网页设计与网站建设哪里可以检测胎儿性别
最近对对抗生成网络GAN比较感兴趣，相关知识点文章还在编辑中，以下这个是一个练手的小项目~
(在原模型上做了，为了减少计算量让其好训练一些。)
一、导入工具包
import tensorflow as tf
from tensorflow.keras import layersimport numpy as np
import os
import time
import glob
import matplotlib.pyplot as plt
from IPython.display import clear_output
from IPython import display1.1 设置GPU
gpus = tf.config.list_physical_devices("GPU")if gpus:gpu0 = gpus[0] #如果有多个GPU，仅使用第0个GPUtf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用tf.config.set_visible_devices([gpu0],"GPU")
gpus [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
二、导入训练数据
链接： 点这里
fileList = glob.glob('./ani_face/*.jpg')
len(fileList)41621
2.1 数据可视化
# 随机显示几张图
for index,i in enumerate(fileList[:3]):display.display(display.Image(fileList[index]))
2.2 数据预处理
# 文件名列表
path_ds = tf.data.Dataset.from_tensor_slices(fileList)# 预处理，归一化，缩放
def load_and_preprocess_image(path):image = tf.io.read_file(path)image = tf.image.decode_jpeg(image, channels=3)image = tf.image.resize(image, [64, 64])image /= 255.0 # normalize to [0,1] rangeimage = tf.reshape(image, [1, 64,64,3])return imageimage_ds = path_ds.map(load_and_preprocess_image)
image_ds<MapDataset shapes: (1, 64, 64, 3), types: tf.float32>
# 查看一张图片
for x in image_ds:plt.axis("off")plt.imshow((x.numpy() * 255).astype("int32")[0])break
三、网络构建
3.1 D网络
discriminator = keras.Sequential([keras.Input(shape=(64, 64, 3)),layers.Conv2D(64, kernel_size=4, strides=2, padding="same"),layers.LeakyReLU(alpha=0.2),layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),layers.LeakyReLU(alpha=0.2),layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),layers.LeakyReLU(alpha=0.2),layers.Flatten(),layers.Dropout(0.2),layers.Dense(1, activation="sigmoid"),],name="discriminator",
)
discriminator.summary()Model: "discriminator" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 32, 32, 64) 3136 _________________________________________________________________ leaky_re_lu (LeakyReLU) (None, 32, 32, 64) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 16, 16, 128) 131200 _________________________________________________________________ leaky_re_lu_1 (LeakyReLU) (None, 16, 16, 128) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 8, 8, 128) 262272 _________________________________________________________________ leaky_re_lu_2 (LeakyReLU) (None, 8, 8, 128) 0 _________________________________________________________________ flatten (Flatten) (None, 8192) 0 _________________________________________________________________ dropout (Dropout) (None, 8192) 0 _________________________________________________________________ dense (Dense) (None, 1) 8193 ================================================================= Total params: 404,801 Trainable params: 404,801 Non-trainable params: 0
3.2 G网络
latent_dim = 128generator = keras.Sequential([keras.Input(shape=(latent_dim,)),layers.Dense(8 * 8 * 128),layers.Reshape((8, 8, 128)),layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding="same"),layers.LeakyReLU(alpha=0.2),layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding="same"),layers.LeakyReLU(alpha=0.2),layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding="same"),layers.LeakyReLU(alpha=0.2),layers.Conv2D(3, kernel_size=5, padding="same", activation="sigmoid"),],name="generator",
)
generator.summary()3.3 重写 train_step
class GAN(keras.Model):def __init__(self, discriminator, generator, latent_dim):super(GAN, self).__init__()self.discriminator = discriminatorself.generator = generatorself.latent_dim = latent_dimdef compile(self, d_optimizer, g_optimizer, loss_fn):super(GAN, self).compile()self.d_optimizer = d_optimizerself.g_optimizer = g_optimizerself.loss_fn = loss_fnself.d_loss_metric = keras.metrics.Mean(name="d_loss")self.g_loss_metric = keras.metrics.Mean(name="g_loss")@propertydef metrics(self):return [self.d_loss_metric, self.g_loss_metric]def train_step(self, real_images):# 生成噪音batch_size = tf.shape(real_images)[0]random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))# 生成的图片generated_images = self.generator(random_latent_vectors)# Combine them with real imagescombined_images = tf.concat([generated_images, real_images], axis=0)# Assemble labels discriminating real from fake imageslabels = tf.concat([tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0)# Add random noise to the labels - important trick!labels += 0.05 * tf.random.uniform(tf.shape(labels))# 训练判别器，生成的当成0，真实的当成1 with tf.GradientTape() as tape:predictions = self.discriminator(combined_images)d_loss = self.loss_fn(labels, predictions)grads = tape.gradient(d_loss, self.discriminator.trainable_weights)self.d_optimizer.apply_gradients(zip(grads, self.discriminator.trainable_weights))# Sample random points in the latent spacerandom_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))# Assemble labels that say "all real images"misleading_labels = tf.zeros((batch_size, 1))# Train the generator (note that we should *not* update the weights# of the discriminator)!with tf.GradientTape() as tape:predictions = self.discriminator(self.generator(random_latent_vectors))g_loss = self.loss_fn(misleading_labels, predictions)grads = tape.gradient(g_loss, self.generator.trainable_weights)self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))# Update metricsself.d_loss_metric.update_state(d_loss)self.g_loss_metric.update_state(g_loss)return {"d_loss": self.d_loss_metric.result(),"g_loss": self.g_loss_metric.result(),}3.4 设置回调函数
class GANMonitor(keras.callbacks.Callback):def __init__(self, num_img=3, latent_dim=128):self.num_img = num_imgself.latent_dim = latent_dimdef on_epoch_end(self, epoch, logs=None):random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))generated_images = self.model.generator(random_latent_vectors)generated_images *= 255generated_images.numpy()for i in range(self.num_img):img = keras.preprocessing.image.array_to_img(generated_images[i])display.display(img)img.save("gen_ani/generated_img_%03d_%d.png" % (epoch, i))四、训练模型
epochs = 100 # In practice, use ~100 epochsgan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)
gan.compile(d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),loss_fn=keras.losses.BinaryCrossentropy(),
)gan.fit(image_ds, epochs=epochs, callbacks=[GANMonitor(num_img=10, latent_dim=latent_dim)]
)五、保存模型
#保存模型
gan.generator.save('./data/ani_G_model')生成模型文件：点这里
六、生成漫画脸
G_model = tf.keras.models.load_model('./data/ani_G_model/',compile=False)def randomGenerate():noise_seed = tf.random.normal([16, 128])predictions = G_model(noise_seed, training=False)fig = plt.figure(figsize=(8, 8))for i in range(predictions.shape[0]):plt.subplot(4, 4, i+1)img = (predictions[i].numpy() * 255 ).astype('int')plt.imshow(img )plt.axis('off')plt.show()count = 0
while True:randomGenerate()clear_output(wait=True)time.sleep(0.1)if count > 100:breakcount+=1