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}
X_samples, y_samples = X_train[:25], y_train[:25]
input_shape = ds['X_train'].shape
input_ndim = len(input_shape)
print("Train shape:", ctext(X_train.shape, 'cyan'))
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_classes = len(labels)
# ===========================================================================
# Create model
# ===========================================================================
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(),
X_samples, y_samples = X_train[:25], y_train[:25]
input_shape = ds['X_train'].shape
input_ndim = len(input_shape)
print("Train shape:", ctext(X_train.shape, 'cyan'))
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(),
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'))
# ====== the network ====== #
if os.path.exists(MODEL_PATH):
x_vec = N.deserialize(path=MODEL_PATH, force_restore_vars=True)
else:
TRAIN_MODEL = True
with N.args_scope(
['TimeDelayedConv',
dict(time_pool='none', activation=K.relu)],
['Dense', dict(activation=K.linear, b_init=None)],
['BatchNorm', dict(activation=K.relu)]):
x_vec = N.Sequence([
N.Dropout(level=0.3),
N.TimeDelayedConv(n_new_features=512, n_time_context=5),
N.TimeDelayedConv(n_new_features=512, n_time_context=5),
N.TimeDelayedConv(n_new_features=512, n_time_context=7),
N.Dense(512),
N.BatchNorm(),
N.Dense(1500),
N.BatchNorm(),
N.StatsPool(axes=1, output_mode='concat'),
N.Flatten(outdim=2),
N.Dense(512, name="LatentOutput"),
N.BatchNorm(),
# ===========================================================================
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'))
# ====== the network ====== #
if os.path.exists(MODEL_PATH):
x_vec = N.deserialize(path=MODEL_PATH, force_restore_vars=True)
else:
TRAIN_MODEL = True
with N.args_scope(
['TimeDelayedConv', dict(time_pool='none', activation=K.relu)],
['Dense', dict(activation=K.linear, b_init=None)],
['BatchNorm', dict(activation=K.relu)]
):
x_vec = N.Sequence([
N.Dropout(level=0.3),
N.TimeDelayedConv(n_new_features=512, n_time_context=5),
N.TimeDelayedConv(n_new_features=512, n_time_context=5),
N.TimeDelayedConv(n_new_features=512, n_time_context=7),
N.Dense(512), N.BatchNorm(),
N.Dense(1500), N.BatchNorm(),
N.StatsPool(axes=1, output_mode='concat'),
N.Flatten(outdim=2),
# ====== load data feeder ====== #
(train, valid,
X_test_name, X_test_true, X_test_data,
labels) = prepare_data(feat=args.feat, label=args.task)
n_classes = len(labels)
# ===========================================================================
# Create model
# ===========================================================================
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(),
