type) + '_params_' + str(pertub_prob) + '.png', 'PNG')
