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regressgan.py
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regressgan.py
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# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
# TensorFlow and tf.keras
import tensorflow as tf
import os
from tensorflow.keras import layers
# Helper libraries
import imageio
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
from IPython import display
import PIL
import glob
from scipy import signal
InputLength = 4
Batch = 20
OutputLength = 1
"""######网络层"""
class Linear(layers.Layer):
'''
线性层
输入：
input: [Batch,length,channel] length为信号长度
units：输出维数
active: 激活函数： 'tanh'(默认), 'linear', 'sigmoid'
注意：
使用 self.add_weight的时候需要添加 name
输出：
线性函数 (units)
Author: Starhou
Email: [email protected]
Date: 2020.1.30
'''
def __init__(self, units = 1, active='linear'):
super(Linear, self).__init__()
self.units = units
self.active = active
self.usebais = True
if self.active == 'tanh':
self.activefun = tf.tanh
if self.active == 'sigmoid':
self.activefun = tf.sigmoid
if self.active == 'linear':
self.activefun = tf.keras.activations.linear
def get_config(self):
base_config = super(covT, self).get_config()
base_config['units'] = self.units
base_config['active'] = self.active
base_config['usebais'] = False
base_config['activefun'] = self.activefun
return base_config
def build(self, input_shape):
self.w = self.add_weight(name='w', shape=(input_shape[-2], self.units),
initializer= 'random_normal',
trainable=True)
if self.usebais:
self.b = self.add_weight(name='b', shape=(self.units,),
initializer= 'random_normal',
trainable=True)
super(Linear, self).build(input_shape)
def call(self, inputs):
if self.usebais:
out = tf.matmul(inputs[:,:,0], self.w) + self.b
else:
out = tf.matmul(inputs[:,:,0], self.w)
out = self.activefun(out)
out = tf.expand_dims(out,-1)
return out
"""######生成器"""
class Generator(tf.keras.Model):
def __init__(self, InputLength=1, Batch=Batch):
super(Generator, self).__init__()
self.start = layers.Dense(units=1,dtype='float32')
self.linear = Linear(units=1,active='tanh')
self.active = layers.LeakyReLU(alpha=0.2)
self.inference_net = tf.keras.Sequential(
[
self.active,
self.linear,
]
)
def call(self, inputs):
x = self.start(inputs)
x = self.inference_net(inputs)
return x
generator=Generator()
"""######判别器"""
class Discriminator(tf.keras.Model):
def __init__(self, InputLength=InputLength+1, Batch=Batch):
super(Discriminator, self).__init__()
self.start = layers.Conv1D(filters = 5,kernel_size = 1, padding = 'causal',input_shape=(InputLength,1))
self.active = layers.LeakyReLU(alpha=0.2)
self.out = layers.Dense(1, activation='tanh',dtype='float32')
self.inference_net = tf.keras.Sequential(
[
self.out,
]
)
def call(self, inputs):
x = self.inference_net(inputs)
return x
discriminator = Discriminator()
"""######测试运行
4个已知数据 ---> 生成器 ---> 1个所求数据
[4个已知数据，1个所求数据(真实) ]---> 判别器
[4个已知数据，1个所求数据(生成) ]---> 判别器
"""
# #测试运行 通过
noise = tf.random.normal([1,4,1])
generateECG = generator(noise)
print(generateECG.shape)
yp = tf.concat((noise,generateECG),1)
yp = discriminator(yp)
print(yp.shape)
"""######定义损失函数和优化器"""
# 定义源损失 交叉熵
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=False)
# 判别损失 判别器要做两件事情，既要真的趋近于-1，又要假的趋近于1
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(-0.5*tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(0.5*tf.ones_like(fake_output), fake_output)
total_loss = real_loss+fake_loss
return total_loss
# 生成损失 生成器使得假的趋近于-1
def generator_loss(fake_output):
fake_loss = cross_entropy(-0.5*tf.ones_like(fake_output), fake_output)
return fake_loss
generator_optimizer = tf.keras.optimizers.Adam(lr=0.05, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
discriminator_optimizer = tf.keras.optimizers.Adam(lr=0.05, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
discriminator=discriminator)
"""######定义训练"""
# 单步训练
# 注意 `tf.function` 的使用
# 该注解使函数被“编译”
@tf.function
def train_step(ECG):
for i in range(5):
## 生成数据
generatorInput = ECG[:,:4,:]
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_ECG = generator(generatorInput, training=True)
generated_ECG = tf.concat((generatorInput,generated_ECG),1)
real_output = discriminator(ECG, training=True)
fake_output = discriminator(generated_ECG, training=True)
gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(real_output, fake_output)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
if i==4:
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
return gen_loss,disc_loss,
# 定义训练
def train(dataset, epochs):
for epoch in range(epochs):
start = time.time()
num = 0
for image_batch in dataset:
gen_loss,disc_loss = train_step(image_batch)
num = num+1
if num%10==0:
print ('generator loss {} discriminator loss {} sec'.format(gen_loss, disc_loss))
# 继续进行时为 GIF 生成图像
# display.clear_output(wait=True)
generate_and_save_images(generator,
epoch + 1,
seed)
# 每 15 个 epoch 保存一次模型
if (epoch + 1) % 15 == 0:
checkpoint.save(file_prefix = checkpoint_prefix)
print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))
# 最后一个 epoch 结束后生成图片
display.clear_output(wait=True)
generate_and_save_images(generator,
epochs,
seed)
def generate_and_save_images(model, epoch, test_input):
predictions = model(test_input, training=False)
print(predictions.numpy()[-1])
"""######加载数据"""
csv_data = pd.read_csv("data/new/data13.csv", header=None)
data = np.array(csv_data)
traindata = data[1:145,:5]
testdata = data[145:433,:5]
# testdata[:,-1]=0
traindata = np.expand_dims(traindata,2)
traindata = traindata.astype(np.float32)
testdata = np.expand_dims(testdata,2)
testdata = testdata.astype(np.float32)
train_dataset = tf.data.Dataset.from_tensor_slices(traindata).shuffle(60000).batch(Batch)
# 测试数据
seed = testdata[0:5,:4,:]
"""######训练保存"""
train(train_dataset, 50)
"""#####恢复模型"""
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))