d317e22332d549184116872b5f6ea8066c66fa2f,deepmedic/neuralnet/layers.py,SoftmaxLayer,makeLayer,#SoftmaxLayer#Any#Any#Any#,421

Before Change

        // At this last classification layer, the conv output needs to have bias added before the softmax.
        // NOTE: So, two biases are associated with this layer. self.b which is added in the ouput of the previous layer"s output of conv,
        // and this self._bClassLayer that is added only to this final output before the softmax.
        (self._b,
        logits_train,
        logits_val,
        logits_test) = makeBiasParamsAndApplyToFms(self.input["train"], self.input["val"], self.input["test"])
        self._params = self._params + [self._b]
        
        // ============ Softmax ==============

After Change

        // NOTE: So, two biases are associated with this layer. self.b which is added in the ouput of the previous layer"s output of conv,
        // and this self._bClassLayer that is added only to this final output before the softmax.
        
        bias_layer = BiasLayer(self.input["train"].shape[1])
        logits_train = bias_layer.apply(self.input["train"])
        logits_val = bias_layer.apply(self.input["val"])
        logits_test = bias_layer.apply(self.input["test"])
        self._params = self._params + bias_layer.trainable_params()
        
        
        // ============ Softmax ==============
        self.p_y_given_x_train = tf.nn.softmax(logits_train/t, axis=1)

Instances