我一直在实现这里描述的类别生成对抗网络。
[Jost T. Springenberg. 使用类别生成对抗网络进行无监督和半监督学习，2016年4月。]
这是第6页介绍的损失函数，问题在于公式使用了arg_max，这很奇怪，因为我能在各种框架（如Tensorflow）上使用的优化器大多只适用于arg_min。
所以你们能告诉我如何实现这个公式吗？
这是我实现的代码。
import tensorflow as tfimport numpy as npimport PIL.Image as Image# constantsX_dim = 256Y_dim = 2Z_dim = 256 * 256value_lambda = 1.0X = tf.placeholder(tf.float32, shape=[None, X_dim, X_dim, 1])Y = tf.placeholder(tf.float32, shape=[None, Y_dim])Z = tf.placeholder(tf.float32, shape=[None, Z_dim])initializer = tf.contrib.layers.variance_scaling_initializeractivation_function = tf.nn.eluregularizer = tf.contrib.layers.l2_regularizer(0.5)custom_filter = np.ones(shape=[32, 256, 256, 1], dtype=np.float)custom_filter[:, 255, :, :] = 0custom_filter[:, :, 255, :] = 0custom_filter = tf.constant(custom_filter, dtype=tf.float32)def discriminator(x, name=None): with tf.name_scope(name, "discriminator", [x]) as scope: D_conv_1 = tf.layers.conv2d(inputs=x, filters=16, kernel_size=[ 5, 5], padding='SAME', activation=activation_function, kernel_regularizer=regularizer) # [256, 256] D_mean_pool_1 = tf.nn.pool(D_conv_1, window_shape=[ 2, 2], pooling_type='AVG', padding='VALID', strides=[2, 2]) # [128, 128] D_conv_2 = tf.layers.conv2d(D_mean_pool_1, filters=32, kernel_size=[ 3, 3], padding='SAME', activation=activation_function, kernel_regularizer=regularizer) # [128, 128] D_mean_pool_2 = tf.nn.pool(D_conv_2, window_shape=[ 2, 2], pooling_type='AVG', padding='VALID', strides=[2, 2]) # [64, 64] D_conv_3 = tf.layers.conv2d(D_mean_pool_2, filters=64, kernel_size=[ 3, 3], padding='SAME', activation=activation_function, kernel_regularizer=regularizer) # [64, 64] D_mean_pool_3 = tf.nn.pool(D_conv_3, window_shape=[ 2, 2], pooling_type='AVG', padding='VALID', strides=[2, 2]) # [32, 32] D_conv_4 = tf.layers.conv2d(D_mean_pool_3, filters=128, kernel_size=[ 3, 3], padding='SAME', activation=activation_function, kernel_regularizer=regularizer) # [32, 32] D_mean_pool_4 = tf.nn.pool(D_conv_4, window_shape=[ 2, 2], pooling_type='AVG', padding='VALID', strides=[2, 2]) # [16, 16] D_conv_5 = tf.layers.conv2d(D_mean_pool_4, filters=256, kernel_size=[ 3, 3], padding='SAME', activation=activation_function, kernel_regularizer=regularizer) # [16, 16] D_mean_pool_5 = tf.nn.pool(D_conv_5, window_shape=[ 4, 4], pooling_type='AVG', padding='VALID', strides=[4, 4]) # [4, 4] D_conv_6 = tf.layers.conv2d(D_mean_pool_5, filters=2, kernel_size=[ 3, 3], padding='SAME', activation=activation_function, kernel_regularizer=regularizer) # [4, 4] D_mean_pool_6 = tf.nn.pool(D_conv_6, window_shape=[ 4, 4], pooling_type='AVG', padding='VALID', strides=[4, 4]) # [1, 1], and finally, [batch_size][1][1][2] D_logit = tf.reshape(D_mean_pool_6, shape=[32, 2]) # [batch_size][2] return D_logit ''' D_hidden_layer_1 = tf.layers.dense( inputs=x, units=255, activation=activation_function) D_hidden_layer_2 = tf.layers.dense( inputs=D_hidden_layer_1, units=16, activation=activation_function) D_logit = tf.layers.dense(inputs=D_hidden_layer_2, units=Y_dim, activation=activation_function) return D_logit '''def generator(z, name=None): with tf.name_scope(name, "generator", [z]) as scope: # z[32, 4096] input = tf.reshape(z, shape=[32, 256, 256, 1]) # input[32, 64, 64, 1] G_conv_1 = tf.layers.conv2d(input, filters=96, kernel_size=[ 8, 8], padding='SAME', activation=activation_function) # [32, 64, 64, 96] # G_upscaled_1 = tf.image.resize_bicubic(images=G_conv_1, size=[128, 128]) # [32, 128, 128, 96] G_conv_2 = tf.layers.conv2d(G_conv_1, filters=64, kernel_size=[ 5, 5], padding='SAME', activation=activation_function) # [32, 128, 128, 64] # G_upscaled_2 = tf.image.resize_bicubic(G_conv_2, size=[256, 256]) # [32, 256, 256, 64] G_conv_3 = tf.layers.conv2d(G_conv_2, filters=64, kernel_size=[ 5, 5], padding='SAME', activation=activation_function) # [32, 256, 256, 64] G_conv_4 = tf.layers.conv2d(G_conv_3, filters=1, kernel_size=[ 5, 5], padding='SAME', activation=activation_function) # [32, 256, 256, 1] G_logit = G_conv_4 * custom_filter # [32, 256, 256, 1], but filtered out the last column and row return G_logit ''' G_hidden_layer_1 = tf.layers.dense( inputs=z, units=255, activation=activation_function) G_outputs = tf.layers.dense(inputs=G_hidden_layer_1, units=X_dim, activation=activation_function) return G_outputs '''with tf.name_scope("training") as scope: # Getting samples from random data G_sample = generator(Z) # Getting logits D_logit_real = discriminator(X) D_logit_fake = discriminator(G_sample) # Applying softmax D_proba_real = tf.nn.softmax(logits=D_logit_real) D_proba_real = tf.clip_by_value( D_proba_real, clip_value_min=1e-4, clip_value_max=1.0) D_proba_fake = tf.nn.softmax(logits=D_logit_fake) D_proba_fake = tf.clip_by_value( D_proba_fake, clip_value_min=1e-4, clip_value_max=1.0) with tf.name_scope("category_1") as sub_scope: # Getting Shannon's entrophy in X's distribution D_log_real = tf.log(D_proba_real) D_entrophy_real = D_proba_real * D_log_real D_mean_real = tf.reduce_sum(D_entrophy_real, axis=1) D_mean_real = -D_mean_real D_entrophy_real_mean = tf.reduce_mean(D_mean_real, axis=0) D_entrophy_real_mean = tf.reshape(D_entrophy_real_mean, shape=[1]) with tf.name_scope("category_2") as sub_scope: # Gettning Shannon's entrophy in Z's distribution G_log_fake = tf.log(D_proba_fake) G_entrophy_fake = D_proba_fake * G_log_fake G_mean = tf.reduce_sum(G_entrophy_fake, axis=1) G_mean = -G_mean G_entrophy_fake_mean = tf.reduce_mean(G_mean, axis=0) G_entrophy_fake_mean = tf.reshape(G_entrophy_fake_mean, shape=[1]) with tf.name_scope("category_3") as sub_scope: # Getting Shannon's entrophy between classes D_class_mean = tf.reduce_mean(D_proba_real, axis=0, keep_dims=True) D_class_mean_log = tf.log(D_class_mean) D_class_entropy = D_class_mean * D_class_mean_log D_class = tf.reduce_sum(D_class_entropy, axis=1) D_class = -D_class D_class = tf.reshape(D_class, shape=[1]) G_class_mean = tf.reduce_mean(D_proba_fake, axis=0, keep_dims=True) G_class_mean_log = tf.log(G_class_mean) G_class_entrophy = G_class_mean * G_class_mean_log G_class = tf.reduce_sum(G_class_entrophy, axis=1) G_class = -G_class G_class = tf.reshape(G_class, shape=[1]) with tf.name_scope("supervised") as sub_scope: # Getting cross entrophy for labeled data D_labeled = Y * D_log_real D_cross_entrophy = tf.reduce_sum(D_labeled, axis=1) D_cross_entrophy = -D_cross_entrophy D_supervised = tf.reduce_mean(D_cross_entrophy, axis=0) D_supervised_weighted = value_lambda * D_supervised D_supervised_weighted = tf.reshape(D_supervised_weighted, shape=[1]) D_loss = D_class - D_entrophy_real_mean + \ G_entrophy_fake_mean + D_supervised_weighted G_loss = -G_class + G_entrophy_fake_mean D_loss = -D_loss D_solver = tf.train.AdamOptimizer().minimize(D_loss) G_solver = tf.train.AdamOptimizer().minimize(G_loss)# with tf.name_scope("testing") as scope:回答：
我做了一些研究，并向我在大公司从事深度学习研究的朋友们咨询了一些问题。结果表明，生成对抗网络并不擅长分类任务。所以我改变了主意，改用GoogLenet实现。问题解决了！
