Python采用keras训练GAN生成对抗网络输出手写数字代码-python代码-BFW代码库

Python采用keras训练GAN生成对抗网络输出手写数字代码

代码语言：python

代码描述：Python采用keras训练GAN生成对抗网络输出手写数字代码

代码标签： Python keras 训练 GAN 生成对抗网络输出手写数字代码

下面为部分代码预览，完整代码请点击下载或在bfwstudio webide中打开

#!/usr/local/python3/bin/python3
# -*- coding: utf-8 -*
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Dense, Dropout, Input
from tensorflow.keras.models import Model,Sequential
from tensorflow.python.keras.layers.advanced_activations import LeakyReLU
from tensorflow.keras.optimizers import Adam
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt

# from google.colab import drive
# drive.mount('/content/gdrive')
# path = 'gdrive/My Drive/Project/Practice/Result_GAN/'
#导入数据集
def load_data():
  (x_train, y_train), (_, _) = mnist.load_data()
  x_train = (x_train.astype(np.float32) - 127.5)/127.5
  x_train = x_train.reshape(60000, 784)
  return (x_train, y_train)

X_train, y_train = load_data()
print(X_train.shape, y_train.shape)


def build_generator():
    model = Sequential()
    
    model.add(Dense(units=256, input_dim=100))
    model.add(LeakyReLU(alpha=0.2))
    
    model.add(Dense(units=512))
    model.add(LeakyReLU(alpha=0.2))
    
    model.add(Dense(units=1024))
    model.add(LeakyReLU(alpha=0.2))
    
    model.add(Dense(units=784, activation='tanh'))
    
    model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return model

generator = build_generator()
generator.summary()

def build_discriminator():
    model = Sequential()
    
    model.add(Dense(units=1024 ,input_dim=784))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.3))
       
    model.add(Dense(units=512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.3))
       
    model.add(Dense(units=256))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.3))
      
    model.add(Dense(units=1, activation='sigmoid'))
    
    model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return model
  
discriminator = build_discriminator()
discriminator.summary()

def build_GAN(discriminator, generator):
    discriminator.trainable=False
    GAN_input = Input(shape=(100,))
    x = generator(GAN_input)
    GAN_output= discriminator(x)
    GAN = Model(inputs=GAN_input, outputs=GAN_output)
    GAN.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return GAN
    
#建立一个GAN网络，GAN由两个神经网络（generator, discriminator）连接而成。
GAN = build_GAN(discriminator, generator)
GAN.summary()

def draw_images(generator, epoch, examples=25, dim=(5,5), figsize=(10,10)):
    
    noise= np.random.normal(loc=0, scale=1, size=[examples, 100])
    generated_images = generator.predict(noise)
    generated_images = generated_images.reshape(25,28,28)
    plt.figure(figsize=figsize)
    for i in range(generated_images.shape[0]):
        plt.subplot(dim[0], dim[1], i+1)
        plt.imshow(generated_images[i], interpolation='nearest', cmap='Greys')
        plt.axis('off')
    plt.tight_layout()

    plt.savefig('/data/wwwroot/default/Data/image_at_epoch_%d.png' %epoch)
    
def train_GAN(epochs=1, batch_size=128):
    
  #Loading the data
  X_train, y_train = load_data()

  # Creating GAN
  generator= build_generator()
  discriminator= buil.........完整代码请登录后点击上方下载按钮下载查看