我在尝试构建一些神经网络，并且希望在激活函数之前使用批量归一化，但遇到了一些问题。我不确定是否正确使用了这些层。
graph = tf.Graph()with graph.as_default(): x = tf.placeholder(tf.float32, shape=(batch_size, image_width, image_height, image_depth), name='x') y = tf.placeholder(tf.float32, shape=(batch_size, num_categories), name='y') keep_prob = tf.placeholder(tf.float32, name='keep_prob') phase = tf.placeholder(tf.bool, name='phase') layer1_weights = tf.Variable(tf.truncated_normal(shape=(filter_size, filter_size, image_depth, num_filters), stddev=0.01)) layer1_biases = tf.Variable(tf.ones(shape=(num_filters))) layer2_weights = tf.Variable(tf.truncated_normal(shape=(filter_size, filter_size, num_filters, num_filters), stddev=0.01)) layer2_biases = tf.Variable(tf.ones(shape=(num_filters))) layer3_weights = tf.Variable(tf.truncated_normal(shape=(filter_size, filter_size, num_filters, num_filters*2), stddev=0.01)) layer3_biases = tf.Variable(tf.ones(shape=(num_filters*2))) layer4_weights = tf.Variable(tf.truncated_normal(shape=(filter_size, filter_size, num_filters*2, num_categories), stddev=0.01)) layer4_biases = tf.Variable(tf.ones(shape=(num_categories))) x = batch_normalization(x, training=phase) conv = tf.nn.conv2d(x, layer1_weights, [1, 1, 1, 1], padding='SAME') + layer1_biases conv = batch_normalization(conv, training=phase) conv = tf.nn.elu(conv) conv = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(conv, layer2_weights, [1, 1, 1, 1], padding='SAME') + layer2_biases conv = batch_normalization(conv, training=phase) conv = tf.nn.elu(conv) conv = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(conv, layer3_weights, [1, 1, 1, 1], padding='SAME') + layer3_biases conv = batch_normalization(conv, training=phase) conv = tf.nn.elu(conv) conv = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(conv, layer4_weights, [1, 1, 1, 1], padding='SAME') + layer4_biases conv = batch_normalization(conv, training=phase) conv = tf.nn.elu(conv) conv = tf.layers.average_pooling2d(conv, [4, 4], [4, 4]) shape = conv.get_shape().as_list() size = shape[1] * shape[2] * shape[3] conv = tf.reshape(conv, shape=[-1, size]) y_ = tf.nn.softmax(conv) # 损失函数 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=conv, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=0.0001) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_update_ops): train_step = optimizer.minimize(loss) # 准确率 accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y_, axis=1), tf.argmax(y, axis=1)), tf.float32))epochs = 1dropout = 0.5with tf.Session(graph=graph) as sess: sess.run(tf.global_variables_initializer()) losses = [] acc = [] for e in range(epochs): print('\nEpoch {}'.format(e+1)) for b in range(0, len(X_train), batch_size): be = min(len(X_train), b + batch_size) x_batch = X_train[b: be] y_batch = y_train[b: be] extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) l, a, _ = sess.run([loss, accuracy, train_step, extra_update_ops], feed_dict={x: x_batch, y: y_batch, keep_prob: dropout, phase: True}) losses += [l] acc += [a] print('\r[{:5d}/{:5d}] loss = {}'.format(be, len(X_train), l), end='') validation_accuracy = 0 for b in range(0, len(y_test), batch_size): be = min(len(y_test), b + batch_size) a = sess.run(accuracy, feed_dict={x: X_test[b: be], y: y_test[b: be], keep_prob: 1, phase: False}) validation_accuracy += a * (be - b) validation_accuracy /= len(y_test) training_accuracy = 0 for b in range(0, len(y_train), batch_size): be = min(len(y_train), b + batch_size) a = sess.run(accuracy, feed_dict={x: X_train[b: be], y: y_train[b: be], keep_prob: 1, phase: False}) training_accuracy += a * (be - b) training_accuracy /= len(y_train)plt.plot(losses)plt.plot(acc)plt.show()print('Validation accuracy: {}'.format(validation_accuracy))print()print('Training accuracy: {}'.format(training_accuracy))错误：我不知道为什么它说我没有为张量 x 提供值？
InvalidArgumentError: You must feed a value for placeholder tensor 'x' with dtype float and shape [16,32,32,3] [[Node: x = Placeholder[dtype=DT_FLOAT, shape=[16,32,32,3], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]回答：
在某一行中，你定义了 x 作为占位符
x = tf.placeholder(tf.float32, shape=(batch_size, image_width, image_height, image_depth), name='x')在接下来的某一行中，你用 batch_normalization 函数调用的结果覆盖了 x 变量
x = batch_normalization(x, training=phase)此时 x 不再是一个 tf.placeholder，因此当你在 feed_dict 中使用它时，你不是在覆盖一个 tf.placeholder 的值，而是在覆盖由 batch_normalization 操作生成的 tf.Tensor。
要解决这个问题，你可以更改这一行
x = batch_normalization(x, training=phase)为
x_bn = batch_normalization(x, training=phase)并在后续的行中用 x_bn 替换 x。
这样，占位符变量 x 就不会被覆盖，你的代码应该可以正常运行。
