def gender(X, f, **kwargs):
nb_gender = kwargs.get('nb_gender', 4)
if f is None:
f = N.Sequence([
N.Dimshuffle(pattern=(0, 1, 2, 'x')),
N.Conv(num_filters=32,
filter_size=3,
strides=1,
b_init=None,
pad='valid'),
N.BatchNorm(activation=K.relu),
N.Pool(pool_size=2, mode='avg'),
N.Conv(num_filters=64,
filter_size=3,
strides=1,
b_init=None,
pad='valid'),
N.BatchNorm(activation=K.relu),
N.Pool(pool_size=2, mode='avg'),
N.Flatten(outdim=3),
N.Dense(num_units=512, b_init=None),
N.BatchNorm(axes=(0, 1)),
N.AutoRNN(num_units=128,
rnn_mode='gru',
num_layers=2,
input_mode='linear',
direction_mode='unidirectional'),
N.Flatten(outdim=2),
N.Dense(num_units=nb_gender, activation=K.softmax)
],
debug=True)
return f(X), f
def test_seq(self):
X = K.placeholder((None, 28, 28, 1))
f = N.Sequence([
N.Conv(8, (3, 3), strides=1, pad='same'),
N.Dimshuffle(pattern=(0, 3, 1, 2)),
N.Flatten(outdim=2),
N.Noise(level=0.3, noise_dims=None, noise_type='gaussian'),
N.Dense(128, activation=tf.nn.relu),
N.Dropout(level=0.3, noise_dims=None),
N.Dense(10, activation=tf.nn.softmax)
])
y = f(X)
yT = f.T(y)
f1 = K.function(X, y, defaults={K.is_training(): True})
f2 = K.function(X, yT, defaults={K.is_training(): False})
f = cPickle.loads(cPickle.dumps(f))
y = f(X)
yT = f.T(y)
f3 = K.function(X, y, defaults={K.is_training(): True})
f4 = K.function(X, yT, defaults={K.is_training(): False})
x = np.random.rand(12, 28, 28, 1)
self.assertEquals(f1(x).shape, (2688, 10))
self.assertEquals(f3(x).shape, (2688, 10))
self.assertEqual(np.round(f1(x).sum(), 4), np.round(f3(x).sum(), 4))
self.assertEquals(y.shape.as_list(), (None, 10))
self.assertEquals(f2(x).shape, (12, 28, 28, 1))
self.assertEquals(f4(x).shape, (12, 28, 28, 1))
self.assertEqual(str(f2(x).sum())[:4], str(f4(x).sum())[:4])
self.assertEquals(yT.shape.as_list(), (None, 28, 28, 1))
def cnn(X, y):
nb_classes = y.shape.as_list()[-1]
with N.args_scope(['Conv', dict(b_init=None, activation=K.linear)],
['BatchNorm', dict(activation=K.relu)]):
f = N.Sequence([
N.Dimshuffle(pattern=(0, 2, 3, 1)),
N.Conv(32, (3, 3), pad='same', stride=(1, 1)),
N.BatchNorm(),
N.Conv(32, (3, 3), pad='same', stride=(1, 1),
b_init=0, activation=K.relu),
N.Pool(pool_size=(2, 2), strides=None, mode='max'),
N.Dropout(level=0.25),
#
N.Conv(64, (3, 3), pad='same', stride=(1, 1)),
N.BatchNorm(),
N.Conv(64, (3, 3), pad='same', stride=(1, 1),
b_init=0., activation=K.relu),
N.Pool(pool_size=(2, 2), strides=None, mode='max'),
N.Dropout(level=0.25),
#
N.Flatten(outdim=2),
N.Dense(512, activation=K.relu),
N.Dropout(level=0.5),
N.Dense(nb_classes, activation=K.linear)
], debug=1)
logit = f(X)
prob = tf.nn.softmax(logit)
return {'logit': logit, 'prob': prob}
def odin_net2():
"CNN"
f = N.Sequence([
N.Dimshuffle((0, 1, 2, 'x')),
N.Conv(12, (3, 3), strides=(1, 1), pad='same',
untie_biases=False,
W_init=random(3, 3, 1, 12),
activation=K.relu),
N.Pool(pool_size=(2, 2), strides=None, mode='max',
ignore_border=True),
N.Conv(16, (3, 3), strides=(1, 1), pad='same',
untie_biases=False,
W_init=random(3, 3, 12, 16),
activation=K.sigmoid),
N.Dimshuffle((0, 3, 1, 2))
])
return X1, f(X1)
def test_shape(self):
x = K.variable(np.ones((25, 8, 12)))
def test_func(func):
y = func(x)
yT = func.T(func(x))
self.assertEquals(K.eval(y).shape, tuple(y.shape.as_list()))
self.assertEquals(K.eval(yT).shape, (25, 8, 12))
self.assertEquals(K.eval(yT).shape, tuple(yT.shape.as_list()))
test_func(N.Flatten(outdim=2))
test_func(N.Flatten(outdim=1))
test_func(N.Reshape((25, 4, 2, 6, 2)))
test_func(N.Dimshuffle((2, 0, 1)))
def test_load_save3(self):
X = K.placeholder(shape=(None, 28, 28))
ops = N.Sequence([
N.Dimshuffle(pattern=(0, 1, 2, 'x')),
N.Conv(8, (3, 3), strides=(1, 1), pad='same', activation=K.relu),
K.pool2d,
N.Flatten(outdim=2),
N.Dense(64, activation=K.relu),
N.Dense(10, activation=K.softmax)
])
y = ops(X)
f1 = K.function(X, y)
ops_ = cPickle.loads(
cPickle.dumps(ops, protocol=cPickle.HIGHEST_PROTOCOL))
y_ = ops_(X)
f2 = K.function(X, y_)
x = np.random.rand(32, 28, 28)
self.assertEqual(np.sum(f1(x) - f2(x)), 0.)
def create():
f = N.Sequence([
N.Conv(8, (3, 3), strides=1, pad='same'),
N.Dimshuffle(pattern=(0, 3, 1, 2)),
N.FlattenLeft(outdim=2),
N.Noise(level=0.3, noise_dims=None, noise_type='gaussian'),
N.Dense(128, activation=K.relu),
N.Dropout(level=0.3, noise_dims=None),
N.Dense(10, activation=K.softmax)
],
debug=True)
y = f(X)
yT = f.T(y)
f1 = K.function(X, y)
f2 = K.function(X, yT)
cPickle.dump(f, open(U.get_modelpath('dummy.ai', override=True), 'w'))
_ = f1(x)
print(_.shape, _.sum())
_ = f2(x)
print(_.shape, _.sum())
print("Valid shape:", ctext(X_valid.shape, 'cyan'))
print("Test shape:", ctext(X_test.shape, 'cyan'))
# ====== create basic tensor ====== #
X = K.placeholder(shape=(None,) + input_shape[1:], name='X_input')
y = K.placeholder(shape=(None,), name='y_input')
# ===========================================================================
# Create the network
# ===========================================================================
LATENT_DROPOUT = 0.3
if args.cnn:
with N.args_scope(([N.Conv, N.Dense], dict(b_init=None, activation=K.linear)),
(N.BatchNorm, dict(activation=tf.nn.elu)),
(N.Pool, dict(mode='max', pool_size=2))):
f_encoder = N.Sequence([
N.Dropout(level=0.5),
N.Dimshuffle((0, 2, 3, 1)) if is_cifar10 else N.Dimshuffle((0, 1, 2, 'x')),
N.Conv(num_filters=32, filter_size=3, pad='valid'),
N.Pool(),
N.BatchNorm(),
N.Conv(num_filters=64, filter_size=3, pad='same'),
N.BatchNorm(),
N.Conv(num_filters=64, filter_size=3, pad='valid'),
N.BatchNorm(activation=tf.nn.elu),
N.Pool(),
N.Flatten(outdim=2),
N.Dense(num_units=args.dim)
], debug=True, name='EncoderNetwork')
# ===========================================================================
inputs = [
K.placeholder(shape=(None, ) + shape[1:],
dtype='float32',
name='input%d' % i)
for i, shape in enumerate(as_tuple_of_shape(train.shape))
]
X = inputs[0]
y = inputs[1]
print("Inputs:", ctext(inputs, 'cyan'))
# ====== create the networks ====== #
with N.args_scope([('Conv', 'Dense'),
dict(b_init=None, activation=K.linear, pad='same')],
['BatchNorm', dict(activation=K.relu)]):
f = N.Sequence([
N.Dimshuffle(pattern=(0, 1, 2, 'x')),
N.Conv(num_filters=32, filter_size=(9, 7)),
N.BatchNorm(),
N.Pool(pool_size=(3, 2), strides=2),
N.Conv(num_filters=64, filter_size=(5, 3)),
N.BatchNorm(),
N.Pool(pool_size=(3, 1), strides=(2, 1), name='PoolOutput1'),
N.Conv(num_filters=64, filter_size=(5, 3)),
N.BatchNorm(),
N.Pool(pool_size=(3, 2), strides=(2, 2), name='PoolOutput2'),
N.Flatten(outdim=2),
N.Dense(512, name="LatentDense"),
N.BatchNorm(),
N.Dense(512),
N.BatchNorm(),
N.Dense(n_classes)
# ===========================================================================
ds = F.load_cifar10()
print(ds)
X_learn = ds['X_train'][:].astype('float32') / 255.
y_learn = ds['y_train']
X_test = ds['X_test'][:].astype('float32') / 255.
y_test = ds['y_test']
# ===========================================================================
# Create network
# ===========================================================================
X = K.placeholder(shape=(None, ) + X_learn.shape[1:], name='X')
y_true = K.placeholder(shape=(None, ), name='y_true', dtype='int32')
f = N.Sequence([
N.Dimshuffle(pattern=(0, 2, 3, 1)),
N.Conv(32, (3, 3), pad='same', stride=(1, 1), activation=K.relu),
N.Conv(32, (3, 3), pad='same', stride=(1, 1), activation=K.relu),
N.Pool(pool_size=(2, 2), ignore_border=True, strides=None, mode='max'),
N.Dropout(level=0.25),
N.Conv(64, (3, 3), pad='same', stride=(1, 1), activation=K.relu),
N.Conv(64, (3, 3), pad='same', stride=(1, 1), activation=K.relu),
N.Pool(pool_size=(2, 2), ignore_border=True, strides=None, mode='max'),
N.Dropout(level=0.25),
N.Flatten(outdim=2),
N.Dense(512, activation=K.relu),
N.Dropout(level=0.5),
N.Dense(10, activation=K.softmax)
],
debug=True)
K.set_training(True)
print('X_train:', X_train.shape, 'y_train:', y_train.shape)
print('X_valid:', X_valid.shape, 'y_valid:', y_valid.shape)
E = tk.embed(embedding)
# these numbers must be the same for all time
print('Tokenizer:', np.sum(E), np.sum(X_train), np.sum(y_train),
np.sum(X_valid), np.sum(y_valid))
# ===========================================================================
# Building model
# ===========================================================================
X = K.placeholder(shape=(None, MAX_SEQ_LEN), dtype='int32', name='X')
y = K.placeholder(shape=(None, nb_labels), dtype='float32', name='y')
f = N.Sequence([
N.Embedding(tk.nb_words, embedding_dims, W_init=E),
N.Dimshuffle(pattern=(0, 1, 'x', 2)),
N.Conv(num_filters=128,
filter_size=(5, 1),
strides=1,
pad='valid',
activation=K.relu),
N.Pool(pool_size=(5, 1), pad='valid', mode='max'),
N.Conv(num_filters=128,
filter_size=(5, 1),
strides=1,
pad='valid',
activation=K.relu),
N.Pool(pool_size=(5, 1), pad='valid', mode='max'),
N.Conv(num_filters=128,
filter_size=(5, 1),
strides=1,
# ===========================================================================
USE_MNIST_DATA = True if 'mnist' in arg['ds'].lower() else False
if USE_MNIST_DATA:
ds = fuel.load_mnist()
else:
ds = fuel.load_cifar10()
X = K.placeholder(shape=(None, ) + ds['X_train'].shape[1:], name='X')
y = K.placeholder(shape=(None, ), name='y', dtype='int32')
# ===========================================================================
# Build network
# ===========================================================================
ops = N.Sequence([
N.Dimshuffle((0, 1, 2, 'x')) if USE_MNIST_DATA else None,
N.BatchNorm(axes='auto'),
N.Conv(32, (3, 3), strides=(1, 1), pad='same', activation=K.relu),
N.Pool(pool_size=(2, 2), strides=None),
N.Conv(64, (3, 3), strides=(1, 1), pad='same', activation=K.relu),
N.Pool(pool_size=(2, 2), strides=None),
N.Flatten(outdim=2),
N.Dense(256, activation=K.relu),
N.Dense(10, activation=K.softmax)
],
debug=True)
ops = cPickle.loads(cPickle.dumps(ops)) # test if the ops is pickle-able
K.set_training(True)
y_pred_train = ops(X)
K.set_training(False)
ds = F.load_imdb(nb_words=max_features, maxlen=maxlen)
X_train = K.placeholder(shape=(None, ) + ds['X_train'].shape[1:],
name='X_train')
X_score = K.placeholder(shape=(None, ) + ds['X_train'].shape[1:],
name='X_score')
y = K.placeholder(shape=(None, ), name='y', dtype='int32')
# ===========================================================================
# Build model
# ===========================================================================
f = N.Sequence(
[
N.Embedding(max_features, embedding_size),
N.Dropout(0.25),
N.Dimshuffle(pattern=(0, 1, 'x', 2)), # convolution on time dimension
N.Conv(nb_filter,
filter_size=(filter_length, 1),
pad='valid',
stride=(1, 1),
activation=K.relu),
N.Pool(pool_size=(pool_length, 1), mode='max'),
N.Flatten(outdim=3),
N.Merge(
[
N.Dense(lstm_output_size, activation=K.linear,
name='ingate'), # input-gate
N.Dense(lstm_output_size,
activation=K.linear,
name='forgetgate'), # forget-gate
N.Dense(lstm_output_size,
USE_MNIST_DATA = True if 'mnist' in arg['ds'].lower() else False
if USE_MNIST_DATA:
ds = fuel.load_mnist()
else:
ds = fuel.load_cifar10()
print(ds)
X = K.placeholder(shape=(None, ) + ds['X_train'].shape[1:], name='X')
y = K.placeholder(shape=(None, ), name='y', dtype='int32')
# ===========================================================================
# Build network
# ===========================================================================
ops = N.Sequence([
N.Dimshuffle((0, 1, 2, 'x')) if USE_MNIST_DATA else N.Dimshuffle(
(0, 2, 3, 1)),
N.Conv(32, filter_size=3, strides=1, pad='same', activation=K.linear),
N.BatchNorm(axes='auto', activation=K.relu),
N.Pool(pool_size=2, strides=None),
N.Dimshuffle(pattern=(0, 3, 1, 2)),
N.Flatten(outdim=3),
N.CudnnRNN(18,
initial_states=None,
rnn_mode='lstm',
num_layers=2,
input_mode='linear',
direction_mode='unidirectional',
params_split=False),
N.Flatten(outdim=2),
N.Dense(128, activation=K.relu),
def convolutional_vae(X, saved_states, **kwargs):
""" convolutional_vae
Return
------
[y_encoder, y_decoder]
States
------
[f_inference (encoder), f_generative (decoder)]
"""
n = kwargs.get('n', 10)
batch_size = K.get_shape(X)[0]
if batch_size is None:
raise ValueError(
"You must specify batch_size dimension for the input placeholder.")
# ====== init ====== #
if saved_states is None:
# Encoder
f_inference = N.Sequence([
N.Reshape(shape=(-1, 28, 28, 1)),
N.Conv(num_filters=32,
filter_size=3,
strides=1,
pad='valid',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.Conv(num_filters=64,
filter_size=5,
strides=2,
pad='same',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.Dropout(level=0.1),
N.Flatten(outdim=2),
N.Dense(num_units=n * 2, b_init=None),
N.BatchNorm(axes=0)
],
debug=True,
name='Encoder')
# Decoder
f_generative = N.Sequence([
N.Dimshuffle(pattern=(0, 'x', 'x', 1)),
N.TransposeConv(num_filters=64,
filter_size=3,
strides=1,
pad='valid',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.TransposeConv(num_filters=32,
filter_size=5,
strides=2,
pad='same',
b_init=init_ops.constant_initializer(0.),
activation=K.elu),
N.TransposeConv(num_filters=1,
filter_size=13,
strides=3,
pad='valid',
b_init=None),
N.BatchNorm(activation=K.linear),
N.Flatten(outdim=3)
],
debug=True,
name="Decoder")
else:
f_inference, f_generative = saved_states
# ====== Perfrom ====== #
# Encoder
y_encoder = f_inference(K.cast(X, 'float32'))
mu = y_encoder[:, :n]
sigma = K.softplus(y_encoder[:, n:])
qz = Normal(mu=mu, sigma=sigma, name='Normal_qz')
# Decoder
z = Normal(mu=K.zeros(shape=(batch_size, n)),
sigma=K.ones(shape=(batch_size, n)),
name="Normal_pz")
logits = f_generative(z)
X_reconstruct = Bernoulli(logits=logits)
# inference
params = f_inference.parameters + f_generative.parameters
inference = ed.KLqp(latent_vars={z: qz}, data={X_reconstruct: X})
# ====== get cost for training ====== #
# Bind p(x, z) and q(z | x) to the same placeholder for x.
if K.is_training():
import tensorflow as tf
inference.initialize()
if True:
optimizer = tf.train.AdamOptimizer(0.01, epsilon=1.0)
updates = optimizer.apply_gradients(
optimizer.compute_gradients(inference.loss, var_list=params))
init = tf.global_variables_initializer()
init.run()
f_train = K.function(X, inference.loss, updates)
else:
optimizer = tf.train.AdamOptimizer(0.01, epsilon=1.0)
inference.initialize(optimizer=optimizer, var_list=params)
init = tf.global_variables_initializer()
init.run()
f_train = lambda x: inference.update(feed_dict={X: x})['loss']
samples = K.sigmoid(logits)
return (samples, z, qz), (f_inference, f_generative)
