Caffe Stacking LSTMs
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I have an input feature sequence of size [3, 1, 1024], clip1 variable [0, 1, 1] and clip2 variable [1, 1, 0]. What I aim to do is to stack two LSTM layers to go through this feature sequence first from left to right and then right to left. My existing prototxt is as follows:
input: "data"
input_shape { dim: 3 dim: 1 dim: 1024}
input: "clip1"
input_shape { dim: 3 dim: 1}
input: "clip2"
input_shape { dim: 3 dim: 1}
layer {
name: "lstm1"
type: "LSTM"
bottom: "data"
bottom: "clip"
top: "lstm1"
recurrent_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "lstm2"
type: "LSTM"
bottom: "lstm1"
bottom: "clip2"
top: "lstm2"
recurrent_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "fc8-final"
type: "InnerProduct"
bottom: "lstm2"
top: "fc8-final"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 101
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
axis: 2
}
}
layer {
name: "probs"
type: "Softmax"
bottom: "fc8-final"
top: "probs"
softmax_param {
axis: 2
}
}
layer {
bottom: "probs"
bottom: "probs"
top: "loss"
name: "dummyLoss"
type: "EuclideanLoss"
}
Question:
Is my prototxt the correct way to implement my idea? or should I slice lstm1 blob and concatenate the sliced blobs in a reverse order and then input the concatenated blob to lstm2 layer?
caffe lstm
asked Nov 7 at 7:32
Johnnylin
161113
add a comment |
up vote
1
down vote
favorite
I have an input feature sequence of size [3, 1, 1024], clip1 variable [0, 1, 1] and clip2 variable [1, 1, 0]. What I aim to do is to stack two LSTM layers to go through this feature sequence first from left to right and then right to left. My existing prototxt is as follows:
input: "data"
input_shape { dim: 3 dim: 1 dim: 1024}
input: "clip1"
input_shape { dim: 3 dim: 1}
input: "clip2"
input_shape { dim: 3 dim: 1}
layer {
name: "lstm1"
type: "LSTM"
bottom: "data"
bottom: "clip"
top: "lstm1"
recurrent_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "lstm2"
type: "LSTM"
bottom: "lstm1"
bottom: "clip2"
top: "lstm2"
recurrent_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "fc8-final"
type: "InnerProduct"
bottom: "lstm2"
top: "fc8-final"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 101
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
axis: 2
}
}
layer {
name: "probs"
type: "Softmax"
bottom: "fc8-final"
top: "probs"
softmax_param {
axis: 2
}
}
layer {
bottom: "probs"
bottom: "probs"
top: "loss"
name: "dummyLoss"
type: "EuclideanLoss"
}
Question:
Is my prototxt the correct way to implement my idea? or should I slice lstm1 blob and concatenate the sliced blobs in a reverse order and then input the concatenated blob to lstm2 layer?
caffe lstm
asked Nov 7 at 7:32
Johnnylin
161113
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have an input feature sequence of size [3, 1, 1024], clip1 variable [0, 1, 1] and clip2 variable [1, 1, 0]. What I aim to do is to stack two LSTM layers to go through this feature sequence first from left to right and then right to left. My existing prototxt is as follows:
input: "data"
input_shape { dim: 3 dim: 1 dim: 1024}
input: "clip1"
input_shape { dim: 3 dim: 1}
input: "clip2"
input_shape { dim: 3 dim: 1}
layer {
name: "lstm1"
type: "LSTM"
bottom: "data"
bottom: "clip"
top: "lstm1"
recurrent_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "lstm2"
type: "LSTM"
bottom: "lstm1"
bottom: "clip2"
top: "lstm2"
recurrent_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "fc8-final"
type: "InnerProduct"
bottom: "lstm2"
top: "fc8-final"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 101
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
axis: 2
}
}
layer {
name: "probs"
type: "Softmax"
bottom: "fc8-final"
top: "probs"
softmax_param {
axis: 2
}
}
layer {
bottom: "probs"
bottom: "probs"
top: "loss"
name: "dummyLoss"
type: "EuclideanLoss"
}
Question:
Is my prototxt the correct way to implement my idea? or should I slice lstm1 blob and concatenate the sliced blobs in a reverse order and then input the concatenated blob to lstm2 layer?
caffe lstm
asked Nov 7 at 7:32
Johnnylin
161113
I have an input feature sequence of size [3, 1, 1024], clip1 variable [0, 1, 1] and clip2 variable [1, 1, 0]. What I aim to do is to stack two LSTM layers to go through this feature sequence first from left to right and then right to left. My existing prototxt is as follows:
input: "data"
input_shape { dim: 3 dim: 1 dim: 1024}
input: "clip1"
input_shape { dim: 3 dim: 1}
input: "clip2"
input_shape { dim: 3 dim: 1}
layer {
name: "lstm1"
type: "LSTM"
bottom: "data"
bottom: "clip"
top: "lstm1"
recurrent_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "lstm2"
type: "LSTM"
bottom: "lstm1"
bottom: "clip2"
top: "lstm2"
recurrent_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 1
}
}
layer {
name: "fc8-final"
type: "InnerProduct"
bottom: "lstm2"
top: "fc8-final"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 101
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
axis: 2
}
}
layer {
name: "probs"
type: "Softmax"
bottom: "fc8-final"
top: "probs"
softmax_param {
axis: 2
}
}
layer {
bottom: "probs"
bottom: "probs"
top: "loss"
name: "dummyLoss"
type: "EuclideanLoss"
}
Question:
Is my prototxt the correct way to implement my idea? or should I slice lstm1 blob and concatenate the sliced blobs in a reverse order and then input the concatenated blob to lstm2 layer?
caffe lstm
caffe lstm
asked Nov 7 at 7:32
Johnnylin
161113
asked Nov 7 at 7:32
Johnnylin
161113
asked Nov 7 at 7:32
Johnnylin
161113
asked Nov 7 at 7:32
Johnnylin
161113
asked Nov 7 at 7:32
Johnnylin
161113
161113
add a comment |
add a comment |
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