def __init__(self, mini_batch_size):
self.image_shape = (16, 128, 128, 3)
learning_rate = 0.03
opt = keras.optimizers.Adam(lr=learning_rate)
opt1 = keras.optimizers.Adam(lr=learning_rate)
opt_slow = keras.optimizers.Adam(lr=1)
#Build and compile the discriminator
self.discriminator = create_discriminator_model()
self.discriminator.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=[
'accuracy',
tf.keras.metrics.TruePositives(),
tf.keras.metrics.FalsePositives(),
tf.keras.metrics.TrueNegatives(),
tf.keras.metrics.FalseNegatives()
])
#Build and compile the generator
self.generator = AutoEncoderModel()
self.generator.compile(loss='mse', optimizer=opt_slow)
#the generator takes a video as input and generates a modified video
z = Input(shape=(self.image_shape))
img = self.generator(z)
self.discriminator.trainable = False
validity = self.discriminator(img)
self.combined = Model(z, validity)
self.combined.compile(loss='binary_crossentropy',
optimizer=opt1,
metrics=['accuracy'])
self.dir_path = '/kaggle/input/ucf-crime-training-subset/tfrecords2/'
self.ds = build_dataset(self.dir_path,
batch_size=mini_batch_size,
file_buffer=512 * 1024)
def setup(self, X, num_centers, alpha, znum, save_to='dec_model'):
# Read previously trained _SAE
ae_model = AutoEncoderModel(self.xpu,
[X.shape[1], 500, 500, 2000, znum],
pt_dropout=0.2)
ae_model.load(
os.path.join(
save_to,
'SAE_zsize{}_wimgfeatures_descStats_zeromean.arg'.format(
str(znum)))) #_Nbatch_wimgfeatures
logging.log(
logging.INFO,
"Reading Autoencoder from file..: %s" % (os.path.join(
save_to, 'SAE_zsize{}_wimgfeatures_descStats_zeromean.arg'.
format(znum))))
self.ae_model = ae_model
logging.log(logging.INFO, "finished reading Autoencoder from file..: ")
self.dec_op = DECModel.DECLoss(num_centers, alpha)
label = mx.sym.Variable('label')
self.feature = self.ae_model.encoder
self.loss = self.dec_op(data=self.ae_model.encoder,
label=label,
name='dec')
self.args.update({
k: v
for k, v in self.ae_model.args.items()
if k in self.ae_model.encoder.list_arguments()
})
self.args['dec_mu'] = mx.nd.empty(
(num_centers, self.ae_model.dims[-1]), ctx=self.xpu)
self.args_grad.update({
k: mx.nd.empty(v.shape, ctx=self.xpu)
for k, v in self.args.items()
})
self.args_mult.update(
{k: k.endswith('bias') and 2.0 or 1.0
for k in self.args})
self.num_centers = num_centers
self.best_args = {}
self.best_args['num_centers'] = num_centers
self.best_args['znum'] = znum
######################
from decModel_wimgF_dualopt_descStats import *
from utilities import *
import model
save_to = r'Z:\Cristina\Section3\paper_notes_section3_MODIFIED\save_to\SAEmodels'
input_size = combX_allNME.shape[1]
latent_size = [input_size / rxf for rxf in [15, 10, 5, 2]]
############ BEST PERFORMIGN of Step 1 nzum = 2x
znum = 261
X = combX_allNME
y = roi_labels
xpu = mx.cpu()
ae_model = AutoEncoderModel(xpu, [X.shape[1], 500, 500, 2000, znum],
pt_dropout=0.2)
print('Loading autoencoder of znum = {}, post training'.format(znum))
ae_model.load(
os.path.join(
save_to,
'SAE_zsize{}_wimgfeatures_descStats_zeromean.arg'.format(str(znum))))
data_iter = mx.io.NDArrayIter({'data': X},
batch_size=X.shape[0],
shuffle=False,
last_batch_handle='pad')
# extract only the encoder part of the SAE
feature = ae_model.encoder
zspace = model.extract_feature(feature, ae_model.args, None, data_iter,
X.shape[0], xpu).values()[0]
# train/test splits (test is 10% of labeled data)
sep = int(combX_allNME.shape[0] * 0.10)
X_val = combX_allNME[:sep]
y_val = YnxG_allNME[1][:sep]
X_train = combX_allNME[sep:]
y_train = YnxG_allNME[1][sep:]
batch_size = 125 # 160 32*5 = update_interval*5
allAutoencoders = []
for output_size in latent_size:
# Train or Read autoencoder: interested in encoding/decoding the input nxg features into LD latent space
# optimized for clustering with DEC
xpu = mx.cpu()
ae_model = AutoEncoderModel(
xpu, [X_train.shape[1], 500, 500, 2000, output_size],
pt_dropout=0.2)
## Pre-train
ae_model.layerwise_pretrain(X_train,
batch_size,
50000,
'sgd',
l_rate=0.1,
decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(
20000, 0.1))
## finetune
ae_model.finetune(X_train,
batch_size,
100000,
'sgd',
# pylint: skip-file
import mxnet as mx
import numpy as np
import logging
import data
from autoencoder import AutoEncoderModel
if __name__ == '__main__':
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
ae_model = AutoEncoderModel(mx.gpu(0), [784,500,500,2000,10], pt_dropout=0.2,
internal_act='relu', output_act='relu')
X, _ = data.get_mnist()
train_X = X[:60000]
val_X = X[60000:]
ae_model.layerwise_pretrain(train_X, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
ae_model.finetune(train_X, 256, 100000, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
ae_model.save('mnist_pt.arg')
ae_model.load('mnist_pt.arg')
print "Training error:", ae_model.eval(train_X)
print "Validation error:", ae_model.eval(val_X)
lambda_u, lv, K)
fp.write('p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d\n' % \
(p,lambda_v,lambda_u,lv,K))
fp.close()
if is_dummy:
X = data.get_dummy_mult()
R = data.read_dummy_user()
else:
X = data.get_mult()
R = data.read_user()
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
#ae_model = AutoEncoderModel(mx.gpu(0), [784,500,500,2000,10], pt_dropout=0.2,
# internal_act='relu', output_act='relu')
ae_model = AutoEncoderModel(mx.cpu(2), [X.shape[1], 100, K],
pt_dropout=0.2,
internal_act='relu',
output_act='relu')
train_X = X
#ae_model.layerwise_pretrain(train_X, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
# lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
#V = np.zeros((train_X.shape[0],10))
V = np.random.rand(train_X.shape[0], K) / 10
lambda_v_rt = np.ones((train_X.shape[0], K)) * sqrt(lv)
U, V, theta, BCD_loss = ae_model.finetune(
train_X,
R,
V,
lambda_v_rt,
lambda_u,
help='the number of hidden units for the layers of the encoder.' \
'The decoder layers are created in the reverse order.')
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
opt = parser.parse_args()
logging.info(opt)
print_every = opt.print_every
batch_size = opt.batch_size
pretrain_num_iter = opt.pretrain_num_iter
finetune_num_iter = opt.finetune_num_iter
visualize = opt.visualize
layers = [int(i) for i in opt.num_units.split(',')]
if __name__ == '__main__':
ae_model = AutoEncoderModel(mx.cpu(0), layers, pt_dropout=0.2,
internal_act='relu', output_act='relu')
X, _ = data.get_mnist()
train_X = X[:60000]
val_X = X[60000:]
ae_model.layerwise_pretrain(train_X, batch_size, pretrain_num_iter, 'sgd', l_rate=0.1,
decay=0.0, lr_scheduler=mx.misc.FactorScheduler(20000,0.1),
print_every=print_every)
ae_model.finetune(train_X, batch_size, finetune_num_iter, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1), print_every=print_every)
ae_model.save('mnist_pt.arg')
ae_model.load('mnist_pt.arg')
print("Training error:", ae_model.eval(train_X))
print("Validation error:", ae_model.eval(val_X))
if visualize:
def setup(self, X, num_centers, alpha, znum, save_to='dec_model'):
self.sep = int(X.shape[0] * 0.75)
X_train = X[:self.sep]
X_val = X[self.sep:]
batch_size = 32 # 160 32*5 = update_interval*5
# Train or Read autoencoder: note is not dependent on number of clusters just on z latent size
ae_model = AutoEncoderModel(self.xpu,
[X.shape[1], 500, 500, 2000, znum],
pt_dropout=0.2)
if not os.path.exists(save_to + '_pt.arg'):
ae_model.layerwise_pretrain(X_train,
batch_size,
50000,
'sgd',
l_rate=0.1,
decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(
20000, 0.1))
ae_model.finetune(X_train,
batch_size,
100000,
'sgd',
l_rate=0.1,
decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000, 0.1))
ae_model.save(save_to + '_pt.arg')
logging.log(
logging.INFO,
"Autoencoder Training error: %f" % ae_model.eval(X_train))
logging.log(
logging.INFO,
"Autoencoder Validation error: %f" % ae_model.eval(X_val))
else:
ae_model.load(save_to + '_pt.arg')
logging.log(
logging.INFO,
"Reading Autoencoder from file..: %s" % (save_to + '_pt.arg'))
logging.log(
logging.INFO,
"Autoencoder Training error: %f" % ae_model.eval(X_train))
logging.log(
logging.INFO,
"Autoencoder Validation error: %f" % ae_model.eval(X_val))
self.ae_model = ae_model
logging.log(logging.INFO, "finished reading Autoencoder from file..: ")
# prep model for clustering
self.dec_op = DECModel.DECLoss(num_centers, alpha)
label = mx.sym.Variable('label')
self.feature = self.ae_model.encoder
self.loss = self.dec_op(data=self.ae_model.encoder,
label=label,
name='dec')
self.args.update({
k: v
for k, v in self.ae_model.args.items()
if k in self.ae_model.encoder.list_arguments()
})
self.args['dec_mu'] = mx.nd.empty(
(num_centers, self.ae_model.dims[-1]), ctx=self.xpu)
self.args_grad.update({
k: mx.nd.empty(v.shape, ctx=self.xpu)
for k, v in self.args.items()
})
self.args_mult.update(
{k: k.endswith('bias') and 2.0 or 1.0
for k in self.args})
self.num_centers = num_centers
self.znum = znum
self.batch_size = batch_size
self.G = self.ae_model.eval(X_train) / self.ae_model.eval(X_val)
def setup(self, X, num_centers, alpha, save_to='dec_model'):
sep = X.shape[0] * 9 / 10
X_train = X[:sep]
X_val = X[sep:]
ae_model = AutoEncoderModel(self.xpu, [X.shape[1], 500, 500, 2000, 10],
pt_dropout=0.2)
if not os.path.exists(save_to + '_pt.arg'):
ae_model.layerwise_pretrain(X_train,
256,
50000,
'sgd',
l_rate=0.1,
decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(
20000, 0.1))
ae_model.finetune(X_train,
256,
100000,
'sgd',
l_rate=0.1,
decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000, 0.1))
ae_model.save(save_to + '_pt.arg')
logging.log(
logging.INFO,
"Autoencoder Training error: %f" % ae_model.eval(X_train))
logging.log(
logging.INFO,
"Autoencoder Validation error: %f" % ae_model.eval(X_val))
else:
ae_model.load(save_to + '_pt.arg')
self.ae_model = ae_model
self.dec_op = DECModel.DECLoss(num_centers, alpha)
label = mx.sym.Variable('label')
self.feature = self.ae_model.encoder
self.loss = self.dec_op(data=self.ae_model.encoder,
label=label,
name='dec')
self.args.update({
k: v
for k, v in self.ae_model.args.items()
if k in self.ae_model.encoder.list_arguments()
})
self.args['dec_mu'] = mx.nd.empty(
(num_centers, self.ae_model.dims[-1]), ctx=self.xpu)
self.args_grad.update({
k: mx.nd.empty(v.shape, ctx=self.xpu)
for k, v in self.args.items()
})
self.args_mult.update(
{k: k.endswith('bias') and 2.0 or 1.0
for k in self.args})
self.num_centers = num_centers
fp.write('p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d\n' % \
(p,lambda_v,lambda_u,lv,K))
fp.close()
if is_dummy:
X = data.get_dummy_mult()
R = data.read_dummy_user()
else:
X = data.get_mult()
R = data.read_user()
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
#ae_model = AutoEncoderModel(mx.gpu(0), [784,500,500,2000,10], pt_dropout=0.2,
# internal_act='relu', output_act='relu')
#mx.cpu() no param needed for cpu.
ae_model = AutoEncoderModel(mx.cpu(), [X.shape[1],100,K],
pt_dropout=0.2, internal_act='relu', output_act='relu')
train_X = X
#ae_model.layerwise_pretrain(train_X, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
# lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
#V = np.zeros((train_X.shape[0],10))
V = np.random.rand(train_X.shape[0],K)/10
lambda_v_rt = np.ones((train_X.shape[0],K))*sqrt(lv)
U, V, theta, BCD_loss = ae_model.finetune(train_X, R, V, lambda_v_rt, lambda_u,
lambda_v, dir_save, batch_size,
num_iter, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
#ae_model.save('cdl_pt.arg')
np.savetxt(dir_save+'/final-U.dat',U,fmt='%.5f',comments='')
np.savetxt(dir_save+'/final-V.dat',V,fmt='%.5f',comments='')
import mxnet as mx
import numpy as np
import logging
from autoencoder import AutoEncoderModel
if __name__ == '__main__':
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
ae_model = AutoEncoderModel(mx.gpu(0), [784,500,500,2000,10], pt_dropout=0.2,
internal_act='relu', output_act='relu')
X=np.random.rand(100000,784)
train_X = X[:60000]
val_X = X[60000:]
ae_model.layerwise_pretrain(train_X, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
ae_model.finetune(train_X, 256, 100000, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
ae_model.save('mnist_pt.arg')
ae_model.load('mnist_pt.arg')
print "Training error:", ae_model.eval(train_X)
print "Validation error:", ae_model.eval(val_X)
help='the number of hidden units for the layers of the encoder.' \
'The decoder layers are created in the reverse order.')
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
opt = parser.parse_args()
logging.info(opt)
print_every = opt.print_every
batch_size = opt.batch_size
pretrain_num_iter = opt.pretrain_num_iter
finetune_num_iter = opt.finetune_num_iter
visualize = opt.visualize
layers = [int(i) for i in opt.num_units.split(',')]
if __name__ == '__main__':
ae_model = AutoEncoderModel(mx.cpu(0), layers, pt_dropout=0.2,
internal_act='relu', output_act='relu')
X, _ = data.get_mnist()
train_X = X[:60000]
val_X = X[60000:]
ae_model.layerwise_pretrain(train_X, batch_size, pretrain_num_iter, 'sgd', l_rate=0.1,
decay=0.0, lr_scheduler=mx.misc.FactorScheduler(20000,0.1),
print_every=print_every)
ae_model.finetune(train_X, batch_size, finetune_num_iter, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1), print_every=print_every)
ae_model.save('mnist_pt.arg')
ae_model.load('mnist_pt.arg')
print("Training error:", ae_model.eval(train_X))
print("Validation error:", ae_model.eval(val_X))
if visualize:
class GAN():
def __init__(self, mini_batch_size):
self.image_shape = (16, 128, 128, 3)
learning_rate = 0.03
opt = keras.optimizers.Adam(lr=learning_rate)
opt1 = keras.optimizers.Adam(lr=learning_rate)
opt_slow = keras.optimizers.Adam(lr=1)
#Build and compile the discriminator
self.discriminator = create_discriminator_model()
self.discriminator.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=[
'accuracy',
tf.keras.metrics.TruePositives(),
tf.keras.metrics.FalsePositives(),
tf.keras.metrics.TrueNegatives(),
tf.keras.metrics.FalseNegatives()
])
#Build and compile the generator
self.generator = AutoEncoderModel()
self.generator.compile(loss='mse', optimizer=opt_slow)
#the generator takes a video as input and generates a modified video
z = Input(shape=(self.image_shape))
img = self.generator(z)
self.discriminator.trainable = False
validity = self.discriminator(img)
self.combined = Model(z, validity)
self.combined.compile(loss='binary_crossentropy',
optimizer=opt1,
metrics=['accuracy'])
self.dir_path = '/kaggle/input/ucf-crime-training-subset/tfrecords2/'
self.ds = build_dataset(self.dir_path,
batch_size=mini_batch_size,
file_buffer=512 * 1024)
def train(self, epochs, mini_batch_size):
#this function will need to be added later
tf.summary.trace_off()
for epoch in range(epochs):
d_loss_sum = tf.zeros(6)
reconstruct_error_sum = 0
g_loss_sum = tf.zeros(2)
no_of_minibatches = 0
for minibatch, labels in self.ds:
# ---------------------
# Train Discriminator
# ---------------------
#normalize inputs
no_of_minibatches += 1
minibatch = tf.cast(tf.math.divide(minibatch, 255), tf.float32)
gen_vids = self.generator.predict(minibatch)
#might have to combine these to improve batch norm
d_loss_real = self.discriminator.train_on_batch(
minibatch, tf.ones((mini_batch_size, 1)))
d_loss_fake = self.discriminator.train_on_batch(
gen_vids, tf.zeros((mini_batch_size, 1)))
d_loss = 0.5 * tf.math.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
# The generator wants the discriminator to label the generated samples as valid (ones)
valid_y = tf.ones((mini_batch_size, 1))
# Train the generator
g_loss = self.combined.train_on_batch(minibatch, valid_y)
reconstruct_error = self.generator.train_on_batch(
minibatch, minibatch)
d_loss_sum += d_loss
g_loss_sum += g_loss
reconstruct_error_sum += reconstruct_error
print(no_of_minibatches)
self.combined.save_weights('/kaggle/working/weights_epoch%d' %
(epoch))
g_loss = g_loss_sum / no_of_minibatches
d_loss = d_loss_sum / no_of_minibatches
reconstruct_error = reconstruct_error_sum / no_of_minibatches
# Plot the progress
print(
"%d [D loss: %f, acc.: %.2f%%] [G loss: %f, accuracy %.2f%% from which %f is combined loss and %f is reconstruction loss]"
% (epoch, d_loss[0], 100 * d_loss[1],
g_loss[0] + reconstruct_error, g_loss[1] * 100, g_loss[0],
reconstruct_error))
tf.summary.trace_on()
def test(self, dev_set_path, mini_batch_size):
dev_set = build_test_dataset(dev_set_path,
batch_size=mini_batch_size,
file_buffer=500 * 1024)
no_of_minibatches = 0
ans_final = tf.zeros(6)
for minibatch, labels in dev_set:
no_of_minibatches += 1
ans = self.discriminator.test_on_batch(minibatch, (labels == 0),
reset_metrics=False)
ans_final = ans
print(no_of_minibatches, ans_final[0], ans_final[1], ans_final[2],
ans_final[3], ans_final[4], ans_final[5])
def test_real_vs_fake(self, dev_set_path, mini_batch_size):
dev_set = build_dataset(dev_set_path,
batch_size=mini_batch_size,
file_buffer=500 * 1024)
ans_final = tf.zeros(6)
no_of_minibatches = 0
for minibatch, labels in dev_set:
no_of_minibatches += 1
ans = self.discriminator.test_on_batch(minibatch,
labels,
reset_metrics=False)
fake_vals = np.random.random((mini_batch_size, 16, 128, 128, 3))
ans = self.discriminator.test_on_batch(fake_vals,
tf.zeros(
(mini_batch_size, 1)),
reset_metrics=False)
ans_final = ans
print(no_of_minibatches, ans_final[0], ans_final[1], ans_final[2],
ans_final[3], ans_final[4], ans_final[5])
def visualise_autoencoder_outputs(self, no_of_minibatches):
fourcc = VideoWriter_fourcc(
*'MP42') #some code required for VideoWriter
video = VideoWriter('/kaggle/working/reconstructed_video.avi', fourcc,
float(24),
(128, 128)) #creates video to store 1st segment
for i in range(no_of_minibatches):
inp = np.load(
"../input/tom-and-jerry-clips/minibatches/minibatch%d.npz" %
(i))
inp = inp['arr_0']
gen_vids = self.generator.predict(inp)
gen_vids *= 255
for j in range(16):
for k in range(16):
frame = np.uint8(gen_vids[j][k])
video.write(frame)
import mxnet as mx
import sys
sys.path.append("../")
sys.path.append("../../autoencoder")
import logging
import numpy as np
from autoencoder import AutoEncoderModel
#from visualize import visualize
from data_all import news_iterator
#ae_model = AutoEncoderModel(mx.gpu(0), [5000,100], pt_dropout=0.5)
ae_model = AutoEncoderModel(mx.gpu(3),[5000,100],internal_act='sigmoid', output_act='sigmoid', sparseness_penalty=1e-4, pt_dropout=0)
logging.basicConfig(level=logging.DEBUG)
#ae_model.load('../../autoencoder/news_20classes_small.arg')#classes_small.arg') #news_20_ltest.arg
ae_model.load('news_20classes_small_1e-4_non-neg.arg')
batch_size = 100
fea_sym = ae_model.loss.get_internals()#[3]
logging.info(fea_sym.list_outputs())
output=fea_sym['sparse_encoder_0_output']
fc3 = mx.symbol.FullyConnected(data=output, num_hidden=20)
softmax = mx.symbol.SoftmaxOutput(data=fc3, name='softmax')
#logging.info(softmax.list_arguments())
args=ae_model.args
datashape=(100,5000)
train, val, _ = news_iterator(input_size = 5000,batchsize=100)
#fc = softmax.get_internals()
#logging.info(fc.list_arguments())
args_shape,ow,aw = softmax.get_internals().infer_shape(data=datashape)
#logging.info(args_shape)
layers = str(size) + ",500,500,2000,10"
layers = [int(i) for i in layers.split(',')]
xpu = mx.gpu() if gpu else mx.cpu()
print("Training on {}".format("GPU" if gpu else "CPU"))
if (gpu):
hyperparameters['device'] = 'GPU'
else:
hyperparameters['device'] = 'CPU'
ae_model = AutoEncoderModel(xpu,
layers,
eval_batch_size,
hyperparameters=hyperparameters,
pt_dropout=0.2,
internal_act='relu',
output_act='relu')
split_point = int(len(X) * 0.75)
train_X = X[:split_point]
val_X = X[split_point:]
ae_model.layerwise_pretrain(train_X,
batch_size,
pretrain_num_iter,
'sgd',
l_rate=0.001,
decay=0.0,
lr_scheduler=mx.lr_scheduler.FactorScheduler(
# pylint: skip-file
import mxnet as mx
import numpy as np
import logging
import mnist_data as data
from math import sqrt
from autoencoder import AutoEncoderModel
if __name__ == '__main__':
lv = 1e-2# lv/ln in CDL
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
#ae_model = AutoEncoderModel(mx.gpu(0), [784,500,500,2000,10], pt_dropout=0.2,
# internal_act='relu', output_act='relu')
ae_model = AutoEncoderModel(mx.cpu(2), [784,500,500,2000,10], pt_dropout=0.2,
internal_act='relu', output_act='relu')
X, _ = data.get_mnist()
train_X = X[:60000]
val_X = X[60000:]
#ae_model.layerwise_pretrain(train_X, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
# lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
#V = np.zeros((train_X.shape[0],10))
V = np.random.rand(train_X.shape[0],10)/10
lambda_v_rt = np.ones((train_X.shape[0],10))*sqrt(lv)
ae_model.finetune(train_X, V, lambda_v_rt, 256,
20, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
ae_model.save('mnist_pt.arg')
ae_model.load('mnist_pt.arg')
def setup(self, X, num_centers, alpha, save_to='dec_model'):
sep = X.shape[0]*9/10
X_train = X[:sep]
X_val = X[sep:]
ae_model = AutoEncoderModel(self.xpu, [X.shape[1],500,500,2000,10], pt_dropout=0.2)
if not os.path.exists(save_to+'_pt.arg'):
ae_model.layerwise_pretrain(X_train, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
ae_model.finetune(X_train, 256, 100000, 'sgd', l_rate=0.1, decay=0.0,
lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
ae_model.save(save_to+'_pt.arg')
logging.log(logging.INFO, "Autoencoder Training error: %f"%ae_model.eval(X_train))
logging.log(logging.INFO, "Autoencoder Validation error: %f"%ae_model.eval(X_val))
else:
ae_model.load(save_to+'_pt.arg')
self.ae_model = ae_model
self.dec_op = DECModel.DECLoss(num_centers, alpha)
label = mx.sym.Variable('label')
self.feature = self.ae_model.encoder
self.loss = self.dec_op(data=self.ae_model.encoder, label=label, name='dec')
self.args.update({k:v for k,v in self.ae_model.args.items() if k in self.ae_model.encoder.list_arguments()})
self.args['dec_mu'] = mx.nd.empty((num_centers, self.ae_model.dims[-1]), ctx=self.xpu)
self.args_grad.update({k: mx.nd.empty(v.shape, ctx=self.xpu) for k,v in self.args.items()})
self.args_mult.update({k: k.endswith('bias') and 2.0 or 1.0 for k in self.args})
self.num_centers = num_centers
gpus = [mx.gpu(i) for i in range(num_gpu)]
dev=mx.gpu()
# train, val, voc = news_iterator(voc_count, 100)
vocfile = "/home/tingyubi/20w/data/tfidf_extraction-1_26.voc"
with open(vocfile,'r') as f:
voc = f.read().decode('utf-8').split(" ")
"""
data = mx.symbol.Variable('data')
fc1 = mx.symbol.FullyConnected(name='encoder_%d'%istack, data=data, num_hidden=20)
sig1 = mx.symbol.Activation(data=fc1, act_type='sigmoid')
sparse1 = mx.symbol.SparseReg(data=sig1, penalty=1e-3, sparseness_target=0.1)
fc2 = mx.symbol.FullyConnected(name='decoder_%d'%istack, data=sparse1, num_hidden=5000)
loss = mx.symbol.LinearRegressionOutput(data=fc2, name='softmax')
"""
aem = AutoEncoderModel(mx.gpu(3),[voc_count,1000,200],internal_act='sigmoid', output_act='sigmoid', sparseness_penalty=1e-4, pt_dropout=0)
aem.load('/home/tingyubi/20w/autoencoder/20w_1000_200_1e-4_non-neg.arg')
#print aem.loss.get_internals().list_outputs()
fc2 = aem.encoder
print aem.loss.get_internals().list_outputs()
#print aem.loss.get_internals().list_arguments()
model = aem
#print model.arg_params['encoder_0_weight'].shape
"""
for k in range(2):
words_index=np.argsort(model.args['encoder_0_weight'].asnumpy()[k,:])
logging.info('Topic %d' % k)
logging.info([voc[i] for i in words_index[-20:]])
"""
