def train():
from tensorgraph.trainobject import train as mytrain
with tf.Session() as sess:
word_len = 20
sent_len = 50
# load data
X_train, y_train, nclass = tweets(word_len, sent_len)
# build model
X_ph, y_train_sb, y_test_sb = model(word_len, sent_len, nclass)
y_ph = tf.placeholder('float32', [None, nclass])
# set cost and optimizer
train_cost_sb = entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.001)
test_accu_sb = accuracy(y_ph, y_test_sb)
# train model
mytrain(session=sess,
feed_dict={X_ph:X_train, y_ph:y_train},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5, max_epoch=100,
percent_decrease=0, train_valid_ratio=[5,1],
batchsize=64, randomize_split=False)
def train_with_trainobject():
from tensorgraph.trainobject import train as mytrain
with tf.Session() as sess:
seq = model()
X_train, y_train, X_test, y_test = Mnist(flatten=False,
onehot=True,
binary=True,
datadir='.')
X_ph = tf.placeholder('float32', [None, 28, 28, 1])
y_ph = tf.placeholder('float32', [None, 10])
y_train_sb = seq.train_fprop(X_ph)
y_test_sb = seq.test_fprop(X_ph)
train_cost_sb = entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.001)
test_accu_sb = accuracy(y_ph, y_test_sb)
mytrain(session=sess,
feed_dict={
X_ph: X_train,
y_ph: y_train
},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5,
max_epoch=100,
percent_decrease=0,
train_valid_ratio=[5, 1],
batchsize=64,
randomize_split=False)
def train_with_trainobject():
from tensorgraph.trainobject import train as mytrain
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
X_train, y_train, X_test, y_test = Cifar10(contrast_normalize=False,
whiten=False)
_, h, w, c = X_train.shape
_, nclass = y_train.shape
seq = model(nclass=nclass, h=h, w=w, c=c)
X_ph = tf.placeholder('float32', [None, h, w, c])
y_ph = tf.placeholder('float32', [None, nclass])
y_train_sb = seq.train_fprop(X_ph)
y_test_sb = seq.test_fprop(X_ph)
train_cost_sb = entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.001)
test_accu_sb = accuracy(y_ph, y_test_sb)
mytrain(session=sess,
feed_dict={
X_ph: X_train,
y_ph: y_train
},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5,
max_epoch=100,
percent_decrease=0,
train_valid_ratio=[5, 1],
batchsize=64,
randomize_split=False)
def train_with_Resnet():
from tensorgraph.trainobject import train as mytrain
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
X_train, y_train, X_test, y_test = Cifar10(contrast_normalize=False,
whiten=False)
_, h, w, c = X_train.shape
_, nclass = y_train.shape
print('X max', np.max(X_train))
print('X min', np.min(X_train))
seq = tg.Sequential()
id1 = IdentityBlock(input_channels=c,
input_shape=(h, w),
nlayers=4,
filters=[32, 64])
seq.add(id1)
trans1 = TransitionLayer(input_channels=id1.output_channels,
input_shape=id1.output_shape)
seq.add(trans1)
id2 = IdentityBlock(input_channels=trans1.output_channels,
input_shape=trans1.output_shape,
nlayers=4,
filters=[64, 128])
seq.add(id2)
trans2 = TransitionLayer(input_channels=id2.output_channels,
input_shape=id2.output_shape)
seq.add(trans2)
seq.add(Flatten())
ldim = trans2.output_channels * np.prod(trans2.output_shape)
seq.add(Linear(ldim, nclass))
seq.add(Softmax())
X_ph = tf.placeholder('float32', [None, h, w, c])
y_ph = tf.placeholder('float32', [None, nclass])
y_train_sb = seq.train_fprop(X_ph)
y_test_sb = seq.test_fprop(X_ph)
train_cost_sb = entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.001)
test_accu_sb = accuracy(y_ph, y_test_sb)
mytrain(session=sess,
feed_dict={
X_ph: X_train,
y_ph: y_train
},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5,
max_epoch=100,
percent_decrease=0,
train_valid_ratio=[5, 1],
batchsize=64,
randomize_split=False)
def train_with_Densenet():
from tensorgraph.trainobject import train as mytrain
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
X_train, y_train, X_test, y_test = Cifar10(contrast_normalize=False,
whiten=False)
_, h, w, c = X_train.shape
_, nclass = y_train.shape
print('X max', np.max(X_train))
print('X min', np.min(X_train))
seq = tg.Sequential()
dense = DenseNet(input_channels=c,
input_shape=(h, w),
ndense=3,
growth_rate=4,
nlayer1blk=4)
seq.add(dense)
seq.add(Flatten())
ldim = dense.output_channels
seq.add(Linear(ldim, nclass))
seq.add(Softmax())
X_ph = tf.placeholder('float32', [None, h, w, c])
y_ph = tf.placeholder('float32', [None, nclass])
y_train_sb = seq.train_fprop(X_ph)
y_test_sb = seq.test_fprop(X_ph)
train_cost_sb = entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.001)
test_accu_sb = accuracy(y_ph, y_test_sb)
print(tf.global_variables())
print('..total number of global variables: {}'.format(
len(tf.global_variables())))
count = 0
for var in tf.global_variables():
count += int(np.prod(var.get_shape()))
print('..total number of global parameters: {}'.format(count))
mytrain(session=sess,
feed_dict={
X_ph: X_train,
y_ph: y_train
},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5,
max_epoch=100,
percent_decrease=0,
train_valid_ratio=[5, 1],
batchsize=64,
randomize_split=False)
def train(seq):
y_train_sb = seq.train_fprop(X_ph)
y_test_sb = seq.test_fprop(X_ph)
train_cost_sb = tg.cost.entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.0001)
test_accu_sb = tg.cost.accuracy(y_ph, y_test_sb)
with tf.Session() as sess:
mytrain(session=sess,
feed_dict={X_ph:X_train, y_ph:y_train},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5, max_epoch=1,
percent_decrease=0, train_valid_ratio=[5,1],
batchsize=1, randomize_split=False)
def train(seq):
y_train_sb = seq.train_fprop(X_ph)
y_test_sb = seq.test_fprop(X_ph)
train_cost_sb = tg.cost.entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.0001)
test_accu_sb = tg.cost.accuracy(y_ph, y_test_sb)
with tf.Session() as sess:
this_dir = os.path.dirname(os.path.realpath(__file__))
writer = tf.summary.FileWriter(this_dir + '/tensorboard', sess.graph)
mytrain(session=sess,
feed_dict={X_ph:X_train, y_ph:y_train},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5, max_epoch=1,
percent_decrease=0, train_valid_ratio=[5,1],
batchsize=1, randomize_split=False)
writer.close()
def train_with_VGG():
from tensorgraph.trainobject import train as mytrain
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
X_train, y_train, X_test, y_test = Cifar10(contrast_normalize=False,
whiten=False)
_, h, w, c = X_train.shape
_, nclass = y_train.shape
print('X max', np.max(X_train))
print('X min', np.min(X_train))
from tensorgraph.layers import VGG19
seq = tg.Sequential()
layer = VGG19(input_channels=c, input_shape=(h, w))
seq.add(layer)
seq.add(Flatten())
seq.add(Linear(512, nclass))
seq.add(Softmax())
X_ph = tf.placeholder('float32', [None, h, w, c])
y_ph = tf.placeholder('float32', [None, nclass])
y_train_sb = seq.train_fprop(X_ph)
y_test_sb = seq.test_fprop(X_ph)
train_cost_sb = entropy(y_ph, y_train_sb)
optimizer = tf.train.AdamOptimizer(0.001)
test_accu_sb = accuracy(y_ph, y_test_sb)
mytrain(session=sess,
feed_dict={
X_ph: X_train,
y_ph: y_train
},
train_cost_sb=train_cost_sb,
valid_cost_sb=-test_accu_sb,
optimizer=optimizer,
epoch_look_back=5,
max_epoch=100,
percent_decrease=0,
train_valid_ratio=[5, 1],
batchsize=64,
randomize_split=False)
