ml_trainer.py 文件源码

def _setup_initial_blobs(self):
        # Define models
        self.score_model = ModelHelper(name="score_" + self.model_id)
        self.train_model = ModelHelper(name="train_" + self.model_id)

        # Create input, output, labels, and loss blobs
        self.input_blob = "ModelInput_" + self.model_id
        workspace.FeedBlob(self.input_blob, np.zeros(1, dtype=np.float32))
        self.output_blob = "ModelOutput_" + self.model_id
        workspace.FeedBlob(self.output_blob, np.zeros(1, dtype=np.float32))
        self.labels_blob = "ModelLabels_" + self.model_id
        workspace.FeedBlob(self.labels_blob, np.zeros(1, dtype=np.float32))
        self.loss_blob = "loss"  # "ModelLoss_" + self.model_id
        workspace.FeedBlob(self.loss_blob, np.zeros(1, dtype=np.float32))

        # Create blobs for model parameters
        self.weights: List[str] = []
        self.biases: List[str] = []

        for x in six.moves.range(len(self.layers) - 1):
            dim_in = self.layers[x]
            dim_out = self.layers[x + 1]

            weight_name = "Weights_" + str(x) + "_" + self.model_id
            bias_name = "Biases_" + str(x) + "_" + self.model_id
            self.weights.append(weight_name)
            self.biases.append(bias_name)

            bias = np.zeros(
                shape=[
                    dim_out,
                ], dtype=np.float32
            )
            workspace.FeedBlob(bias_name, bias)

            gain = np.sqrt(2) if self.activations[x] == 'relu' else 1
            weights = scipy.stats.norm(0, gain * np.sqrt(1 / dim_in)).rvs(
                size=[dim_out, dim_in]
            ).astype(np.float32)
            workspace.FeedBlob(weight_name, weights)