WanjieZhan
commented
3 years ago 我也遇到了相同的问题，好像是因为batch_dot()改版了，跟原来的不一样了！
Open daanvdn opened 4 years ago
WanjieZhan
commented
3 years ago 我也遇到了相同的问题，好像是因为batch_dot()改版了，跟原来的不一样了！
WanjieZhan
commented
3 years ago 我把capsulelayers.py 中的call()修改成了这样
def call(self, inputs, training=None):
# inputs_expand.shape=[None, 1, input_num_capsule, input_dim_capsule]
inputs_expand = K.expand_dims(inputs, 1)
# Replicate num_capsule dimension to prepare being multiplied by W
# inputs_tiled.shape=[None, num_capsule, input_num_capsule, input_dim_capsule](?,12,2805,8)
inputs_tiled = K.tile(inputs_expand, [1, self.num_capsule, 1, 1])
# Compute `inputs * W` by scanning inputs_tiled on dimension 0.
# x.shape=[num_capsule, input_num_capsule, input_dim_capsule]
# W.shape=[num_capsule, input_num_capsule, dim_capsule, input_dim_capsule]
# Regard the first two dimensions as `batch` dimension,
# then matmul: [input_dim_capsule] x [dim_capsule, input_dim_capsule]^T -> [dim_capsule].
# inputs_hat.shape = [None, num_capsule, input_num_capsule, dim_capsule](?,12,?,2805,16)
#inputs_hat = K.map_fn(lambda x: K.batch_dot(x, self.W, [2, 3]), elems=inputs_tiled)
inputs_hat = K.map_fn(lambda x: tf.reshape(tf.matmul(self.W,K.expand_dims(x,3)),[self.num_capsule,self.input_num_capsule,self.dim_capsule]), elems=inputs_tiled)
# Begin: Routing algorithm ---------------------------------------------------------------------#
# The prior for coupling coefficient, initialized as zeros.
# b.shape = [None, self.num_capsule, self.input_num_capsule]
b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.num_capsule, self.input_num_capsule])
assert self.routings > 0, 'The routings should be > 0.'
for i in range(self.routings):
# c.shape=[batch_size, num_capsule, input_num_capsule]
c = tf.nn.softmax(b, dim=1)
# c.shape = [batch_size, num_capsule, input_num_capsule]
# inputs_hat.shape=[None, num_capsule, input_num_capsule, dim_capsule]
# The first two dimensions as `batch` dimension,
# then matmal: [input_num_capsule] x [input_num_capsule, dim_capsule] -> [dim_capsule].
# outputs.shape=[None, num_capsule, dim_capsule]
#outputs = squash(K.batch_dot(c, inputs_hat, [2, 2])) # [None, 10, 16]
c = tf.expand_dims(c,2)
z = tf.matmul(c,inputs_hat)
outputs = squash(tf.reshape(z,[-1,self.num_capsule,self.dim_capsule]))
if i < self.routings - 1:
# outputs.shape = [None, num_capsule, dim_capsule]
# inputs_hat.shape=[None, num_capsule, input_num_capsule, dim_capsule]
# The first two dimensions as `batch` dimension,
# then matmal: [dim_capsule] x [input_num_capsule, dim_capsule]^T -> [input_num_capsule].
# b.shape=[batch_size, num_capsule, input_num_capsule]
#b += K.batch_dot(outputs, inputs_hat, [2, 3])
outputs1 = tf.expand_dims(outputs,3)
x = tf.matmul(inputs_hat,outputs1)
x = tf.reshape(x,[-1,self.num_capsule,self.input_num_capsule])
b += x
# End: Routing algorithm -----------------------------------------------------------------------#
return outputs
Hi,
I'm trying to run the capsulenet classifier using the command below:
python main.py --mode train_validation --classifier capsule --lang EN --sequence_length 100 --learning_rate 0.001 --learning_decay 1However, the
create_modelmethod throws an exception when constructing the model. The Traceback is as followsI'v made no changes to the code. The only difference is that I'm not using tensorflow-gpu, but plain tensorflow. Do you have any idea what might be causing this?
For completeness' sake I've added the output of pip freeze below: