在tf.layers模块的帮助下，我创建了一个简单的CNN来在MNIST数据库上进行训练。
首先我们加载数据：
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)然后设置一些基本参数，构建并训练模型：
learning_rate = 0.01training_epochs = 10batch_size = 100x = tf.placeholder(tf.float32, [None, 784], name='InputData')y = tf.placeholder(tf.float32, [None, 10], name='LabelData')with tf.name_scope('Model'): input_layer = tf.reshape(x, [-1, 28, 28, 1], name='InputReshaped') conv1 = tf.layers.conv2d(inputs=input_layer, filters=32, kernel_size=[4, 4], padding="same", activation=tf.nn.relu) pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) dropout1 = tf.layers.dropout(inputs=pool1, rate=0.25) conv2 = tf.layers.conv2d(inputs=dropout1, filters=32, kernel_size=[4, 4], padding="same", activation=tf.nn.relu) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) dropout2 = tf.layers.dropout(inputs=pool2, rate=0.25) pool2_flat = tf.reshape(dropout2, [-1, 7 * 7 * 32]) dense = tf.layers.dense(inputs=pool2_flat, units=256, activation=tf.nn.relu) dropout3 = tf.layers.dropout(inputs=dense, rate=0.5) pred = tf.layers.dense(inputs=dropout3, units=10)with tf.name_scope('Loss'): loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y))with tf.name_scope('SGD'): optimizer = tf.train.GradientDescentOptimizer(learning_rate) train_step = optimizer.minimize(loss)with tf.name_scope('Accuracy'): acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) acc = tf.reduce_mean(tf.cast(acc, tf.float32))init = tf.global_variables_initializer()with tf.Session() as sess: sess.run(init) for epoch in range(training_epochs): avg_cost = 0. avg_acc = 0. total_batch = int(mnist.train.num_examples/batch_size) for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) _, c, ac = sess.run([train_step, loss, acc], feed_dict={x: batch_xs, y: batch_ys}) avg_cost += c / total_batch avg_acc += ac / total_batch print("Epoch: {:04}, avg_cost = {:.9f}, avg_acc = {:.4f}".format(epoch + 1, avg_cost, avg_acc )) print("Optimization Finished!")它运行良好，表现不错，并输出以下内容：
Epoch: 0001, avg_cost = 1.032827925, avg_acc = 0.7110Epoch: 0002, avg_cost = 0.271804677, avg_acc = 0.9180...Epoch: 0010, avg_cost = 0.067859485, avg_acc = 0.9790Optimization Finished!然而，我希望在不使用tf.layers的情况下重写模型。因此，我将Model块中的代码更改为以下内容 – 我认为这应该与之前的几乎相同：
def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1, mean = 0.1) return tf.Variable(initial)def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial)with tf.name_scope('Model'): with tf.name_scope('Input_L'): input_tsr = tf.reshape(x, [-1, 28, 28, 1], name='InputReshaped') with tf.name_scope('Conv1_L'): W_conv1 = weight_variable([4, 4, 1, 32]) b_conv1 = bias_variable([32]) conv1 = tf.add(tf.nn.conv2d(input_tsr, W_conv1, strides=[1, 1, 1, 1], padding='SAME'), b_conv1) h_conv1 = tf.nn.relu(conv1) h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') dropout1 = tf.nn.dropout(h_pool1, 0.75) with tf.name_scope('Conv2_L'): W_conv2 = weight_variable([4, 4, 32, 32]) b_conv2 = bias_variable([32]) conv2 = tf.add(tf.nn.conv2d(dropout1, W_conv2, strides=[1, 1, 1, 1], padding='SAME'), b_conv2) h_conv2 = tf.nn.relu(conv2) h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') dropout2 = tf.nn.dropout(h_pool2, 0.75) with tf.name_scope('Dense_L'): W_dense = weight_variable([7 * 7 * 32, 256]) b_dense = bias_variable([256]) flat_tsr = tf.reshape(dropout2, [-1, 7 * 7 * 32]) dense = tf.add(tf.matmul(flat_tsr, W_dense), b_dense) h_dense = tf.nn.relu(dense) dropout3 = tf.nn.dropout(h_dense, 0.5) with tf.name_scope('Output_L'): W_out = weight_variable([256, 10]) b_out = bias_variable([10]) pred = tf.add(tf.matmul(dropout3, W_out), b_out)不幸的是，它的表现非常差，准确率无法超过0.12，我认为这意味着模型只是在猜测正确答案。
Epoch: 0001, avg_cost = 22.226242821, avg_acc = 0.1106Epoch: 0002, avg_cost = 2.301470806, avg_acc = 0.1123...Epoch: 0010, avg_cost = 2.301233784, avg_acc = 0.1123Optimization Finished!为什么第二个模型无法正常学习？你能指出第一个模型和第二个模型之间的区别吗（除了权重和偏置的初始化）？
回答：
我认为文档中没有提到，但对于tf.layers子模块中的层，当未提供初始化器时，变量初始化器默认为glorot_uniform_initializer。
如果你相应地替换你的权重定义，应该会更接近你之前的结果。
