Tensor3 not used

[Dahll]

The 3rd tensor where you add previous porfolio repartition is never used. It's not what is explain by the paper.
`

        with tf.variable_scope("Tensor3"):
            #w from last periods
            # trick to have good dimensions
            w_wo_c = self.W_previous[:, 1:]
            w_wo_c = tf.expand_dims(w_wo_c, 1)
            w_wo_c = tf.expand_dims(w_wo_c, -1)
            self.tensor3 = tf.concat([self.conv2, w_wo_c], axis=3)

        with tf.variable_scope("Conv3"):
            #last feature map WITHOUT cash bias
            self.conv3 = tf.layers.conv2d(
                inputs=self.conv2,
                activation=tf.nn.relu,
                filters=1,
                strides=(self.n_filter_2 + 1, 1),
                kernel_size=(1, 1),
                padding='same')

        with tf.variable_scope("Tensor4"):
            #last feature map WITH cash bias
            self.tensor4 = tf.concat([self.cash_bias, self.conv3], axis=2)
            # we squeeze to reduce and get the good dimension
            self.squeezed_tensor4 = tf.squeeze(self.tensor4, [1, 3])

        with tf.variable_scope("Policy_Output"):
            # softmax layer to obtain weights
            self.action = tf.nn.softmax(self.squeezed_tensor4)`