def train_with_Resnet():
from tensorgraphx.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 test_Atrous_Conv2d():
seq = tg.Sequential()
seq.add(
Atrous_Conv2D(input_channels=5,
num_filters=2,
kernel_size=(3, 3),
rate=3,
padding='SAME'))
h, w, c = 100, 300, 5
X_ph = tf.placeholder('float32', [None, h, w, c])
y_sb = seq.train_fprop(X_ph)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
out = sess.run(y_sb, feed_dict={X_ph: np.random.rand(32, h, w, c)})
print(out.shape)
assert out.shape[1] == h and out.shape[2] == w
seq = tg.Sequential()
r = 2
k = 5
seq.add(
Atrous_Conv2D(input_channels=5,
num_filters=2,
kernel_size=(k, k),
rate=r,
padding='VALID'))
h, w, c = 100, 300, 5
X_ph = tf.placeholder('float32', [None, h, w, c])
y_sb = seq.train_fprop(X_ph)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
out = sess.run(y_sb, feed_dict={X_ph: np.random.rand(32, h, w, c)})
print(out.shape)
assert out.shape[1] == h - 2 * int(
(k + (k - 1) * (r - 1)) / 2), out.shape[2] == w - 2 * int(
(w + (w - 1) * (r - 1)) / 2)
def train_with_Densenet():
from tensorgraphx.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 test_VGG19():
seq = tg.Sequential()
vgg = VGG19(input_channels=c, input_shape=(h, w))
print('output channels:', vgg.output_channels)
print('output shape:', vgg.output_shape)
out_dim = np.prod(vgg.output_shape) * vgg.output_channels
seq.add(vgg)
seq.add(Flatten())
seq.add(Linear(int(out_dim), nclass))
seq.add(Softmax())
train(seq)
def test_Dropout():
X_ph = tf.placeholder('float32', [None, 32])
seq = tg.Sequential()
seq.add(Linear(32, 20))
seq.add(Dropout(0.2, noise_shape=[-1, 20]))
out = seq.train_fprop(X_ph)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
out = sess.run(out, feed_dict={X_ph: np.random.rand(1, 32)})
print(out)
print(out.shape)
def test_UNet():
seq = tg.Sequential()
model = UNet(input_channels=c, input_shape=(h, w))
print('output channels:', model.output_channels)
print('output shape:', model.output_shape)
out_dim = np.prod(model.output_shape) * model.output_channels
seq.add(model)
seq.add(
MaxPooling(poolsize=tuple(model.output_shape),
stride=(1, 1),
padding='VALID'))
seq.add(Flatten())
seq.add(Linear(model.output_channels, nclass))
seq.add(Softmax())
train(seq)
def model():
with tf.name_scope('MnistCNN'):
seq = tg.Sequential()
seq.add(
Conv2D(input_channels=1,
num_filters=32,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
h, w = same(in_height=28,
in_width=28,
stride=(1, 1),
kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 32]))
seq.add(RELU())
seq.add(MaxPooling(poolsize=(2, 2), stride=(2, 2), padding='SAME'))
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(2, 2))
seq.add(LRN())
seq.add(
Conv2D(input_channels=32,
num_filters=64,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 64]))
seq.add(RELU())
seq.add(MaxPooling(poolsize=(2, 2), stride=(2, 2), padding='SAME'))
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(2, 2))
seq.add(LRN())
seq.add(Flatten())
seq.add(Linear(int(h * w * 64), 128))
seq.add(BatchNormalization(input_shape=[128]))
seq.add(Tanh())
seq.add(Dropout(0.8))
seq.add(Linear(128, 256))
seq.add(BatchNormalization(input_shape=[256]))
seq.add(Tanh())
seq.add(Dropout(0.8))
seq.add(Linear(256, 10))
seq.add(Softmax())
return seq
def test_DenseNet():
seq = tg.Sequential()
model = DenseNet(input_channels=c,
input_shape=(h, w),
ndense=1,
growth_rate=1,
nlayer1blk=1)
print('output channels:', model.output_channels)
print('output shape:', model.output_shape)
seq.add(model)
seq.add(
MaxPooling(poolsize=tuple(model.output_shape),
stride=(1, 1),
padding='VALID'))
seq.add(Flatten())
seq.add(Linear(model.output_channels, nclass))
seq.add(Softmax())
train(seq)
def test_Depthwise_Conv2d():
seq = tg.Sequential()
seq.add(
Depthwise_Conv2D(input_channels=5,
num_filters=2,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
X_ph = tf.placeholder('float32', [None, 100, 100, 5])
y_sb = seq.train_fprop(X_ph)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
out = sess.run(y_sb, feed_dict={X_ph: np.random.rand(32, 100, 100, 5)})
print(out.shape)
def test_OneHot():
X1 = tf.placeholder('int32', [5, 6, 7])
X2 = tf.placeholder('int32', [5, 6, 7, 8])
seq = tg.Sequential()
seq.add(OneHot(onehot_size=3))
y1 = seq.train_fprop(X1)
y2 = seq.train_fprop(X2)
with tf.Session() as sess:
print(
sess.run(y1,
feed_dict={
X1: np.random.random_integers(0, 2, [5, 6, 7])
}).shape)
print(
sess.run(y2,
feed_dict={
X2: np.random.random_integers(0, 2, [5, 6, 7, 8])
}).shape)
def train_with_VGG():
from tensorgraphx.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 tensorgraphx.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)
def test_ResNetBase():
seq = tg.Sequential()
model = ResNetBase(input_channels=c,
input_shape=(h, w),
config=[1, 1, 1, 1])
print('output channels:', model.output_channels)
print('output shape:', model.output_shape)
seq.add(model)
seq.add(
MaxPooling(poolsize=tuple(model.output_shape),
stride=(1, 1),
padding='VALID'))
outshape = valid_nd(model.output_shape,
kernel_size=model.output_shape,
stride=(1, 1))
print(outshape)
out_dim = model.output_channels
seq.add(Flatten())
seq.add(Linear(int(out_dim), nclass))
seq.add(Softmax())
train(seq)
def model(nclass, h, w, c):
with tf.name_scope('Cifar10AllCNN'):
seq = tg.Sequential()
seq.add(
Conv2D(input_channels=c,
num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 96]))
seq.add(
Conv2D(input_channels=96,
num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
seq.add(Dropout(0.5))
seq.add(
Conv2D(input_channels=96,
num_filters=96,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 96]))
seq.add(
Conv2D(input_channels=96,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
seq.add(Dropout(0.5))
seq.add(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 192]))
seq.add(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(3, 3))
seq.add(Dropout(0.5))
seq.add(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 192]))
seq.add(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(1, 1))
seq.add(Dropout(0.5))
seq.add(
Conv2D(input_channels=192,
num_filters=nclass,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(1, 1))
seq.add(BatchNormalization(input_shape=[h, w, nclass]))
seq.add(AvgPooling(poolsize=(h, w), stride=(1, 1), padding='VALID'))
seq.add(Flatten())
seq.add(Softmax())
return seq