keras实现DCGAN生成mnist原代码

算法原理

具体算法原理，在这里不再阐述，可以参考：

https://blog.csdn.net/stdcoutzyx/article/details/53872121
https://ask.julyedu.com/question/7681

基于keras的原代码

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Reshape
from keras.layers.core import Activation
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import UpSampling2D
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.core import Flatten
from keras.optimizers import SGD
from keras.datasets import mnist
import numpy as np
from PIL import Image
import argparse
import mathdef generator_model():model = Sequential()model.add(Dense(input_dim=100, output_dim=1024))model.add(Activation('tanh'))model.add(Dense(128*7*7))model.add(BatchNormalization())model.add(Activation('tanh'))model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))model.add(UpSampling2D(size=(2, 2)))model.add(Conv2D(64, (5, 5), padding='same'))model.add(Activation('tanh'))model.add(UpSampling2D(size=(2, 2)))model.add(Conv2D(1, (5, 5), padding='same'))model.add(Activation('tanh'))return modeldef discriminator_model():model = Sequential()model.add(Conv2D(64, (5, 5),padding='same',input_shape=(28, 28, 1)))model.add(Activation('tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Conv2D(128, (5, 5)))model.add(Activation('tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Flatten())model.add(Dense(1024))model.add(Activation('tanh'))model.add(Dense(1))model.add(Activation('sigmoid'))return modeldef generator_containing_discriminator(g, d):model = Sequential()model.add(g)d.trainable = Falsemodel.add(d)return modeldef combine_images(generated_images):num = generated_images.shape[0]width = int(math.sqrt(num))height = int(math.ceil(float(num)/width))shape = generated_images.shape[1:3]image = np.zeros((height*shape[0], width*shape[1]),dtype=generated_images.dtype)for index, img in enumerate(generated_images):i = int(index/width)j = index % widthimage[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \img[:, :, 0]return imagedef train(BATCH_SIZE):(X_train, y_train), (X_test, y_test) = mnist.load_data()X_train = (X_train.astype(np.float32) - 127.5)/127.5X_train = X_train[:, :, :, None]X_test = X_test[:, :, :, None]# X_train = X_train.reshape((X_train.shape, 1) + X_train.shape[1:])d = discriminator_model()g = generator_model()d_on_g = generator_containing_discriminator(g, d)#使用判断器来监督生成器的训练d_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)g_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)g.compile(loss='binary_crossentropy', optimizer="SGD")#只有生成器d_on_g.compile(loss='binary_crossentropy', optimizer=g_optim)#编译生成器d.trainable = Trued.compile(loss='binary_crossentropy', optimizer=d_optim)#只有判断器for epoch in range(100):print("Epoch is", epoch)print("Number of batches", int(X_train.shape[0]/BATCH_SIZE))for index in range(int(X_train.shape[0]/BATCH_SIZE)):#每轮有多少个step，数据/batch_sizenoise = np.random.uniform(-1, 1, size=(BATCH_SIZE, 100))#生成100维度的噪声image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]generated_images = g.predict(noise, verbose=0)#生成器生成的图像if index % 20 == 0:image = combine_images(generated_images)#这个是把20张图像拼接起来，组成一个大图像image = image*127.5+127.5#从-1~1还原成0~255Image.fromarray(image.astype(np.uint8)).save(str(epoch)+"_"+str(index)+".png")#保存图像X = np.concatenate((image_batch, generated_images))y = [1] * BATCH_SIZE + [0] * BATCH_SIZEd_loss = d.train_on_batch(X, y)#训练判断器print("batch %d d_loss : %f" % (index, d_loss))noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))d.trainable = False#把判断器冻结g_loss = d_on_g.train_on_batch(noise, [1] * BATCH_SIZE)#固定判断器参数，训练生成器d.trainable = Trueprint("batch %d g_loss : %f" % (index, g_loss))if index % 10 == 9:#每更新十次，保存模型g.save_weights('generator', True)d.save_weights('discriminator', True)#加载训练好的模型进行预测
def generate(BATCH_SIZE, nice=False):g = generator_model()g.compile(loss='binary_crossentropy', optimizer="SGD")g.load_weights('generator')#生成比较好的模型if nice:d = discriminator_model()d.compile(loss='binary_crossentropy', optimizer="SGD")d.load_weights('discriminator')noise = np.random.uniform(-1, 1, (BATCH_SIZE*20, 100))generated_images = g.predict(noise, verbose=1)d_pret = d.predict(generated_images, verbose=1)index = np.arange(0, BATCH_SIZE*20)index.resize((BATCH_SIZE*20, 1))pre_with_index = list(np.append(d_pret, index, axis=1))pre_with_index.sort(key=lambda x: x[0], reverse=True)nice_images = np.zeros((BATCH_SIZE,) + generated_images.shape[1:3], dtype=np.float32)nice_images = nice_images[:, :, :, None]for i in range(BATCH_SIZE):idx = int(pre_with_index[i][1])nice_images[i, :, :, 0] = generated_images[idx, :, :, 0]image = combine_images(nice_images)#生成一般的模型else:noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))generated_images = g.predict(noise, verbose=1)image = combine_images(generated_images)image = image*127.5+127.5Image.fromarray(image.astype(np.uint8)).save("generated_image.png")def get_args():parser = argparse.ArgumentParser()parser.add_argument("--mode", type=str,default='train')parser.add_argument("--batch_size", type=int, default=128)parser.add_argument("--nice", dest="nice", action="store_true")parser.set_defaults(nice=False)args = parser.parse_args()return argsif __name__ == "__main__":args = get_args()if args.mode == "train":train(BATCH_SIZE=args.batch_size)elif args.mode == "generate":generate(BATCH_SIZE=args.batch_size, nice=args.nice)

实验结果

迭代的次数还不够，生成的效果不算太好，若有兴趣，可以多增加迭代次数，会生成更好的图像。

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keras实现DCGAN生成mnist原代码

算法原理

基于keras的原代码

实验结果

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