| 113 | } |
| 114 | |
| 115 | Status RunClassification(const ClassificationSignature& signature, |
| 116 | const Tensor& input, Session* session, Tensor* classes, |
| 117 | Tensor* scores) { |
| 118 | std::vector<string> output_tensor_names; |
| 119 | if (classes) { |
| 120 | output_tensor_names.push_back(signature.classes().tensor_name()); |
| 121 | } |
| 122 | if (scores) { |
| 123 | output_tensor_names.push_back(signature.scores().tensor_name()); |
| 124 | } |
| 125 | // Run the graph with our inputs and outputs. |
| 126 | std::vector<Tensor> outputs; |
| 127 | const Status run_status = |
| 128 | session->Run({{signature.input().tensor_name(), input}}, |
| 129 | output_tensor_names, {}, &outputs); |
| 130 | if (!run_status.ok()) { |
| 131 | return run_status; |
| 132 | } |
| 133 | // Ensure the output is shaped how we expect. |
| 134 | // There should be one string Tensor of shape, |
| 135 | // [batch_size, num_recommendations]. |
| 136 | if (outputs.size() != output_tensor_names.size()) { |
| 137 | return errors::Internal( |
| 138 | strings::StrCat("Expected ", output_tensor_names.size(), |
| 139 | " output tensor(s). Got: ", outputs.size())); |
| 140 | } |
| 141 | if (classes) { |
| 142 | *classes = outputs[0]; |
| 143 | TF_RETURN_IF_ERROR(BatchSizesMatch(input, *classes)); |
| 144 | } |
| 145 | if (scores) { |
| 146 | *scores = outputs[classes ? 1 : 0]; |
| 147 | TF_RETURN_IF_ERROR(BatchSizesMatch(input, *scores)); |
| 148 | } |
| 149 | return Status::OK(); |
| 150 | } |
| 151 | |
| 152 | Status GetRegressionSignature(const tensorflow::MetaGraphDef& meta_graph_def, |
| 153 | RegressionSignature* signature) { |