f68733f5a94aff716051e16a5bea00b1f4059963,policy.py,PolicyNetwork,set_up_network,#PolicyNetwork#,45

Before Change

        _current_h_conv = h_conv_init
        for i in range(self.num_int_conv_layers):
            W_conv_intermediate.append(_weight_variable([3, 3, self.k, self.k], name="W_conv_inter" + str(i)))
            h_conv_intermediate.append(tf.nn.relu(_conv2d(_current_h_conv, W_conv_intermediate[-1]), name="h_conv_inter" + str(i)))
            _current_h_conv = h_conv_intermediate[-1]

        W_conv_final = _weight_variable([1, 1, self.k, 1], name="W_conv_final")

After Change

        h_conv_intermediate = []
        _current_h_conv = h_conv_init
        for i in range(self.num_int_conv_layers):
            with tf.name_scope("layer"+str(i)):
                W_conv_intermediate.append(_weight_variable([3, 3, self.k, self.k], name="W_conv"))
                h_conv_intermediate.append(tf.nn.relu(_conv2d(_current_h_conv, W_conv_intermediate[-1]), name="h_conv"))
                _current_h_conv = h_conv_intermediate[-1]

        W_conv_final = _weight_variable([1, 1, self.k, 1], name="W_conv_final")
        b_conv_final = tf.Variable(tf.constant(0, shape=[go.N ** 2], dtype=tf.float32), name="b_conv_final")
        h_conv_final = _conv2d(h_conv_intermediate[-1], W_conv_final)
        output = tf.nn.softmax(tf.reshape(h_conv_final, [-1, go.N ** 2]) + b_conv_final)

Instances