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Policy Gradients in Caffe
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name: "VPGNet" | |
layer { | |
name: "data" | |
type: "Input" | |
top: "data" | |
input_param { shape: {dim: 20 dim: 1} } | |
} | |
layer { | |
name: "reward" | |
type: "Input" | |
top: "reward" | |
input_param { shape: {dim:20 dim: 2} } | |
} | |
layer { | |
# This layer is used for numerical stability in the log layer. | |
type: "DummyData" | |
name: "epsilon" | |
top: "epsilon" | |
dummy_data_param { | |
shape: { dim: 20 dim: 2 } | |
data_filler: { type: "constant" value: 0.0000001 } | |
} | |
} | |
layer { | |
name: "fc1" | |
type: "InnerProduct" | |
bottom: "data" | |
top: "fc1" | |
inner_product_param { | |
num_output: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "softmax" | |
type: "Softmax" | |
bottom: "fc1" | |
top: "softmax" | |
} | |
layer { | |
name: "eps_softmax" | |
type: "Eltwise" | |
eltwise_param: { | |
operation: SUM | |
} | |
bottom: "epsilon" | |
bottom: "softmax" | |
top: "eps_softmax" | |
} | |
layer { | |
name: "log" | |
type: "Log" | |
bottom: "eps_softmax" | |
top: "log" | |
} | |
layer { | |
name: "rew_prod_log" | |
type: "Eltwise" | |
eltwise_param: { | |
operation: PROD | |
} | |
bottom: "log" | |
bottom: "reward" | |
top: "rew_prod_log" | |
} | |
layer { | |
name: "action_sum" | |
type: "Reduction" | |
reduction_param: { | |
operation: SUM | |
axis: 1 | |
} | |
bottom: "rew_prod_log" | |
top: "action_sum" | |
} | |
layer { | |
name: "loss" | |
type: "Reduction" | |
reduction_param: { | |
operation: MEAN | |
axis: 0 | |
} | |
bottom: "action_sum" | |
top: "loss" | |
loss_weight: 1 | |
} |
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