def lenet5_with_dropout_original(graph, verbose=False):
with graph.as_default():
with tf.device('gpu:0'):
is_train = tf.placeholder_with_default(False,
shape=(),
name='is_train')
with tf.name_scope('inputs'):
x = tf.placeholder(DTYPE, shape=(None, 32, 32, 3), name='x')
y = tf.placeholder(tf.int32, shape=(None), name='y')
keep_prob = tf.placeholder_with_default(input=1.0,
shape=(),
name='keep_prob')
with tf.name_scope('augmentation'):
augmented = utils.augment_images(x, is_train)
with tf.name_scope('C1-conv'):
conv1 = tf.layers.conv2d(
augmented,
filters=6,
kernel_size=5,
strides=(1, 1),
padding='VALID',
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER)
if verbose:
print('C1-outshape:', conv1.get_shape().as_list())
with tf.variable_scope('S2-avg_pool'):
conv1 = utils.average_pooling_with_weights(
value=conv1,
ksize=(1, 2, 2, 1),
strides=(1, 2, 2, 1),
padding='VALID',
activation=tf.nn.tanh)
if verbose:
print('S2-outshape:', conv1.get_shape().as_list())
with tf.variable_scope('dropout1'):
conv1 = tf.nn.dropout(conv1, keep_prob)
with tf.variable_scope('C3-conv'):
conv2 = tf.layers.conv2d(
conv1,
filters=16,
kernel_size=5,
padding='VALID',
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER)
if verbose:
print('C3-outshape:', conv2.get_shape().as_list())
with tf.variable_scope('S4-avg_pool'):
conv2 = utils.average_pooling_with_weights(
value=conv2,
ksize=(1, 2, 2, 1),
strides=(1, 2, 2, 1),
padding='VALID',
activation=tf.nn.tanh)
if verbose:
print('S4-outshape:', conv2.get_shape().as_list())
with tf.variable_scope('dropout2'):
conv2 = tf.nn.dropout(conv2, keep_prob)
with tf.variable_scope('C5-conv'):
conv3 = tf.layers.conv2d(
conv2,
filters=120,
kernel_size=5,
padding='VALID',
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER)
if verbose:
print('C5-outshape:', conv3.get_shape().as_list())
conv3 = tf.nn.dropout(conv3, keep_prob)
with tf.name_scope('F6-fc'):
flatten = tf.layers.Flatten()(conv3)
fc1 = tf.layers.dense(inputs=flatten,
units=84,
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER,
name='fc2')
with tf.name_scope('dropout3'):
fc1 = tf.nn.dropout(fc1, keep_prob)
if verbose:
print('F6-outshape:', fc1.get_shape().as_list())
with tf.name_scope('logits'):
'''
logits = tf.layers.dense(inputs=fc1,
units=10,
kernel_initializer=DEFAULT_INITIALIZER)
'''
logits = utils.rbf_euclidean_layer(inputs=fc1, units=10)
return x, y, is_train, keep_prob, logits
def lenet5(graph, verbose=False):
# for regularization
with graph.as_default():
with tf.device('gpu:0'):
is_train = tf.placeholder_with_default(False,
shape=(),
name='is_train')
with tf.name_scope('inputs'):
x = tf.placeholder(DTYPE, shape=(None, 28, 28, 1), name='x')
y = tf.placeholder(tf.int32, shape=(None), name='y')
padded = tf.pad(x, [[0, 0], [2, 2], [2, 2], [0, 0]])
with tf.name_scope('C1-conv'):
conv1 = tf.layers.conv2d(
padded,
filters=6,
kernel_size=5,
strides=(1, 1),
padding='VALID',
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER)
if verbose:
print('C1-outshape:', conv1.get_shape().as_list())
with tf.variable_scope('S2-avg_pool'):
conv1 = utils.average_pooling_with_weights(
value=conv1,
ksize=(1, 2, 2, 1),
strides=(1, 2, 2, 1),
padding='VALID',
activation=tf.nn.tanh)
if verbose:
print('S2-outshape:', conv1.get_shape().as_list())
with tf.variable_scope('C3-conv'):
conv2 = tf.layers.conv2d(
conv1,
filters=16,
kernel_size=5,
padding='VALID',
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER)
if verbose:
print('C3-outshape:', conv2.get_shape().as_list())
with tf.variable_scope('S4-avg_pool'):
conv2 = utils.average_pooling_with_weights(
value=conv2,
ksize=(1, 2, 2, 1),
strides=(1, 2, 2, 1),
padding='VALID',
activation=tf.nn.tanh)
if verbose:
print('S4-outshape:', conv2.get_shape().as_list())
with tf.variable_scope('C5-conv'):
conv3 = tf.layers.conv2d(
conv2,
filters=120,
kernel_size=5,
padding='VALID',
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER)
if verbose:
print('C5-outshape:', conv3.get_shape().as_list())
with tf.name_scope('F6-fc'):
flatten = tf.layers.Flatten()(conv3)
fc1 = tf.layers.dense(inputs=flatten,
units=84,
activation=tf.nn.tanh,
kernel_initializer=DEFAULT_INITIALIZER,
name='fc2')
if verbose:
print('F6-outshape:', fc1.get_shape().as_list())
with tf.name_scope('logits'):
'''
logits = tf.layers.dense(inputs=fc1,
units=10,
kernel_initializer=DEFAULT_INITIALIZER)
'''
logits = utils.rbf_euclidean_layer(inputs=fc1, units=26)
return x, y, is_train, logits