实例化DCGAN
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dcgan = DCGAN( sess, input_width=FLAGS.input_width, input_height=FLAGS.input_height, output_width=FLAGS.output_width, output_height=FLAGS.output_height, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, dataset_name=FLAGS.dataset, input_fname_pattern=FLAGS.input_fname_pattern, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) |
batch_size 网络迭代同时处理的图像数量，共同决定网络该怎么更新
c_dim 输入图像的通道数，彩色图为3
dataset 数据集所在文件夹的名称
is_crop当输入图像比较大的时候，可以以图像为中心，进行相应尺寸的剪裁
checkpoint_dir用来保存模型参数的
DCGAN类解读
初始化
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def __init__(self, sess, input_height=108, input_width=108, is_crop=True, batch_size=64, sample_num = 64, output_height=64, output_width=64, y_dim=None, z_dim=100, gf_dim=64, df_dim=64, gfc_dim=1024, dfc_dim=1024, c_dim=3, dataset_name='default', input_fname_pattern='*.jpg', checkpoint_dir=None, sample_dir=None): """ Args: sess: TensorFlow session batch_size: The size of batch. Should be specified before training. y_dim: (optional) Dimension of dim for y. [None] z_dim: (optional) Dimension of dim for Z. [100] gf_dim: (optional) Dimension of gen filters in first conv layer. [64] df_dim: (optional) Dimension of discrim filters in first conv layer. [64] gfc_dim: (optional) Dimension of gen units for for fully connected layer. [1024] dfc_dim: (optional) Dimension of discrim units for fully connected layer. [1024] c_dim: (optional) Dimension of image color. For grayscale input, set to 1. [3] """ self.sess = sess self.is_crop = is_crop self.is_grayscale = (c_dim == 1) self.batch_size = batch_size self.sample_num = sample_num #测试生成网络 self.input_height = input_height self.input_width = input_width self.output_height = output_height self.output_width = output_width self.y_dim = y_dim self.z_dim = z_dim #生成网络训练时，输入噪声点的维度 self.gf_dim = gf_dim #64为基数 特征图的基数 self.df_dim = df_dim self.gfc_dim = gfc_dim #全连接的向量 1024 self.dfc_dim = dfc_dim self.c_dim = c_dim #最后想得到三通道的彩色图，即64*64*3 # batch normalization : deals with poor initialization helps gradient flow self.d_bn1 = batch_norm(name='d_bn1') #经过卷积，但还没经过relu的时候，进行一个归一化操作 self.d_bn2 = batch_norm(name='d_bn2') if not self.y_dim: self.d_bn3 = batch_norm(name='d_bn3') #判别网络三层 self.g_bn0 = batch_norm(name='g_bn0') self.g_bn1 = batch_norm(name='g_bn1') self.g_bn2 = batch_norm(name='g_bn2') if not self.y_dim: self.g_bn3 = batch_norm(name='g_bn3') #生成网络四层 self.dataset_name = dataset_name #文件夹的名字 self.input_fname_pattern = input_fname_pattern #取图片，默认为*.jpg self.checkpoint_dir = checkpoint_dir self.build_model() |
创建模型
在该部分中，完成了对判别网络和生成网络内部结构的构建
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def build_model(self): if self.y_dim: self.y= tf.placeholder(tf.float32, [self.batch_size, self.y_dim], name='y') if self.is_crop: image_dims = [self.output_height, self.output_width, self.c_dim] #希望crop后，输出是64*64*3 else: image_dims = [self.input_height, self.input_height, self.c_dim] self.inputs = tf.placeholder( tf.float32, [self.batch_size] + image_dims, name='real_images') #输入图像的大小 self.sample_inputs = tf.placeholder( tf.float32, [self.sample_num] + image_dims, name='sample_inputs') inputs = self.inputs sample_inputs = self.sample_inputs self.z = tf.placeholder( tf.float32, [None, self.z_dim], name='z') #噪音数据，None:添加多大的数都可以，？*100 self.z_sum = histogram_summary("z", self.z) if self.y_dim: <span style="color: #ff0000;">self.G = self.generator(self.z, self.y) #创建生成网络，输入为噪音向量</span> self.D, self.D_logits = \ self.discriminator(inputs, self.y, reuse=False) self.sampler = self.sampler(self.z, self.y) self.D_, self.D_logits_ = \ self.discriminator(self.G, self.y, reuse=True) else: self.G = self.generator(self.z) self.D, self.D_logits = self.discriminator(inputs) #真实的输入 self.sampler = self.sampler(self.z) self.D_, self.D_logits_ = self.discriminator(self.G, reuse=True) #生成的输入 self.d_sum = histogram_summary("d", self.D) self.d__sum = histogram_summary("d_", self.D_) self.G_sum = image_summary("G", self.G) self.d_loss_real = tf.reduce_mean( #利用了交叉熵函数 tf.nn.sigmoid_cross_entropy_with_logits( logits=self.D_logits, targets=tf.ones_like(self.D))) #希望真实输入判别网络判别为1 labels self.d_loss_fake = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits( logits=self.D_logits_, targets=tf.zeros_like(self.D_))) #生成输入希望判别网络为0 self.g_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits( logits=self.D_logits_, targets=tf.ones_like(self.D_))) #生成输入希望生成网络为1 self.d_loss_real_sum = scalar_summary("d_loss_real", self.d_loss_real) self.d_loss_fake_sum = scalar_summary("d_loss_fake", self.d_loss_fake) self.d_loss = self.d_loss_real + self.d_loss_fake self.g_loss_sum = scalar_summary("g_loss", self.g_loss) self.d_loss_sum = scalar_summary("d_loss", self.d_loss) t_vars = tf.trainable_variables() self.d_vars = [var for var in t_vars if 'd_' in var.name] self.g_vars = [var for var in t_vars if 'g_' in var.name] self.saver = tf.train.Saver() #保存节点 |
生成网络的构建
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def generator(self, z, y=None): with tf.variable_scope("generator") as scope: if not self.y_dim: s_h, s_w = self.output_height, self.output_width #64*64 s_h2, s_w2 = conv_out_size_same(s_h, 2), conv_out_size_same(s_w, 2) #32*32 s_h4, s_w4 = conv_out_size_same(s_h2, 2), conv_out_size_same(s_w2, 2) #16*16 s_h8, s_w8 = conv_out_size_same(s_h4, 2), conv_out_size_same(s_w4, 2) #8*8 s_h16, s_w16 = conv_out_size_same(s_h8, 2), conv_out_size_same(s_w8, 2) #4*4 每一层特征图的大小 # project `z` and reshape <span style="color: #ff0000;">self.z_, self.h0_w, self.h0_b = linear( #输入z向量，输出8192向量；全连接操作 z, self.gf_dim*8*s_h16*s_w16, 'g_h0_lin', with_w=True)</span> self.h0 = tf.reshape( self.z_, [-1, s_h16, s_w16, self.gf_dim * 8]) #reshape成 4*4*512,-1表示可推断出的数，reshape后，h0变成了一个特征图 <span style="color: #ff0000;">h0 = tf.nn.relu(self.g_bn0(self.h0)) #relu层</span> <span style="color: #ff0000;">self.h1, self.h1_w, self.h1_b = deconv2d( h0, [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='g_h1', with_w=True)</span> h1 = tf.nn.relu(self.g_bn1(self.h1)) <span style="color: #ff0000;">h2, self.h2_w, self.h2_b = deconv2d( h1, [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='g_h2', with_w=True)</span> h2 = tf.nn.relu(self.g_bn2(h2)) <span style="color: #ff0000;"> h3, self.h3_w, self.h3_b = deconv2d( h2, [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='g_h3', with_w=True)</span> h3 = tf.nn.relu(self.g_bn3(h3)) h4, self.h4_w, self.h4_b = deconv2d( h3, [self.batch_size, s_h, s_w, self.c_dim], name='g_h4', with_w=True) return tf.nn.tanh(h4) else: s_h, s_w = self.output_height, self.output_width s_h2, s_h4 = int(s_h/2), int(s_h/4) s_w2, s_w4 = int(s_w/2), int(s_w/4) # yb = tf.expand_dims(tf.expand_dims(y, 1),2) yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim]) z = concat([z, y], 1) h0 = tf.nn.relu( self.g_bn0(linear(z, self.gfc_dim, 'g_h0_lin'))) h0 = concat([h0, y], 1) h1 = tf.nn.relu(self.g_bn1( linear(h0, self.gf_dim*2*s_h4*s_w4, 'g_h1_lin'))) h1 = tf.reshape(h1, [self.batch_size, s_h4, s_w4, self.gf_dim * 2]) h1 = conv_cond_concat(h1, yb) h2 = tf.nn.relu(self.g_bn2(deconv2d(h1, [self.batch_size, s_h2, s_w2, self.gf_dim * 2], name='g_h2'))) h2 = conv_cond_concat(h2, yb) return tf.nn.sigmoid( deconv2d(h2, [self.batch_size, s_h, s_w, self.c_dim], name='g_h3')) |
全连接层
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def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False): shape = input_.get_shape().as_list() #100 with tf.variable_scope(scope or "Linear"): matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32, #w 100*8192 tf.random_normal_initializer(stddev=stddev)) bias = tf.get_variable("bias", [output_size], #b 8192 initializer=tf.constant_initializer(bias_start)) #初始化 if with_w: return tf.matmul(input_, matrix) + bias, matrix, bias else: return tf.matmul(input_, matrix) + bias |
反卷积层
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def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="deconv2d", with_w=False): with tf.variable_scope(name): # filter : [height, width, output_channels, in_channels] w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]], initializer=tf.random_normal_initializer(stddev=stddev)) try: deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1]) # Support for verisons of TensorFlow before 0.7.0 except AttributeError: deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1]) biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0)) #b的大小总与输出的大小有关 deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape()) if with_w: return deconv, w, biases else: return deconv |
判别网络的构建
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def discriminator(self, image, y=None, reuse=False): with tf.variable_scope("discriminator") as scope: if reuse: scope.reuse_variables() if not self.y_dim: h0 = lrelu(conv2d(image, self.df_dim, name='d_h0_conv')) h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv'))) h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv'))) h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv'))) h4 = linear(tf.reshape(h3, [self.batch_size, -1]), 1, 'd_h3_lin') return tf.nn.sigmoid(h4), h4 else: yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim]) x = conv_cond_concat(image, yb) h0 = lrelu(conv2d(x, self.c_dim + self.y_dim, name='d_h0_conv')) h0 = conv_cond_concat(h0, yb) h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim + self.y_dim, name='d_h1_conv'))) h1 = tf.reshape(h1, [self.batch_size, -1]) h1 = concat([h1, y], 1) h2 = lrelu(self.d_bn2(linear(h1, self.dfc_dim, 'd_h2_lin'))) h2 = concat([h2, y], 1) h3 = linear(h2, 1, 'd_h3_lin') return tf.nn.sigmoid(h3), h3 |
卷积层
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def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="conv2d"): with tf.variable_scope(name): w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim], initializer=tf.truncated_normal_initializer(stddev=stddev)) conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME') biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0)) conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape()) return conv |
训练DCGAN
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def train(self, config): """Train DCGAN""" if config.dataset == 'mnist': data_X, data_y = self.load_mnist() else: data = glob(os.path.join("./data", config.dataset, self.input_fname_pattern)) #图像读入 #np.random.shuffle(data) d_optim = tf.train.AdamOptimizer(config.learning_rate, beta1=config.beta1) \ #定义了优化器，最小化Loss .minimize(self.d_loss, var_list=self.d_vars) g_optim = tf.train.AdamOptimizer(config.learning_rate, beta1=config.beta1) \ .minimize(self.g_loss, var_list=self.g_vars) try: tf.global_variables_initializer().run() #全局变量初始化 except: tf.initialize_all_variables().run() self.g_sum = merge_summary([self.z_sum, self.d__sum, self.G_sum, self.d_loss_fake_sum, self.g_loss_sum]) self.d_sum = merge_summary( [self.z_sum, self.d_sum, self.d_loss_real_sum, self.d_loss_sum]) self.writer = SummaryWriter("./logs", self.sess.graph) sample_z = np.random.uniform(-1, 1, size=(self.sample_num , self.z_dim)) #生成一个batch的sample_z噪音向量 if config.dataset == 'mnist': #取数据 sample_inputs = data_X[0:self.sample_num] sample_labels = data_y[0:self.sample_num] else: sample_files = data[0:self.sample_num] sample = [ get_image(sample_file, input_height=self.input_height, input_width=self.input_width, resize_height=self.output_height, resize_width=self.output_width, is_crop=self.is_crop, is_grayscale=self.is_grayscale) for sample_file in sample_files] if (self.is_grayscale): sample_inputs = np.array(sample).astype(np.float32)[:, :, :, None] else: sample_inputs = np.array(sample).astype(np.float32) #归一化处理完的值 counter = 1 start_time = time.time() if self.load(self.checkpoint_dir): print(" [*] Load SUCCESS") else: print(" [!] Load failed...") for epoch in xrange(config.epoch): if config.dataset == 'mnist': batch_idxs = min(len(data_X), config.train_size) // config.batch_size else: data = glob(os.path.join( "./data", config.dataset, self.input_fname_pattern)) batch_idxs = min(len(data), config.train_size) // config.batch_size for idx in xrange(0, batch_idxs): if config.dataset == 'mnist': batch_images = data_X[idx*config.batch_size:(idx+1)*config.batch_size] batch_labels = data_y[idx*config.batch_size:(idx+1)*config.batch_size] else: batch_files = data[idx*config.batch_size:(idx+1)*config.batch_size] batch = [ get_image(batch_file, input_height=self.input_height, input_width=self.input_width, resize_height=self.output_height, resize_width=self.output_width, is_crop=self.is_crop, is_grayscale=self.is_grayscale) for batch_file in batch_files] if (self.is_grayscale): batch_images = np.array(batch).astype(np.float32)[:, :, :, None] else: batch_images = np.array(batch).astype(np.float32) #一个Batch的图像 batch_z = np.random.uniform(-1, 1, [config.batch_size, self.z_dim]) \ #对应生成网络的batch输入 .astype(np.float32) if config.dataset == 'mnist': # Update D network _, summary_str = self.sess.run([d_optim, self.d_sum], feed_dict={ self.inputs: batch_images, self.z: batch_z, self.y:batch_labels, }) self.writer.add_summary(summary_str, counter) # Update G network _, summary_str = self.sess.run([g_optim, self.g_sum], feed_dict={ self.z: batch_z, self.y:batch_labels, }) self.writer.add_summary(summary_str, counter) # Run g_optim twice to make sure that d_loss does not go to zero (different from paper) _, summary_str = self.sess.run([g_optim, self.g_sum], feed_dict={ self.z: batch_z, self.y:batch_labels }) self.writer.add_summary(summary_str, counter) errD_fake = self.d_loss_fake.eval({ self.z: batch_z, self.y:batch_labels }) errD_real = self.d_loss_real.eval({ self.inputs: batch_images, self.y:batch_labels }) errG = self.g_loss.eval({ self.z: batch_z, self.y: batch_labels }) else: # Update D network _, summary_str = self.sess.run([d_optim, self.d_sum], feed_dict={ self.inputs: batch_images, self.z: batch_z }) self.writer.add_summary(summary_str, counter) # Update G network _, summary_str = self.sess.run([g_optim, self.g_sum], feed_dict={ self.z: batch_z }) self.writer.add_summary(summary_str, counter) # Run g_optim twice to make sure that d_loss does not go to zero (different from paper) _, summary_str = self.sess.run([g_optim, self.g_sum], feed_dict={ self.z: batch_z }) self.writer.add_summary(summary_str, counter) errD_fake = self.d_loss_fake.eval({ self.z: batch_z }) errD_real = self.d_loss_real.eval({ self.inputs: batch_images }) errG = self.g_loss.eval({self.z: batch_z}) counter += 1 print("Epoch: [%2d] [%4d/%4d] time: %4.4f, d_loss: %.8f, g_loss: %.8f" \ % (epoch, idx, batch_idxs, time.time() - start_time, errD_fake+errD_real, errG)) if np.mod(counter, 100) == 1: #每迭代100次保存 if config.dataset == 'mnist': samples, d_loss, g_loss = self.sess.run( [self.sampler, self.d_loss, self.g_loss], feed_dict={ self.z: sample_z, self.inputs: sample_inputs, self.y:sample_labels, } ) save_images(samples, [8, 8], './{}/train_{:02d}_{:04d}.png'.format(config.sample_dir, epoch, idx)) print("[Sample] d_loss: %.8f, g_loss: %.8f" % (d_loss, g_loss)) else: try: samples, d_loss, g_loss = self.sess.run( [self.sampler, self.d_loss, self.g_loss], feed_dict={ self.z: sample_z, self.inputs: sample_inputs, }, ) save_images(samples, [8, 8], './{}/train_{:02d}_{:04d}.png'.format(config.sample_dir, epoch, idx)) print("[Sample] d_loss: %.8f, g_loss: %.8f" % (d_loss, g_loss)) except: print("one pic error!...") if np.mod(counter, 100) == 2: self.save(config.checkpoint_dir, counter) |
