ml_conv_trainer.py 文件源码

def _setup_initial_blobs(self):
        MLTrainer._setup_initial_blobs(self)

        self.output_conv_blob = "Conv_output_{}".format(self.model_id)
        workspace.FeedBlob(self.output_conv_blob, np.zeros(1, dtype=np.float32))

        self.conv_weights: List[str] = []
        self.conv_biases: List[str] = []

        for x in six.moves.range(len(self.dims) - 1):
            dim_in = self.dims[x]
            dim_out = self.dims[x + 1]
            kernel_h = self.conv_height_kernels[x]
            kernel_w = self.conv_width_kernels[x]

            weight_shape = [dim_out, kernel_h, kernel_w, dim_in]
            bias_shape = [dim_out, ]

            conv_weight_name = "ConvWeights_" + str(x) + "_" + self.model_id
            bias_name = "ConvBiases_" + str(x) + "_" + self.model_id
            self.conv_weights.append(conv_weight_name)
            self.conv_biases.append(bias_name)

            conv_bias = np.zeros(shape=bias_shape, dtype=np.float32)
            workspace.FeedBlob(bias_name, conv_bias)

            conv_weights = scipy.stats.norm(0, np.sqrt(1 / dim_in)).rvs(
                size=weight_shape
            ).astype(np.float32)
            workspace.FeedBlob(conv_weight_name, conv_weights)