Tensorflow模型评估基于批量大小

Question

我在TensorFlow中有一个图表，我已经根据成百上千个时期的32个观察值对其进行了训练。我现在想要根据训练好的图预测一些新数据，所以我保存了它并重新加载了它，但我被迫总是传递与我的批处理大小相同数量的观察值，因为我在我的图中声明了一个与批处理大小相对应的占位符。我怎样才能让我的图表接受任何数量的观察？

我应该如何配置它，以便我可以对任何数量的观察进行训练，然后在以后运行不同的数量？

下面是代码的一些重要部分的摘录。构建图表：

graph = tf.Graph()
    with graph.as_default():
        x = tf.placeholder(tf.float32, shape=[batch_size, self.image_height,  self.image_width, 1], name="data")

        y_ = tf.placeholder(tf.float32, shape=[batch_size, num_labels], name="labels")

        # Layer 1
        W_conv1 = weight_variable([patch_size, patch_size, 1, depth], name="weight_1")
        b_conv1 = bias_variable([depth], name="bias_1")

        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1, name="conv_1") + b_conv1, name="relu_1")
        h_pool1 = max_pool_2x2(h_conv1, name="pool_1")

        #Layer 2
        #W_conv2 = weight_variable([patch_size, patch_size, depth, depth*2])
        #b_conv2 = bias_variable([depth*2])

        #h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
        #h_pool2 = max_pool_2x2(h_conv2)

        # Densely Connected Layer
        W_fc1 = weight_variable([self.image_height/4 * self.image_width/2 * depth*2, depth], name="weight_2")
        b_fc1 = bias_variable([depth], name="bias_2")

        h_pool2_flat = tf.reshape(h_pool1, [-1, self.image_height/2 * self.image_width/2 * depth], name="reshape_1")
        h_fc1 = tf.nn.relu(tf.nn.xw_plus_b(h_pool2_flat, W_fc1, b_fc1), name="relu_2")

        keep_prob = tf.placeholder(tf.float32, name="keep_prob")
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob, name="drop_1")

        W_fc2 = weight_variable([depth, num_labels], name="dense_weight")
        b_fc2 = bias_variable([num_labels], name="dense_bias")

        logits = tf.nn.xw_plus_b(h_fc1_drop, W_fc2, b_fc2)
        tf.add_to_collection("logits", logits)
        y_conv = tf.nn.softmax(logits, name="softmax_1")
        tf.add_to_collection("y_conv", y_conv)


        with tf.name_scope("cross-entropy") as scope:
            cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_, name="cross_entropy_1"))
            ce_summ = tf.scalar_summary("cross entropy", cross_entropy, name="cross_entropy")

        optimizer = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy, name="min_adam_1")

        with tf.name_scope("prediction") as scope:
            correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
            accuracy_summary = tf.scalar_summary("accuracy", accuracy, name="accuracy_summary")

        merged = tf.merge_all_summaries()

加载和运行新数据

with tf.Session() as sess:
            new_saver = tf.train.import_meta_graph('./simple_model/one-layer-50.meta')
            new_saver.restore(sess, './simple_model/one-layer-50')
            logger.info("Model restored")
            image, _ = tf_nn.reformat(images, None, 3)

            x_image = tf.placeholder(tf.float32, shape=[image.shape[0], 28, 28, 1],
                                     name="data")
            keep_prob = tf.placeholder(tf.float32, name="keep_prob")

            feed_dict = {x_image: image, keep_prob: .01}
            y_ = tf.get_collection("y_")
            prediction = sess.run(y_, feed_dict=feed_dict)