TensorFlow2_200729系列---27、gan对抗神经网络
一、总结
一句话总结：
A、gan对抗神经网络主要也是用到的卷积神经网络，就是一个生成器（卷积神经网络做的生成器）和一个分类器（卷积神经网络做的分类器），
B、比如生成器是在卷积神经网络的基础上小小改动了一下，分类器自然也是，因为有g和d，所以计算loss的时候是计算两个loss，代码也还是非常简单的
import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers class Generator(keras.Model): def __init__(self): super(Generator, self).__init__() # z: [b, 100] => [b, 3*3*512] => [b, 3, 3, 512] => [b, 64, 64, 3] self.fc = layers.Dense(3*3*512) self.conv1 = layers.Conv2DTranspose(256, 3, 3, 'valid') self.bn1 = layers.BatchNormalization() self.conv2 = layers.Conv2DTranspose(128, 5, 2, 'valid') self.bn2 = layers.BatchNormalization() self.conv3 = layers.Conv2DTranspose(3, 4, 3, 'valid') def call(self, inputs, training=None): # [z, 100] => [z, 3*3*512] x = self.fc(inputs) x = tf.reshape(x, [-1, 3, 3, 512]) x = tf.nn.leaky_relu(x) # x = tf.nn.leaky_relu(self.bn1(self.conv1(x), training=training)) x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training)) x = self.conv3(x) x = tf.tanh(x) return x class Discriminator(keras.Model): def __init__(self): super(Discriminator, self).__init__() # [b, 64, 64, 3] => [b, 1] self.conv1 = layers.Conv2D(64, 5, 3, 'valid') self.conv2 = layers.Conv2D(128, 5, 3, 'valid') self.bn2 = layers.BatchNormalization() self.conv3 = layers.Conv2D(256, 5, 3, 'valid') self.bn3 = layers.BatchNormalization() # [b, h, w ,c] => [b, -1] self.flatten = layers.Flatten() self.fc = layers.Dense(1) def call(self, inputs, training=None): x = tf.nn.leaky_relu(self.conv1(inputs)) x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training)) x = tf.nn.leaky_relu(self.bn3(self.conv3(x), training=training)) # [b, h, w, c] => [b, -1] x = self.flatten(x) # [b, -1] => [b, 1] logits = self.fc(x) return logits def main(): d = Discriminator() g = Generator() x = tf.random.normal([2, 64, 64, 3]) z = tf.random.normal([2, 100]) prob = d(x) print(prob) x_hat = g(z) print(x_hat.shape) if __name__ == '__main__': main()
1、神经网络中设计随机种子的好处是什么，比如tf.random.set_seed(22)？
方便神经网络复现，比如某次某一个随机种子下的参数特别好，设置了随机种子一般就可以复现
2、如下代码中r的作用是什么？
|||-begin
imgpath=glob. glob(r'C: Users Jackie Loong Downloads DCGe')
|||-end
window中反斜杠有专门的用处，加上r，就能正常表示路径了
二、gan对抗神经网络
博客对应课程的视频位置：
gan.py
import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers class Generator(keras.Model): def __init__(self): super(Generator, self).__init__() # z: [b, 100] => [b, 3*3*512] => [b, 3, 3, 512] => [b, 64, 64, 3] self.fc = layers.Dense(3*3*512) self.conv1 = layers.Conv2DTranspose(256, 3, 3, 'valid') self.bn1 = layers.BatchNormalization() self.conv2 = layers.Conv2DTranspose(128, 5, 2, 'valid') self.bn2 = layers.BatchNormalization() self.conv3 = layers.Conv2DTranspose(3, 4, 3, 'valid') def call(self, inputs, training=None): # [z, 100] => [z, 3*3*512] x = self.fc(inputs) x = tf.reshape(x, [-1, 3, 3, 512]) x = tf.nn.leaky_relu(x) # x = tf.nn.leaky_relu(self.bn1(self.conv1(x), training=training)) x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training)) x = self.conv3(x) x = tf.tanh(x) return x class Discriminator(keras.Model): def __init__(self): super(Discriminator, self).__init__() # [b, 64, 64, 3] => [b, 1] self.conv1 = layers.Conv2D(64, 5, 3, 'valid') self.conv2 = layers.Conv2D(128, 5, 3, 'valid') self.bn2 = layers.BatchNormalization() self.conv3 = layers.Conv2D(256, 5, 3, 'valid') self.bn3 = layers.BatchNormalization() # [b, h, w ,c] => [b, -1] self.flatten = layers.Flatten() self.fc = layers.Dense(1) def call(self, inputs, training=None): x = tf.nn.leaky_relu(self.conv1(inputs)) x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training)) x = tf.nn.leaky_relu(self.bn3(self.conv3(x), training=training)) # [b, h, w, c] => [b, -1] x = self.flatten(x) # [b, -1] => [b, 1] logits = self.fc(x) return logits def main(): d = Discriminator() g = Generator() x = tf.random.normal([2, 64, 64, 3]) z = tf.random.normal([2, 100]) prob = d(x) print(prob) x_hat = g(z) print(x_hat.shape) if __name__ == '__main__': main()
gan_train.py
import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import numpy as np import tensorflow as tf from tensorflow import keras from PIL import Image import glob from gan import Generator, Discriminator from dataset import make_anime_dataset def save_result(val_out, val_block_size, image_path, color_mode): def preprocess(img): img = ((img + 1.0) * 127.5).astype(np.uint8) # img = img.astype(np.uint8) return img preprocesed = preprocess(val_out) final_image = np.array([]) single_row = np.array([]) for b in range(val_out.shape[0]): # concat image into a row if single_row.size == 0: single_row = preprocesed[b, :, :, :] else: single_row = np.concatenate((single_row, preprocesed[b, :, :, :]), axis=1) # concat image row to final_image if (b+1) % val_block_size == 0: if final_image.size == 0: final_image = single_row else: final_image = np.concatenate((final_image, single_row), axis=0) # reset single row single_row = np.array([]) if final_image.shape[2] == 1: final_image = np.squeeze(final_image, axis=2) Image.fromarray(final_image).save(image_path) def celoss_ones(logits): # [b, 1] # [b] = [1, 1, 1, 1,] loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=tf.ones_like(logits)) return tf.reduce_mean(loss) def celoss_zeros(logits): # [b, 1] # [b] = [1, 1, 1, 1,] loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=tf.zeros_like(logits)) return tf.reduce_mean(loss) def d_loss_fn(generator, discriminator, batch_z, batch_x, is_training): # 1. treat real image as real # 2. treat generated image as fake fake_image = generator(batch_z, is_training) d_fake_logits = discriminator(fake_image, is_training) d_real_logits = discriminator(batch_x, is_training) d_loss_real = celoss_ones(d_real_logits) d_loss_fake = celoss_zeros(d_fake_logits) loss = d_loss_fake + d_loss_real return loss def g_loss_fn(generator, discriminator, batch_z, is_training): fake_image = generator(batch_z, is_training) d_fake_logits = discriminator(fake_image, is_training) loss = celoss_ones(d_fake_logits) return loss def main(): tf.random.set_seed(22) np.random.seed(22) assert tf.__version__.startswith('2.') # hyper parameters z_dim = 100 epochs = 3000000 batch_size = 512 learning_rate = 0.002 is_training = True img_path = glob.glob(r'C:UsersJackieDownloadsfaces*.jpg') assert len(img_path) > 0 dataset, img_shape, _ = make_anime_dataset(img_path, batch_size) print(dataset, img_shape) sample = next(iter(dataset)) print(sample.shape, tf.reduce_max(sample).numpy(), tf.reduce_min(sample).numpy()) dataset = dataset.repeat() db_iter = iter(dataset) generator = Generator() generator.build(input_shape = (None, z_dim)) discriminator = Discriminator() discriminator.build(input_shape=(None, 64, 64, 3)) g_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5) d_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5) for epoch in range(epochs): batch_z = tf.random.normal([batch_size, z_dim]) batch_x = next(db_iter) # train D with tf.GradientTape() as tape: d_loss = d_loss_fn(generator, discriminator, batch_z, batch_x, is_training) grads = tape.gradient(d_loss, discriminator.trainable_variables) d_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables)) batch_z = tf.random.normal([batch_size, z_dim]) with tf.GradientTape() as tape: g_loss = g_loss_fn(generator, discriminator, batch_z, is_training) grads = tape.gradient(g_loss, generator.trainable_variables) g_optimizer.apply_gradients(zip(grads, generator.trainable_variables)) if epoch % 100 == 0: print(epoch, 'd-loss:',float(d_loss), 'g-loss:', float(g_loss)) z = tf.random.normal([100, z_dim]) fake_image = generator(z, training=False) img_path = os.path.join('images', 'gan-%d.png'%epoch) save_result(fake_image.numpy(), 10, img_path, color_mode='P') if __name__ == '__main__': main()
dataset.py
import multiprocessing import tensorflow as tf def make_anime_dataset(img_paths, batch_size, resize=64, drop_remainder=True, shuffle=True, repeat=1): @tf.function def _map_fn(img): img = tf.image.resize(img, [resize, resize]) img = tf.clip_by_value(img, 0, 255) img = img / 127.5 - 1 return img dataset = disk_image_batch_dataset(img_paths, batch_size, drop_remainder=drop_remainder, map_fn=_map_fn, shuffle=shuffle, repeat=repeat) img_shape = (resize, resize, 3) len_dataset = len(img_paths) // batch_size return dataset, img_shape, len_dataset def batch_dataset(dataset, batch_size, drop_remainder=True, n_prefetch_batch=1, filter_fn=None, map_fn=None, n_map_threads=None, filter_after_map=False, shuffle=True, shuffle_buffer_size=None, repeat=None): # set defaults if n_map_threads is None: n_map_threads = multiprocessing.cpu_count() if shuffle and shuffle_buffer_size is None: shuffle_buffer_size = max(batch_size * 128, 2048) # set the minimum buffer size as 2048 # [*] it is efficient to conduct `shuffle` before `map`/`filter` because `map`/`filter` is sometimes costly if shuffle: dataset = dataset.shuffle(shuffle_buffer_size) if not filter_after_map: if filter_fn: dataset = dataset.filter(filter_fn) if map_fn: dataset = dataset.map(map_fn, num_parallel_calls=n_map_threads) else: # [*] this is slower if map_fn: dataset = dataset.map(map_fn, num_parallel_calls=n_map_threads) if filter_fn: dataset = dataset.filter(filter_fn) dataset = dataset.batch(batch_size, drop_remainder=drop_remainder) dataset = dataset.repeat(repeat).prefetch(n_prefetch_batch) return dataset def memory_data_batch_dataset(memory_data, batch_size, drop_remainder=True, n_prefetch_batch=1, filter_fn=None, map_fn=None, n_map_threads=None, filter_after_map=False, shuffle=True, shuffle_buffer_size=None, repeat=None): """Batch dataset of memory data. Parameters ---------- memory_data : nested structure of tensors/ndarrays/lists """ dataset = tf.data.Dataset.from_tensor_slices(memory_data) dataset = batch_dataset(dataset, batch_size, drop_remainder=drop_remainder, n_prefetch_batch=n_prefetch_batch, filter_fn=filter_fn, map_fn=map_fn, n_map_threads=n_map_threads, filter_after_map=filter_after_map, shuffle=shuffle, shuffle_buffer_size=shuffle_buffer_size, repeat=repeat) return dataset def disk_image_batch_dataset(img_paths, batch_size, labels=None, drop_remainder=True, n_prefetch_batch=1, filter_fn=None, map_fn=None, n_map_threads=None, filter_after_map=False, shuffle=True, shuffle_buffer_size=None, repeat=None): """Batch dataset of disk image for PNG and JPEG. Parameters ---------- img_paths : 1d-tensor/ndarray/list of str labels : nested structure of tensors/ndarrays/lists """ if labels is None: memory_data = img_paths else: memory_data = (img_paths, labels) def parse_fn(path, *label): img = tf.io.read_file(path) img = tf.image.decode_png(img, 3) # fix channels to 3 return (img,) + label if map_fn: # fuse `map_fn` and `parse_fn` def map_fn_(*args): return map_fn(*parse_fn(*args)) else: map_fn_ = parse_fn dataset = memory_data_batch_dataset(memory_data, batch_size, drop_remainder=drop_remainder, n_prefetch_batch=n_prefetch_batch, filter_fn=filter_fn, map_fn=map_fn_, n_map_threads=n_map_threads, filter_after_map=filter_after_map, shuffle=shuffle, shuffle_buffer_size=shuffle_buffer_size, repeat=repeat) return dataset