model_baseline.py 文件源码

def build(self, is_train=True):

        n = self.a_dim
        conv_info = self.conv_info

        # build loss and accuracy {{{
        def build_loss(logits, labels):
            # Cross-entropy loss
            loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)

            # Classification accuracy
            correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
            return tf.reduce_mean(loss), accuracy
        # }}}

        # Classifier: takes images as input and outputs class label [B, m]
        def C(img, q, scope='Classifier'):
            with tf.variable_scope(scope) as scope:
                log.warn(scope.name)
                conv_1 = conv2d(img, conv_info[0], is_train, s_h=3, s_w=3, name='conv_1')
                conv_2 = conv2d(conv_1, conv_info[1], is_train, s_h=3, s_w=3, name='conv_2')
                conv_3 = conv2d(conv_2, conv_info[2], is_train, name='conv_3')
                conv_4 = conv2d(conv_3, conv_info[3], is_train, name='conv_4')
                conv_q = tf.concat([tf.reshape(conv_4, [self.batch_size, -1]), q], axis=1)
                fc_1 = fc(conv_q, 256, name='fc_1')
                fc_2 = fc(fc_1, 256, name='fc_2')
                fc_2 = slim.dropout(fc_2, keep_prob=0.5, is_training=is_train, scope='fc_3/')
                fc_3 = fc(fc_2, n, activation_fn=None, name='fc_3')
                return fc_3

        logits = C(self.img, self.q, scope='Classifier')
        self.all_preds = tf.nn.softmax(logits)
        self.loss, self.accuracy = build_loss(logits, self.a)

        # Add summaries
        def draw_iqa(img, q, target_a, pred_a):
            fig, ax = tfplot.subplots(figsize=(6, 6))
            ax.imshow(img)
            ax.set_title(question2str(q))
            ax.set_xlabel(answer2str(target_a)+answer2str(pred_a, 'Predicted'))
            return fig

        try:
            tfplot.summary.plot_many('IQA/',
                                     draw_iqa, [self.img, self.q, self.a, self.all_preds],
                                     max_outputs=3,
                                     collections=["plot_summaries"])
        except:
            pass

        tf.summary.scalar("loss/accuracy", self.accuracy)
        tf.summary.scalar("loss/cross_entropy", self.loss)
        log.warn('Successfully loaded the model.')