def capsule_model_A(X, num_classes):
#input dimension:(25, 200, 300, 1)
with tf.variable_scope('capsule_' + str(3)):
nets = _conv2d_wrapper(X,
shape=[3, 300, 1, 32],
strides=[1, 2, 1, 1],
padding='VALID',
add_bias=True,
activation_fn=tf.nn.relu,
name='conv1') #output shape: (25, 99, 1, 32)
tf.logging.info('output shape: {}'.format(nets.get_shape()))
nets = capsules_init(nets,
shape=[1, 1, 32, 16],
strides=[1, 1, 1, 1],
padding='VALID',
pose_shape=16,
add_bias=True,
name='primary') # (25, 99, 1, 16, 16)
nets = capsule_conv_layer(
nets,
shape=[3, 1, 16, 16],
strides=[1, 1, 1, 1],
iterations=3,
name='conv2') #25, 97, 1, 16, 16 #25, 97, 1, 16
nets = capsule_flatten(nets) #25, 97*16, 16 #25, 97*16
poses, activations = capsule_fc_layer(nets, num_classes, 3, 'fc2')
return poses, activations
def capsule_model_A(X, num_classes):
with tf.variable_scope('capsule_' + str(3)):
print('capsule_' + str(3))
print('X ', X)
cnnout = _conv2d_wrapper(X, shape=[3, 300, 1, 32], strides=[1, 2, 1, 1], padding='VALID',
add_bias=True, activation_fn=tf.nn.relu, name='conv1')
print('cnnout', cnnout.shape)
tf.logging.info('output shape: {}'.format(cnnout.get_shape()))
poses_init, activations_init = capsules_init(cnnout, shape=[1, 1, 32, 16], strides=[1, 1, 1, 1],
padding='VALID', pose_shape=16, add_bias=True, name='primary')
print('poses_init', poses_init.shape)
print('activations_init', activations_init.get_shape())
poses_conv, activations_conv = capsule_conv_layer(poses_init, activations_init, shape=[3, 1, 16, 16], strides=[1, 1, 1, 1],
iterations=3, name='conv2')
print('poses_conv', poses_conv.shape)
print('activations_conv', activations_conv.shape)
poses_flat, activations_flat = capsule_flatten(poses_conv, activations_conv)
print('capsule_flatten', poses_flat.shape)
print('activations_flat', activations_flat.shape)
poses, activations = capsule_fc_layer(poses_flat, activations_flat, num_classes, 3, 'fc2')
print('poses ', poses.shape)
print('activations ', activations.shape)
return poses, activations
def capsule_model_B(X, num_classes):
poses_list = []
for _, ngram in enumerate([3, 4, 5]):
with tf.variable_scope('capsule_' + str(ngram)):
nets = _conv2d_wrapper(X,
shape=[ngram, 300, 1, 32],
strides=[1, 2, 1, 1],
padding='VALID',
add_bias=True,
activation_fn=tf.nn.relu,
name='conv1')
tf.logging.info('output shape: {}'.format(nets.get_shape()))
nets = capsules_init(nets,
shape=[1, 1, 32, 16],
strides=[1, 1, 1, 1],
padding='VALID',
pose_shape=16,
add_bias=True,
name='primary')
nets = capsule_conv_layer(nets,
shape=[3, 1, 16, 16],
strides=[1, 1, 1, 1],
iterations=3,
name='conv2')
nets = capsule_flatten(nets)
poses, activations = capsule_fc_layer(nets, num_classes, 3, 'fc2')
poses_list.append(poses)
poses = tf.reduce_mean(tf.convert_to_tensor(poses_list), axis=0)
activations = K.sqrt(K.sum(K.square(poses), 2))
return poses, activations
def capsule_model_B(X, num_classes):
print('X.shape', X.shape) # (25, 200, 300, 1)
print('num_classes: ', num_classes.shape)
poses_list = []
for _, ngram in enumerate([3, 4, 5]):
with tf.variable_scope('capsule_' + str(ngram)):
print('capsule_' + str(ngram))
cnnout = _conv2d_wrapper(
X, shape=[ngram, 300, 1, 32], strides=[1, 2, 1, 1], padding='VALID',
add_bias=True, activation_fn=tf.nn.relu, name='conv1'
)
print('cnnout', cnnout.shape) # (25, 99, 1, 32)
tf.logging.info('output shape: {}'.format(cnnout.get_shape()))
poses_init, activations_init = capsules_init(cnnout, shape=[1, 1, 32, 16], strides=[1, 1, 1, 1],
padding='VALID', pose_shape=16, add_bias=True, name='primary')
print('poses_init', poses_init.shape) # (25, 99, 1, 16, 16)
print('activations_init', activations_init.get_shape()) # (25, 99, 1, 16)
poses_conv, activations_conv = capsule_conv_layer(poses_init, activations_init, shape=[3, 1, 16, 16], strides=[1, 1, 1, 1],
iterations=3, name='conv2')
print('poses_conv', poses_conv.shape) # (25, 97, 1, 16, 16)
print('activations_conv', activations_conv.shape) # (25, 97, 1, 16)
poses_flat, activations_flat = capsule_flatten(poses_conv, activations_conv)
print('capsule_flatten', poses_flat.shape) # (25, 1552, 16)
print('activations_flat', activations_flat.shape) # (25, 1552)
poses, activations = capsule_fc_layer(poses_flat, activations_flat, num_classes, 3, 'fc2')
print('poses ', poses.shape) # (25, 9, 16)
print('activations ', activations.shape) # (25, 9)
poses_list.append(poses)
print('-------------------------------')
poses = tf.reduce_mean(tf.convert_to_tensor(poses_list), axis=0)
print('poses ', poses.shape)
activations = K.sqrt(K.sum(K.square(poses), 2))
print('activations ', activations.shape)
return poses, activations