深度学习课程项目_深度学习项目

多元线性回归的随机梯度方法

代码

import numpy as np
w=np.array([5,3,4]).transpose()
b=np.array([100])
def compute_error_for_given_points(b,w,points):
    totalError = 0
    for i in range(0, len(points)):
        x = points[i].x
        y = points[i].y
        totalError += (y-(np.dot(w,x)+b)) ** 2
    return totalError / float(len(points))
def step_gradient(b_current, w_current, points, learningRate):
    b_gradient = 0
    w_gradient = np.zeros((len(w_current)))
    N = float(len(points))
    for i in range(0, len(points)):
        x = points[i].x
        y = points[i].y
        residual = np.dot(w_current,x)+b_current - y
        b_gradient += (2) * residual
        w_gradient += (2/N) * residual * x
    new_b = b_current - (learningRate * b_gradient)
    new_w = w_current - (learningRate * w_gradient)
    return [new_b, new_w]
def gradient_descent_runner(starting_b, starting_w, points, learning_rate, num_iterations):
    b = starting_b
    w = starting_w
    percent_old = 0
    for i in range(num_iterations):
        percent = i/num_iterations
        if percent-percent_old > 0.01:
            percent_old = percent
            print("\r进度为{:.2f}%".format(percent*100), end="")
        b, w = step_gradient(b, w, points, learning_rate)
    print("\n")
    return [b, w]
class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y
x = np.random.random((1000,3))*10+10
y = b + np.matmul(x,w) + np.random.normal(0, 1, 1000)
# points = tf.data.Dataset.from_tensor_slices((x,y))
points = np.ndarray((1000), dtype=Point)
for i in range(1000):
    points[i] = Point(x[i], y[i])
loss = compute_error_for_line_given_points(0, np.array([0,0,0]), points)
print(f"loss before: {loss}")
b, w = gradient_descent_runner(0, np.array([0,0,0]), points, 0.0001, 5000)
loss = compute_error_for_line_given_points(b, w, points)
print(f"loss after: {loss}")
print(f"{b} + {w}*x")
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效果

原理

实值函数线性模型

$损失函数为MSE时，L(\mathbf{w})=\frac{1}{n}{\Sigma }_{i=1}^n(\hat{f}({\mathbf{x}}^{(i)})-y^{(i)}{)}^2。当模型为\hat{f}(\mathbf{x})=\hat{y}=b+{\Sigma }_{i=1}^d{w}_i{x}_i时，\frac{\partial L(\mathbf{w})}{\partial b}=\frac{2}{n}{\Sigma }_{i=1}^n({\hat{y}}^{(i)}-y^{(i)}),\frac{\partial L(\mathbf{w})}{\partial w_j}=\frac{2}{n}{\Sigma }_{i=1}^n({\hat{y}}^{(i)}-y^{(i)})x_j^{(i)}。由对向量求导的定义得：\frac{\partial L(\mathbf{w})}{\partial \mathbf{w}}=\frac{2}{n}{\Sigma }_{i=1}^n({\hat{y}}^{(i)}-y^{(i)}){\mathbf{x}}^{(i)}。将b视为w_0的增广形式结果类似，只需x_0固定为1.$

实值函数线性基函数模型 linear basis function model

$当模型为\hat{f}(\mathbf{x})=\hat{y}={\Sigma }_{i=0}^m{w}_i{\varphi }_i(\mathbf{x})时,$
$其中{\varphi }_i(\mathbf{x})为d元实值函数，可退化为Polynomial:x_i^i,Gussian:e^{\frac{(x_i-{\mu }_i{)}^2}{2{\sigma }_i^2}},Sigmoidal:\frac{1}{1+e^{\frac{x_i-{\mu }_i}{{\sigma }_i}}}.并保持{\varphi }_0(\mathbf{x})\equiv 1$
$上述求导结果变为\frac{\partial L(\mathbf{w})}{\partial \mathbf{w}}=\frac{2}{n}{\Sigma }_{i=1}^n({\hat{y}}^{(i)}-y^{(i)})\mathbf{\varphi }({\mathbf{x}}^{(i)})$

实值函数线性基函数模型求导的矩阵形式解 derivation

$在求解\frac{\partial L(\mathbf{w})}{\partial \mathbf{w}}=0时需要将上式中的\mathbf{w}分离.即对n个向量求和为0向量问题需转化为矩阵问题$
${\Sigma }_{i=1}^n({\hat{y}}^{(i)}-y^{(i)})\mathbf{\varphi }({\mathbf{x}}^{(i)})$
$={\Sigma }_{i=1}^n\mathbf{\varphi }({\mathbf{x}}^{(i)})({\hat{y}}^{(i)}-y^{(i)})$
$=(\mathbf{\varphi }({\mathbf{x}}^{(1)}),\mathbf{\varphi }({\mathbf{x}}^{(2)},)\cdots ,\mathbf{\varphi }({\mathbf{x}}^{(n)})\cdot ({\hat{y}}^{(1)}-y^{(1)},{\hat{y}}^{(2)}-y^{(2)},\cdots ,{\hat{y}}^{(n)}-y^{(n)}{)}^{\prime }$
= ( ϕ ( x ( 1 ) ) , ϕ ( x ( 2 ) ) , ⋯   , ϕ ( x ( n ) ) ⋅ ( w ′ ϕ ( x ( 1 ) ) − y ( 1 ) , w ′ ϕ ( x ( 2 ) ) − y ( 2 ) , ⋯   , w ′ ϕ ( x ( n ) ) − y ( n ) ) =(\boldsymbol \phi(\boldsymbol x^{(1)}),\boldsymbol \phi(\boldsymbol x^{(2)}),\cdots,\boldsymbol \phi(\boldsymbol x^{(n)})\cdot \left( %左括号

$$\begin{array}{c} %该矩阵一共3列，每一列都居中放置 \boldsymbol w' \boldsymbol \phi(\boldsymbol x^{(1)})- y^{(1)},\\ \boldsymbol w' \boldsymbol \phi(\boldsymbol x^{(2)})- y^{(2)},\\ \cdots,\\ \boldsymbol w' \boldsymbol \phi(\boldsymbol x^{(n)})- y^{(n)} \end{array}$$ \right) =(ϕ(x(1)),ϕ(x(2)),⋯,ϕ(x(n))⋅ ​w′ϕ(x(1))−y(1),w′ϕ(x(2))−y(2),⋯,w′ϕ(x(n))−y(n)​ ​
$=(\mathbf{\varphi }({\mathbf{x}}^{(1)}),\mathbf{\varphi }({\mathbf{x}}^{(2)}),\cdots ,\mathbf{\varphi }({\mathbf{x}}^{(n)})\cdot \left(\begin{array}{c}\mathbf{\varphi }({\mathbf{x}}^{(1)}{)}^{\prime }\mathbf{w}-y^{(1)},\\ \mathbf{\varphi }({\mathbf{x}}^{(2)}{)}^{\prime }\mathbf{w}-y^{(2)},\\ \cdots ,\\ \mathbf{\varphi }({\mathbf{x}}^{(n)}{)}^{\prime }\mathbf{w}-y^{(n)}\end{array}\right)$
$=(\mathbf{\varphi }({\mathbf{x}}^{(1)}),\mathbf{\varphi }({\mathbf{x}}^{(2)}),\cdots ,\mathbf{\varphi }({\mathbf{x}}^{(n)})\cdot (\left(\begin{array}{c}\mathbf{\varphi }({\mathbf{x}}^{(1)}{)}^{\prime },\\ \mathbf{\varphi }({\mathbf{x}}^{(2)}{)}^{\prime },\\ \cdots ,\\ \mathbf{\varphi }({\mathbf{x}}^{(n)}{)}^{\prime }\end{array}\right)\mathbf{w}-\mathbf{y})$
$令\mathbf{\Phi }=(\mathbf{\varphi }({\mathbf{x}}^{(1)}),\mathbf{\varphi }({\mathbf{x}}^{(2)}),\cdots ,\mathbf{\varphi }({\mathbf{x}}^{(n)})=\left(\begin{array}{c}{\varphi }_0({\mathbf{x}}^{(1)}),{\varphi }_0({\mathbf{x}}^{(2)}),\cdots ,{\varphi }_0({\mathbf{x}}^{(n)})\\ {\varphi }_1({\mathbf{x}}^{(1)}),{\varphi }_1({\mathbf{x}}^{(2)}),\cdots ,{\varphi }_1({\mathbf{x}}^{(n)})\\ \cdots \\ {\varphi }_m({\mathbf{x}}^{(1)}),{\varphi }_m({\mathbf{x}}^{(2)}),\cdots ,{\varphi }_m({\mathbf{x}}^{(n)})\end{array}\right)$
$\frac{\partial L(\mathbf{w})}{\partial \mathbf{w}}=\mathbf{\Phi }\cdot ({\mathbf{\Phi }}^{\prime }\mathbf{w}-\mathbf{y})$
$此外，矩阵形式的梯度计算可能可以得到更好的GPU加速$

实值函数线性基函数模型参数求解的解析解 closed form solution

$\frac{\partial L(\mathbf{w})}{\partial \mathbf{w}}=\mathbf{\Phi }\cdot ({\mathbf{\Phi }}^{\prime }\mathbf{w}-\mathbf{y})=0$
$\mathbf{\Phi }{\mathbf{\Phi }}^{\prime }\mathbf{w}-\mathbf{\Phi }\mathbf{y}=0$
$\mathbf{\Phi }{\mathbf{\Phi }}^{\prime }\mathbf{w}=\mathbf{\Phi }\mathbf{y}$
$\mathbf{w}=(\mathbf{\Phi }{\mathbf{\Phi }}^{\prime }{)}^{-1}\mathbf{\Phi }\mathbf{y}$

向量值函数广义线性模型

输出值为c个类别的评分，即c元向量，每个维度均由各自的线性函数产生
$\frac{\partial L({\mathbf{w}}_i)}{\partial {\mathbf{w}}_i}=\frac{2}{n}{\Sigma }_{p=1}^n({\hat{y}}^{(p)}-y^{(p)}){\mathbf{x}}^{(p)}=\frac{2}{n}{\Sigma }_{p=1}^n({\mathbf{w}}_i{\mathbf{x}}^{(p)}-y^{(p)}){\mathbf{x}}^{(p)}$
每个维度的权重向量${\mathbf{w}}_i$均可由上述方法求出。

Tips

1.若生成的x为特殊序列则随机梯度下降可能永远无法得到正确解，例如$(1,1,1),(2,2,2),\cdots ,(n,n,n)$，此时各维权重总是增加相同值。
2.原模型偏置bias相对样本的teaching signal较小时，总是无法得到较好解。

KNN识别CIFAR10

1NN

代码

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam
import time

cifar10 = tf.keras.datasets.cifar10
(x_train, y_train), (x_valid, y_valid) = cifar10.load_data()
x_train = x_train.reshape((50000,32*32*3))
x_valid = x_valid.reshape((10000,32*32*3))
x_valid = x_valid[0:1000]
y_valid = y_valid[0:1000]
class NearestNeighbor:
    def __init__(self):
        pass
    def train(self, X, y):
        """ X is N x D, y is 1-dimension of size N"""
        self.Xtr = X
        self.ytr = y
    def predict(self, X):
        """predict an array of patterns at one time"""
        start = time.time()
        num_test = X.shape[0]
        yPred = np.zeros(num_test, dtype = self.ytr.dtype)
        for i in range(num_test):
            print("\r{:.2f}%".format(i*100/num_test), end="")
            distances = np.sum(np.abs(self.Xtr-X[i,:]), axis = 1)#N1
            # N2
            # distances = np.linalg.norm(self.Xtr-X[i,:], ord=2, axis=1)
			# 余弦
			# distances = np.zeros(self.Xtr.shape[0])
            # for j in range(len(distances)):
            #     numerator = np.dot(X[i], self.Xtr[j])
            #     denominator = math.sqrt(np.sum(np.square(X[i])))
            #     denominator *= math.sqrt(np.sum(np.square(self.Xtr[j])))
            #     distances[j] = numerator/denominator
            min_index = np.argmin(distances)
            yPred[i] = self.ytr[min_index]
        end = time.time()
        print(f"\n耗时{end-start}s")
        return yPred
nn = NearestNeighbor()
nn.train(x_train, y_train)
yPred = nn.predict(x_valid)
num_wrong = 0
for i in range(yPred.shape[0]):
  if yPred[i] != y_valid[i]:
    num_wrong += 1
print(num_wrong)
print("accuracy is {:.2f}%".format((1-num_wrong/x_valid.shape[0])*100))
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效果

KNN

代码

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import math
import keras
from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam
import time

cifar10 = tf.keras.datasets.cifar10
(x_train, y_train), (x_valid, y_valid) = cifar10.load_data()
x_train = x_train.reshape((50000,32*32*3))
x_valid = x_valid.reshape((10000,32*32*3))
x_valid = x_valid[0:1000]
y_valid = y_valid[0:1000]
class KNearestNeighbor:
    def __init__(self):
        pass
    def train(self, X, y, k):
        """ X is N x D, y is 1-dimension of size N"""
        self.Xtr = X
        self.ytr = y
        self.k = k
    def getIndex(self, distances):
        n = len(distances)
        cnts = np.zeros(10)
        pivot = np.partition(distances, self.k)[self.k]
        for i in range(n):
            if distances[i] <= pivot:
                cnts[self.ytr[i]] += 1
        return np.argmax(cnts)
    def predict(self, X):
        """predict an array of patterns at one time"""
        start = time.time()
        num_test = X.shape[0]
        yPred = np.zeros(num_test, dtype = self.ytr.dtype)
        for i in range(num_test):
            print("\r{:.2f}%".format(100*i/num_test), end="")
            distances = np.sum(np.abs(self.Xtr-X[i,:]), axis = 1)
            yPred[i] = self.getIndex(distances)
        end = time.time()
        print(f"耗时{end-start}s")
        return yPred
nn = KNearestNeighbor()
for i in range(2, 10): # define range by your own
    nn.train(x_train, y_train, i)
    yPred = nn.predict(x_valid)
    num_wrong = 0
    for j in range(yPred.shape[0]):
    	if yPred[j] != y_valid[j]:
        	num_wrong += 1
    print(num_wrong)
    print("accuracy is {:.2f}%".format((1-num_wrong/x_valid.shape[0])*100))
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效果

使用L1范式作为测度

降噪(或祛糊)自编码器

使用批规范化后训练效果显著增强
GuassNoise产生噪点版 GaussianBlur产生模糊版

普通卷积网络

代码

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import time
import keras
from keras.layers import Input, Dense, Conv2D, GlobalAvgPool2D, Reshape, Conv2DTranspose, BatchNormalization, ReLU
from keras.models import Model
from keras.optimizers import Adam
import math
import albumentations as A
from keras.callbacks import Callback, TensorBoard
import random
import cv2

mnist = keras.datasets.mnist
(x_train, y_train), (x_valid, y_valid) = mnist.load_data()
x_train = np.expand_dims(x_train, -1)
x_valid = np.expand_dims(x_valid, -1)
# transform = A.Compose([A.GaussNoise(var_limit=(10,1000), p=1)])
transform = A.Compose([A.GaussianBlur(p=1)])
before = x_train[0]
after = transform(image=before)["image"]
plt.subplot(121)
plt.imshow(np.squeeze(after), cmap='gray')
plt.subplot(122)
plt.imshow(np.squeeze(before), cmap='gray')
plt.show()
print(after.shape)
def process_data(x):
    def augment_function(imgs):
        aug_imgs = []
        for img in imgs:
            aug_data = transform(image=img)
            aug_imgs.append(aug_data['image'])
        aug_imgs = tf.cast(aug_imgs, tf.float32) / 255.0  # normalization
        return aug_imgs
    aug_imgs = tf.numpy_function(func=augment_function, inp=[x], Tout=[tf.float32])
    targets = tf.cast(x, tf.float32) / 255.0
    return aug_imgs, targets
train_ds = tf.data.Dataset.from_tensor_slices(x_train)
valid_ds = tf.data.Dataset.from_tensor_slices(x_valid)
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.batch(100)
train_ds = train_ds.map(lambda x:
                        tf.py_function(process_data, [x], [tf.float32, tf.float32]), num_parallel_calls=AUTOTUNE)
valid_ds = valid_ds.batch(100)
valid_ds = valid_ds.map(lambda x:
                        tf.py_function(process_data, [x], [tf.float32, tf.float32]), num_parallel_calls=AUTOTUNE)

aug, ori = next(iter(train_ds))
class Convnet(Model):
    def __init__(self):
        super(Convnet, self).__init__()
    def downsample(self, filters, size=(3,3), stride=(2,2), padding='same', name=''):
        model = tf.keras.Sequential(name=name)
        model.add(Conv2D(filters, size, strides=2, padding=padding))
        model.add(BatchNormalization())  # Use batchnormalization
        model.add(ReLU())
        return model
    def upsample(self, filters, size=(3,3), strides=(2,2), padding='same', name=''):
        model = tf.keras.Sequential(name=name)
        model.add(Conv2DTranspose(filters, size, strides=strides, padding=padding))
        model.add(BatchNormalization())
        model.add(ReLU())
        return model
    def build(self):
        input = Input(shape=(28,28,1), name='input')
        h = self.downsample(16, name='downblock_1')(input)
        h = self.downsample(32, name='downblock_2')(h)
        h = self.downsample(64, name='downblock_3')(h)
        h = self.downsample(128, name='downblock_4')(h)
        encoder_out = GlobalAvgPool2D(name='gap')(h)
        h = Dense(2*2*128)(encoder_out)
        h = Reshape((2,2,128))(h)
        h = self.upsample(64, name='upblock_1')(h)
        h = self.upsample(32, (4, 4),(1,1), 'valid', name='upblock_2')(h)
        h = self.upsample(16, name='upblock_3')(h)
        output = Conv2DTranspose(1, (3,3), (2,2),'same', activation='sigmoid',name='output')(h)
        return Model(inputs=input, outputs=output)
autoencoder = Convnet().build()
autoencoder.summary()
# tf.keras.utils.plot_model(autoencoder, show_shapes=True, dpi=72)
class ShowImage(Callback):
    def on_epoch_end(self, epoch, logs=None):
        aug, ori = next(iter(valid_ds))
        aug = aug.numpy()
        ori = ori.numpy()
        index = np.array([random.choice(range(len(valid_ds))) for i in range(8)])
        aug = aug[index]
        ori = ori[index]
        gen = autoencoder(aug, training=False).numpy()
        plt.subplots_adjust(hspace=0.5)
        for i in range(len(gen)):
            plt.subplot(4, 6, i*3+1)
            plt.title('noise')
            plt.imshow(np.squeeze(aug[i]), cmap='gray')
            plt.subplot(4, 6, i * 3 + 2)
            plt.title('original')
            plt.imshow(np.squeeze(ori[i]), cmap='gray')
            plt.subplot(4, 6, i * 3 + 3)
            plt.title('generated')
            plt.imshow(np.squeeze(gen[i]), cmap='gray')
        plt.show()
autoencoder.compile(Adam(0.001), 'MSE', 'MAE')
autoencoder.fit(train_ds, epochs=5, callbacks=[ShowImage()], validation_data=valid_ds)
autoencoder.save('denoise_convnet.h5')

model = keras.models.load_model('denoise_convnet.h5')
model.summary()
denoiseResult = model.predict(np.expand_dims(after/255, 0))
plt.subplot(121)
plt.imshow(np.squeeze(after), cmap='gray')
plt.subplot(122)
plt.imshow(np.squeeze(denoiseResult), cmap='gray')
plt.show()
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效果

U-net

普通卷积网络会丢失位置信息，在信息丢失前直接通过捷径传给生成网络可以得到更准确的生成图像

代码

import tensorflow as tf
import keras
import numpy as np
import albumentations as A
import copy
from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, Conv2DTranspose, GlobalAvgPool2D, Dense, Reshape, Concatenate, ReLU
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from keras.callbacks import Callback
import random
mnist = keras.datasets.mnist
(x_train, y_train),(x_valid, y_valid) = mnist.load_data()
x_train = np.expand_dims(x_train, -1)
x_valid = np.expand_dims(x_valid, -1)
transform = A.Compose([
    A.GaussNoise((0,10000),p=1) #会溢出吗
])
train_ds = tf.data.Dataset.from_tensor_slices(x_train)
valid_ds = tf.data.Dataset.from_tensor_slices(x_valid)
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.batch(100)
valid_ds = valid_ds.batch(100)
def process(x):
    # print(x.shape) #mini-batch? 是的 (100,28,28,1)
    def augment_function(imgs):
        aug_imgs = copy.deepcopy(imgs)
        for i in range(len(imgs)):
            aug_imgs[i] = transform(image=imgs[i])['image']
        aug_imgs = tf.cast(aug_imgs, tf.float32)/255.0
        return aug_imgs
    aug_imgs = tf.numpy_function(augment_function, [x], [tf.float32])
    ori_imgs = tf.cast(x, tf.float32)/255.0
    return aug_imgs, ori_imgs
train_ds = train_ds.map(lambda x: tf.py_function(process, [x], Tout=[tf.float32, tf.float32]))
valid_ds = valid_ds.map(lambda x: tf.py_function(process, [x], Tout=[tf.float32, tf.float32]))
class Unet(Model):
    #same: o_w=ceil(i_w/s_w)
    #valid: o_w=ceil((i_w-f_w+1)/s_w)=ceil((i_w-f_w)/s_w)+1
    def __init__(self):
        super(Unet, self).__init__()
        self.unet=self.mybuild()
    def downsample(self, num_filter, name='', size_filter=(3,3), padding='same'):
        model = keras.Sequential(name=name)
        model.add(Conv2D(num_filter,size_filter,(2,2),padding,activation='relu'))
        model.add(BatchNormalization())
        # model.add(ReLU())
		#add顺序无影响，add(ReLu())与直接加入conv2D在model中层数显式不同，但是最后训练的结果无差别，可通过加入断点查看model属性验证
        return model
    def upsample(self, num_filter, name='', size_filter=(3,3), padding='same', strides=(2,2)):
        model = keras.Sequential(name=name)
        model.add(Conv2DTranspose(num_filter,size_filter,strides,padding))
        model.add(BatchNormalization())
        model.add(ReLU())
        return model
    def mybuild(self):
        input = Input((28,28,1))
        h1 = self.downsample(16, 'db1')(input)
        h2 = self.downsample(32, 'db2')(h1)
        h3 = self.downsample(64, 'db3')(h2)
        h4 = self.downsample(128, 'db4')(h3)
        gap = GlobalAvgPool2D()(h4)
        h5 = Dense(512, activation='relu')(gap)
        h6 = Reshape((2,2,128))(h5)
        h7 = Concatenate()([h4, h6])
        h8 = self.upsample(64, 'ub1')(h7)
        h9 = Concatenate()([h3, h8])
        h10 = self.upsample(32, 'ub2', (4,4), 'valid', (1,1))(h9)
        h11 = Concatenate()([h2, h10])
        h12 = self.upsample(16, 'ub3')(h11)
        h13 = Concatenate()([h1, h12])
        output = self.upsample(1, 'output')(h13) #activation=sigmoid?
        return Model(inputs=input,outputs=output)
unet = Unet().unet
unet.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])

# Custom callback for showing images at the end of epoch
class ShowImage(Callback):
    # When an epoch ends, this function will be called
    def on_epoch_end(self, epoch, logs=None):
        org_imgs = np.array([random.choice(x_valid) for i in range(8)], dtype=np.uint8)
        noise_data = transform(image=org_imgs)
        noise_img = noise_data['image']
        noise_img = tf.cast(noise_img, tf.float32) / 255.0
        # imgs = tf.expand_dims(imgs, axis=-1)
        print(noise_img.shape)
        gens = unet(noise_img, training=False)
        fig = plt.figure(figsize=(14, 10))
        plt.subplots_adjust(hspace=0.4)
        for i, img in enumerate(gens.numpy()):
            noise = noise_img[i]
            noise = np.squeeze(noise)
            plt.subplot(4, 6, i*3+1)
            plt.title('noise')
            plt.imshow(noise, cmap='gray')
            org = org_imgs[i]
            org = np.squeeze(org)
            plt.subplot(4, 6, i*3+2)
            plt.title('original')
            plt.imshow(org, cmap='gray')
            plt.subplot(4, 6, i*3+3)
            plt.title('generated')
            img = np.squeeze(img)
            plt.imshow(img, cmap='gray')
        plt.show()

unet.fit(train_ds, epochs=5, validation_data=valid_ds, callbacks=[ShowImage()])

before = x_train[0]
after = transform(image=before)['image']
denoiseresult = unet.predict(np.expand_dims(after/255, axis=0))
print(denoiseresult.shape)
plt.subplot(121)
plt.imshow(np.squeeze(after), cmap='gray')
plt.subplot(122)
# predict返回值shape带有batch维度
plt.imshow(np.squeeze(denoiseresult[0]), cmap='gray')
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效果

错误检测自编码器

上述U-net与Convnet均不能训练成错误检测自编码器。
甚至只用一类数字训练5epoch，网络也可对其他类型数字进行降噪，说明所得降噪器是类别无关的。

before = data
after = transform(image=before)['image']
denoiseresult = unet.predict(after/255)
pred=denoiseresult
plt.figure(figsize=(20, 3)) 
plt.subplots_adjust(wspace=0.8, hspace=1.0)
for i, img in enumerate(after):
    plt.subplot(2, 10, i+1)
    plt.title(label[i])
    plt.imshow(np.squeeze(img))
    plt.subplot(2, 10, i+11)
    error = tf.reduce_mean(np.linalg.norm(np.matrix(data[i]-pred[i]), 2))
    plt.title("{:.2}".format(error))
    plt.imshow(np.squeeze(pred[i]))
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但是一旦使用了减少层数的Convnet，即embedding vector很长时，所得降噪自编码器变为类别相关。数字结构语义被存储在了浅层结构的DNN中

import tensorflow as tf
import keras
import numpy as np
import albumentations as A
import copy
from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, Conv2DTranspose, GlobalAvgPool2D, Dense, Reshape, Concatenate, ReLU
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from keras.callbacks import Callback
import random
mnist = keras.datasets.mnist
(x_train, y_train),(x_valid, y_valid) = mnist.load_data()

idx=np.zeros(10,int)
index=0
for i in range(10):
    while True:
        if y_valid[index]==i:
            idx[i]=index
            break
        else:
            index+=1
data=x_valid[idx]
data=np.expand_dims(data,-1)
label=y_valid[idx]

detect=3
idx = [i for i, label in enumerate(y_train) if label==detect]
x3_train = np.array([x_train[i] for i in idx])
idx = [i for i, label in enumerate(y_valid) if label==detect]
x3_valid = np.array([x_valid[i] for i in idx])
x_train=x3_train
x_valid=x3_valid
x_train = np.expand_dims(x_train, -1)
x_valid = np.expand_dims(x_valid, -1)
transform = A.Compose([
    A.GaussNoise((0,10000),p=1) #会溢出吗
])
train_ds = tf.data.Dataset.from_tensor_slices(x_train)
valid_ds = tf.data.Dataset.from_tensor_slices(x_valid)
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.batch(100)
valid_ds = valid_ds.batch(100)
def process(x):
    # print(x.shape) #mini-batch? 是的 (100,28,28,1)
    def augment_function(imgs):
        aug_imgs = copy.deepcopy(imgs)
        for i,img in enumerate(imgs):
            aug_imgs[i] = transform(image=img)['image']
        aug_imgs = tf.cast(aug_imgs, tf.float32)/255.0
        return aug_imgs
    aug_imgs = tf.numpy_function(augment_function, [x], [tf.float32])
    ori_imgs = tf.cast(x, tf.float32)/255.0
    return aug_imgs, ori_imgs
train_ds = train_ds.map(lambda x: tf.py_function(process, [x], Tout=[tf.float32, tf.float32]))
valid_ds = valid_ds.map(lambda x: tf.py_function(process, [x], Tout=[tf.float32, tf.float32]))
class Convnet(Model):
    def __init__(self):
        super(Convnet, self).__init__()
    def downsample(self, filters, size=(3,3), stride=(2,2), padding='same', name=''):
        model = tf.keras.Sequential(name=name)
        model.add(Conv2D(filters, size, strides=2, padding=padding))
        model.add(BatchNormalization())  # Use batchnormalization
        model.add(ReLU())
        return model
    def upsample(self, filters, size=(3,3), strides=(2,2), padding='same', name=''):
        model = tf.keras.Sequential(name=name)
        model.add(Conv2DTranspose(filters, size, strides=strides, padding=padding))
        model.add(BatchNormalization())
        model.add(ReLU())
        return model
    def build(self):
        input = Input(shape=(28,28,1), name='input')
        h = self.downsample(16, name='downblock_1')(input)
        h = self.downsample(32, name='downblock_2')(h)
        encoder_out = GlobalAvgPool2D(name='gap')(h)
        h = Dense(7*7*32)(encoder_out)
        h = Reshape((7,7,32))(h)
        h = self.upsample(16, name='upblock_3')(h)
        output = Conv2DTranspose(1, (3,3), (2,2),'same', activation='sigmoid',name='output')(h)
        return Model(inputs=input, outputs=output)
autoencoder = Convnet().build()

# Custom callback for showing images at the end of epoch
class ShowImage(Callback):
    # When an epoch ends, this function will be called
    def on_epoch_end(self, epoch, logs=None):
        org_imgs = np.array([random.choice(x_valid) for i in range(8)], dtype=np.uint8)
        noise_data = transform(image=org_imgs)
        noise_img = noise_data['image']
        noise_img = tf.cast(noise_img, tf.float32) / 255.0
        # imgs = tf.expand_dims(imgs, axis=-1)
        print(noise_img.shape)
        gens = autoencoder(noise_img, training=False)
        fig = plt.figure(figsize=(14, 10))
        plt.subplots_adjust(hspace=0.4)
        for i, img in enumerate(gens.numpy()):
            noise = noise_img[i]
            noise = np.squeeze(noise)
            plt.subplot(4, 6, i*3+1)
            plt.title('noise')
            plt.imshow(noise, cmap='gray')
            org = org_imgs[i]
            org = np.squeeze(org)
            plt.subplot(4, 6, i*3+2)
            plt.title('original')
            plt.imshow(org, cmap='gray')
            plt.subplot(4, 6, i*3+3)
            plt.title('generated')
            img = np.squeeze(img)
            plt.imshow(img, cmap='gray')
        plt.show()
autoencoder.compile(Adam(0.001), 'MSE', 'MAE')
autoencoder.fit(train_ds, epochs=5, callbacks=[ShowImage()], validation_data=valid_ds)
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before = data
after = transform(image=before)['image']
denoiseresult = autoencoder.predict(after/255)
pred=denoiseresult
plt.figure(figsize=(20, 3)) 
plt.subplots_adjust(wspace=0.8, hspace=1.0)
for i, img in enumerate(after):
    plt.subplot(2, 10, i+1)
    plt.title(label[i])
    plt.imshow(np.squeeze(img))
    plt.subplot(2, 10, i+11)
    error = tf.reduce_mean(np.linalg.norm(np.matrix(data[i]-pred[i]), 2))
    plt.title("{:.2}".format(error))
    plt.imshow(np.squeeze(pred[i]))
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100epoch后的结果

VAE 变分推断自编码器

原理

变分推断是统计学常用方法¹。在VAE中，编码器生成潜空间。在潜空间中采样获得潜变量。令$x$为输入数据的随机变量，$z$为潜变量。由联合概率$p(x,z)=p(x)p_{\varphi }(z|x)=p(z)p_{\theta }(x|z)$有$-log\frac{p(z)p(x|z)}{p(x)p(z|x)}=0$，且$\frac{p(z)p(x|z)}{p(z|x)}=p(x)\le 1$，又输入样本概率与优化参数过程无关，故损失函数为$-log\frac{p(z)p(x|z)}{p(z|x)}$，且损失函数中的概率函数在计算时使用概率密度函数替代。假设$Z|X\sim N({\mu }_{\varphi }(x),{\Sigma }_{\varphi }(x)),Z\sim N(0,1),X|Z\sim N({\mu }_{\theta }(z),{\Sigma }_{\theta }(z))$。计算过程中使用再参数化技巧将z的条件采样改为${\mu }_{\varphi }(z)+ϵ{\Sigma }_{\varphi }(z),ϵ采样自N(0,1)$使得计算图可反向传播。由于重构图像为神经网络确定性生成且网络未计算${\mu }_{\theta }(z),{\Sigma }_{\theta }(z)$，故使用KL散度代替$-logp(x|z)$，$D_{KL}(X||X^{\prime })=H_X(X^{\prime })-H(X)$，即交叉熵减自信息熵，进一步使用交叉熵替代。

代码

import tensorflow as tf
import keras
import numpy as np
from keras.models import Model
from keras.layers import Input, Conv2D, Flatten, Dense, BatchNormalization, Reshape, Conv2DTranspose
import matplotlib.pyplot as plt
from keras.optimizers import Adam
import random
mnist = keras.datasets.mnist
(x_train, y_train),(x_valid, y_valid) = mnist.load_data()
x_train = np.expand_dims(np.float32(x_train), -1)/255.0
x_valid = np.expand_dims(np.float32(x_valid), -1)/255.0
train_ds = tf.data.Dataset.from_tensor_slices(x_train)
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.batch(100)

class VAE(Model):
    def __init__(self, n_zdim):
        super(VAE, self).__init__()
        self.n_zdim=n_zdim
        self.encoder = self.encoder_model()
        self.decoder = self.decoder_model()
        self.loss_tracker = keras.metrics.Mean(name="loss")
    def encoder_model(self):
        input = Input((28,28,1)) # padding 如何影响参数个数
        h = Conv2D(32, (3,3), (2,2), "valid", activation="relu")(input)
        h = BatchNormalization()(h)
        h = Conv2D(64, (3,3), (2,2), "valid", activation="relu")(h)
        h = BatchNormalization()(h)
        h = Flatten()(h)
        z_mean = Dense(self.n_zdim)(h)
        z_log_stddev = Dense(self.n_zdim)(h)
        return Model(inputs=input, outputs=[z_mean, z_log_stddev])
    def decoder_model(self): # 增加反卷积层数 减少全连接单元数 总参数减少且一直少于编码器
        input = Input(self.n_zdim)
        h = Dense(7*7*32, activation='relu')(input)
        h = Reshape((7,7,32))(h)
        h = BatchNormalization()(h)
        h = Conv2DTranspose(64, (3,3), (2,2), "same", activation='relu')(h)
        h = BatchNormalization()(h)
        h = Conv2DTranspose(32, (3,3), (2,2), "same", activation='relu')(h)
        h = BatchNormalization()(h)
        # 最后一层激活函数必须linear 否则loss一直处于高位 因为一开始生成数据都是负数 relu输出全0 无法进行有效反向传播
        output = Conv2DTranspose(1, (3,3), (1,1), "same", activation="linear")(h) #Conv2D也可？
        return Model(input, output)
    def reparameterization(self, z_mean, z_log_stddev):
        epsilon = tf.random.normal(tf.shape(self.n_zdim))
        return z_mean+epsilon*tf.exp(z_log_stddev)
    def encode(self, x):
        mean, log_stddev = self.encoder(x)
        return mean, log_stddev
    def decode(self, z):
        return self.decoder(z)
    @tf.function
    def log_normal_pdf(self, sample, mean, log_stddev, raxis=1):
        # print(sample.shape) #(None, 100)
        log2pi = tf.math.log(2. * np.pi)
        return tf.reduce_sum(
            -log2pi*log_stddev-0.5*(sample-mean)**2.*tf.exp(-2.*log_stddev),
            axis=raxis
        )
    @tf.function
    def train_step(self, data):
        with tf.GradientTape() as tape:
            z_mean, z_log_stddev = self.encoder(data)
            z = self.reparameterization(z_mean, z_log_stddev)
            reconstruct = self.decoder(z)
            cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(
                labels=data, logits=reconstruct)
            # print(cross_entropy.shape) #(None,28,28,1)
            cross_entropy = tf.reduce_sum(cross_entropy, axis=[1,2,3]) #(None,)
            cross_entropy = tf.reduce_mean(cross_entropy)
            log_pz = self.log_normal_pdf(z,0,0) #N(0,1) shape=(None,)
            # log_px_z = -tf.reduce_sum(cross_entropy, axis=[1,2,3])
            log_qz_x = self.log_normal_pdf(z, z_mean, z_log_stddev)
            # total_loss = -tf.reduce_mean(log_pz+log_px_z-log_qz_x) #()
            total_loss = -tf.reduce_mean(log_pz-log_qz_x)+cross_entropy
        gradients = tape.gradient(total_loss, self.trainable_variables)
        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
        self.loss_tracker.update_state(total_loss)
        return {'loss': self.loss_tracker.result()}
model = VAE(100)
model.encoder.summary()
model.decoder.summary()

model.compile(optimizer=Adam(0.001))
model.fit(train_ds, epochs=5)

idx = random.randint(0, 9998) # select image index
data = x_valid[idx:idx+2] # extract two images

z_mu, z_log_stddev = model.encode(data)
w_mu = []
w_log_stddev = []
for i in range(11):
    w_mu.append((1.0 - i * 0.1) * z_mu[0] + (i * 0.1) * z_mu[1])
    w_logvar.append((1.0 - i * 0.1) * z_log_stddev[0] + (i * 0.1) * z_log_stddev[1])
w_mu = tf.cast(w_mu, tf.float32)
w_log_stddev = tf.cast(w_logvar, tf.float32)
z = model.reparameterization(w_mu, w_logvar) # latent vector generation from encoder out
outputs = model.decode(z) # Generate image using latent vecotr
def to_numpy(tftensorimg):
    npy = tftensorimg.numpy().reshape(28, 28)
    max = npy.max()
    min = npy.min()
    npy = (npy - min) / (max - min) * 255.
    return np.uint8(npy)

fig = plt.figure(figsize=(22, 2), dpi=72)
plt.subplots_adjust(wspace=0.5)
for i in range(11):
    img = to_numpy(outputs[i])
    ax = fig.add_subplot(1, 11, i+1)
    if i == 0:
        ax.set_title(str(y_valid[idx]))
    elif i == 10:
        ax.set_title(str(y_valid[idx+1]))
    ax.imshow(img, cmap='gray')
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效果

GAN 生成对抗网络

原理

本质为零和博弈，记鉴别器的收益函数$V(D,G)$，其目标为最大化收益函数，可控量为鉴别器产生的鉴别函数：$\underset{D}{\max }V(D,G)$，同时生成器的收益函数为函数$-V(D,G)$，游戏总体来说变为$\underset{G}{\min }\underset{D}{\max }V(D,G)$。一般令$V(D,G)=E_{X\sim p_{data}(x)}[logD(X)]+E_{Z\sim p_Z(z)}[log(1-D(G(Z)))]$。因此在训练时，生成器的loss可设置为负二值交叉熵$-H_{Z\sim p_Z(z)}(1-D(G(z))$，Z为一随机变量，在此例服从$N(0,1)$，故生成器本质希望交叉熵尽可能大，即$p_Z(z)$与$1-D(G(z))$尽可能不同，即$p_Z(z)$与$D(G(z))$尽可能相同，$p_Z(z)\equiv 1$。通俗理解，生成器期望鉴别器总输出1，以便自己生成的fake图像通过测试。在实践中我们使用随机生成图像确保这点的实现，此时不必关注鉴别器对真实图像的反应。最终生成器的loss设置为$1\times (-logD(G(z)))+0\times (-log(1-D(G(z))))$，即$bce(1,D(G(z)))$。鉴别器同时关心真实图像与fake图像的鉴别结果，因此损失函数为$bce(1,D(x))+bce(0,D(G(z)))$。

代码

import tensorflow as tf
import keras
import numpy as np
from keras.models import Model
from keras.layers import Input, Conv2D, Flatten, Dense, BatchNormalization, Reshape, Conv2DTranspose, LeakyReLU, MaxPooling2D, Dropout
from keras.losses import BinaryCrossentropy
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from keras.callbacks import Callback, TensorBoard

mnist = keras.datasets.mnist
(x_train, y_train), (x_valid, y_valid) = mnist.load_data()
x_train = np.expand_dims(np.float32(x_train)/255.0, -1)
train_ds = tf.data.Dataset.from_tensor_slices(x_train)
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.batch(100)

class DCGAN(Model):
    def __init__(self):
        super(DCGAN, self).__init__()
        self.noise_dim=128
        self.bce = BinaryCrossentropy(False) #prediction is not a logits
        self.generator = self.generator_model()
        self.discriminator = self.discriminator_model()
        self.generator.optimizer = Adam(1e-4)
        self.discriminator.optimizer = Adam(1e-4)
        self.loss_gen_tracker = tf.keras.metrics.Mean(name="loss_gen")
        self.loss_dis_tracker = tf.keras.metrics.Mean(name="loss_dis")
    def deconv(self, filters, size=(3,3), strides=(2,2), padding='same', name=''):
        model = tf.keras.Sequential(name=name)
        model.add(Conv2DTranspose(filters, size, strides=strides, padding=padding))
        model.add(BatchNormalization())
        model.add(LeakyReLU())
        return model
    def generator_model(self):
        input = Input(self.noise_dim, name="input")
        h = Dense(7*7*256)(input)
        h = BatchNormalization()(h)
        h = LeakyReLU()(h)
        h = Reshape((7,7,256))(h)
        h = self.deconv(128, strides = (1, 1))(h)
        h = self.deconv(64)(h)
        h = self.deconv(32)(h)
        output = Conv2DTranspose(1, (3, 3), (1,1), "same", activation="sigmoid")(h)
        return Model(input, output)
    def conv(self, filters, size=(3,3), strides=(1,1), padding='same', name=''):
        model = keras.Sequential(name=name)
        model.add(Conv2D(filters, size, strides, padding, activation="relu"))
        model.add(MaxPooling2D((2,2)))
        model.add(Dropout(0.3))
        return model
    # drop out is necessary for discriminator
    def discriminator_model(self):
        input = Input((28,28,1))
        h = self.conv(32)(input)
        h = self.conv(64)(h)
        h = self.conv(128)(h)
        h = Flatten()(h)
        h = Dense(256, "relu")(h)
        output = Dense(1, "sigmoid")(h)
        return Model(input, output)
    @tf.function
    def train_step(self, data):
        noise = tf.random.normal((len(data), self.noise_dim))
        with tf.GradientTape() as gen_tape, tf.GradientTape() as dis_tape:
            fake_images = self.generator(noise)
            fake_output = self.discriminator(fake_images)
            loss_gen = self.bce(tf.ones_like(fake_output), fake_output)
            real_output = self.discriminator(data)
            loss_real = self.bce(tf.ones_like(real_output), real_output)
            loss_fake = self.bce(tf.zeros_like(fake_output), fake_output)
            loss_dis = loss_real+loss_fake
        gen_gradients = gen_tape.gradient(loss_gen, self.generator.trainable_variables)
        dis_gradients = dis_tape.gradient(loss_dis, self.discriminator.trainable_variables)
        self.generator.optimizer.apply_gradients(zip(gen_gradients, self.generator.trainable_variables))
        self.discriminator.optimizer.apply_gradients(zip(dis_gradients, self.discriminator.trainable_variables))
        self.loss_gen_tracker.update_state(loss_gen)
        self.loss_dis_tracker.update_state(loss_dis)
        return {"loss_gen":self.loss_gen_tracker.result(),
                "loss_dis":self.loss_dis_tracker.result()}
    @property
    def metrics(self):
        return [self.loss_gen_tracker, self.loss_dis_tracker]
model = DCGAN()
model.generator.summary()
model.discriminator.summary()

# Test the models
# show generated image for untrained model
noise = tf.random.normal([1, 128])
gen_out = model.generator(noise, training=False)
gen_img = np.squeeze(gen_out.numpy())
plt.imshow(gen_img, cmap='gray')

fake_out = model.discriminator(gen_out)
r_img = tf.expand_dims(x_train[0], axis=0)
r_img = tf.expand_dims(r_img, axis=-1)
r_img = (tf.cast(r_img, tf.float32) - 127.5) / 127.5
real_out = model.discriminator(r_img, training=False)
tf.print('fake:', fake_out, 'real:', real_out)

model.compile()
tb_cb = TensorBoard("dcgan_logs", 1)
class GenerateImage(Callback):
    # When an epoch ends, this function will be called
    def on_epoch_end(self, epoch, logs=None):
        noise = tf.random.normal([16, 128])
        gen_out = model.generator(noise, training=False)
        gen_imgs = gen_out.numpy()
        fig = plt.figure(figsize=(4, 4))
        for i in range(16):
            plt.subplot(4, 4, i+1)
            img = np.squeeze(gen_imgs[i])
            plt.imshow(img, cmap='gray')
        #plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
        plt.show()
model.fit(train_ds, epochs=20, callbacks=[tb_cb, GenerateImage()])
# Test
noise = tf.random.normal([16, 128])
gen_out = model.generator(noise, training=False)
gen_imgs = gen_out.numpy()
fig = plt.figure(figsize=(4, 4))
plt.subplots_adjust(wspace=0.2)
for i in range(16):
    ax = fig.add_subplot(4,4,i+1)
    img = np.squeeze(gen_imgs[i])
    ax.set_title(f"{i+1}")
    ax.imshow(img, cmap='gray')
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效果

epoch=20，前几个epoch会训练出全0图像，可能是由于0背景占比过大。若由于随机初始点较差使得鉴别器能力过强导致生成器生成纯黑背景且长时间不变可重新训练。

C-DCGAN 条件深度卷积生成对抗网络

代码

import random

import tensorflow as tf
import keras
import numpy as np
from keras.models import Model
from keras.layers import Input, Conv2D, Flatten, Dense, BatchNormalization, Reshape, Conv2DTranspose, LeakyReLU, \
    MaxPooling2D, Dropout, Concatenate
from keras.losses import BinaryCrossentropy
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from keras.callbacks import Callback, TensorBoard

mnist = keras.datasets.mnist
(x_train, y_train), (x_valid, y_valid) = mnist.load_data()
x_train = np.expand_dims(np.float32(x_train) / 255.0, -1)
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.batch(100)

class C_DCGAN(Model):
    def __init__(self):
        super(C_DCGAN, self).__init__()
        self.noise_dim = 128
        self.bce = BinaryCrossentropy(False)  # prediction is not a logits
        self.initializer = tf.keras.initializers.HeNormal(seed=0)
        self.loss_gen_tracker = tf.keras.metrics.Mean(name="loss_gen")
        self.loss_dis_tracker = tf.keras.metrics.Mean(name="loss_dis")
        self.generator = self.generator_model()
        self.discriminator = self.discriminator_model()
        self.generator.optimizer = Adam(1e-4)
        self.discriminator.optimizer = Adam(1e-4)

    def deconv(self, filters, size=(3, 3), strides=(2, 2), padding='same', name=''):
        model = tf.keras.Sequential(name=name)
        model.add(Conv2DTranspose(filters, size, strides, padding, kernel_initializer=self.initializer))
        model.add(BatchNormalization())
        model.add(LeakyReLU())
        return model

    def generator_model(self):
        input_noise = Input(self.noise_dim)
        input_one_hot_label = Input(10)
        h = Dense(7 * 7 * 128, kernel_initializer=self.initializer)(input_noise)
        h = BatchNormalization()(h)
        h = LeakyReLU()(h)
        h = Reshape((7, 7, 128))(h)
        h1 = Dense(7 * 7 * 128, kernel_initializer=self.initializer)(input_one_hot_label)
        h1 = BatchNormalization()(h1)
        h1 = LeakyReLU()(h1)
        h1 = Reshape((7, 7, 128))(h1)
        h = Concatenate()([h, h1])
        h = self.deconv(128, strides=(1, 1))(h)
        h = self.deconv(64)(h)
        h = self.deconv(32)(h)
        output = Conv2DTranspose(1, (3, 3), (1, 1), "same", activation="sigmoid", kernel_initializer=self.initializer)(h)
        return Model([input_noise, input_one_hot_label], output)

    def conv(self, filters, size=(3, 3), strides=(1, 1), padding='same', name=''):
        model = keras.Sequential(name=name)
        model.add(Conv2D(filters, size, strides, padding, activation="relu", kernel_initializer=self.initializer))
        model.add(MaxPooling2D((2, 2)))
        model.add(Dropout(0.3))
        return model

    # drop out is necessary for discriminator
    def discriminator_model(self):
        input_x = Input((28, 28, 1))
        input_one_hot_label = Input(10)
        h = Dense(28*28*1, "relu", kernel_initializer=self.initializer)(input_one_hot_label)
        h = Reshape((28, 28, 1))(h)
        # mix： label 的语义本身涵盖在图像之中
        h = keras.layers.add([input_x, h])
        h = self.conv(32)(h)
        h = self.conv(64)(h)
        h = self.conv(128)(h)
        h = Flatten()(h)
        h = Dense(256, "relu", kernel_initializer=self.initializer)(h)
        output = Dense(1, "sigmoid", kernel_initializer=tf.keras.initializers.glorot_normal())(h)
        return Model([input_x, input_one_hot_label], output)

    @tf.function
    def train_step(self, data):
        x, y = data # data is tuple 'tuple' object has no attribute 'shape'
        # print(len(data))
        # data is a 2-tuple Dataset数据格式为外层是传入的tuple，接下来才是batch维度
        one_hot = tf.one_hot(y, 10) #(None, 10)
        noise = tf.random.normal((len(x), self.noise_dim))
        with tf.GradientTape() as gen_tape, tf.GradientTape() as dis_tape:
            fake_images = self.generator((noise, one_hot))
            print(fake_images.shape)
            fake_output = self.discriminator((fake_images, one_hot))
            loss_gen = self.bce(tf.ones_like(fake_output), fake_output)
            real_output = self.discriminator((x, one_hot))
            loss_real = self.bce(tf.ones_like(real_output), real_output)
            loss_fake = self.bce(tf.zeros_like(fake_output), fake_output)
            loss_dis = loss_real + loss_fake
        gen_gradients = gen_tape.gradient(loss_gen, self.generator.trainable_variables)
        dis_gradients = dis_tape.gradient(loss_dis, self.discriminator.trainable_variables)
        self.generator.optimizer.apply_gradients(zip(gen_gradients, self.generator.trainable_variables))
        self.discriminator.optimizer.apply_gradients(zip(dis_gradients, self.discriminator.trainable_variables))
        self.loss_gen_tracker.update_state(loss_gen)
        self.loss_dis_tracker.update_state(loss_dis)
        return {"loss_gen": self.loss_gen_tracker.result(),
                "loss_dis": self.loss_dis_tracker.result()}

    @property
    def metrics(self):
        return [self.loss_gen_tracker, self.loss_dis_tracker]


model = C_DCGAN()
model.generator.summary()
model.discriminator.summary()

model.compile()
tb_cb = TensorBoard("c_dcgan_logs", 1)

# Test the models
# show generated image for untrained model
label = np.random.randint(0,10,(1))
onehot = tf.one_hot(label, depth=10)
noise = tf.random.normal([1, 128])
gen_out = model.generator((noise, onehot), training=False)
gen_img = np.squeeze(gen_out.numpy())
plt.imshow(gen_img, cmap='gray')

fake_out = model.discriminator((gen_out, onehot))
r_img = tf.expand_dims(x_train[0], axis=0)
r_img = tf.expand_dims(r_img, axis=-1)
r_img = (tf.cast(r_img, tf.float32) - 127.5) / 127.5
real_out = model.discriminator((r_img, onehot), training=False)
tf.print('fake:', fake_out, 'real:', real_out)

# Custom callback
class GenerateImage(Callback):
    # When an epoch ends, this function will be called
    def on_epoch_end(self, epoch, logs=None):
        noise = tf.random.normal([100, 128])
        onehot = tf.one_hot(tf.repeat(tf.range(10), repeats=10), depth=10)
        gen_out = model.generator((noise, onehot), training=False)
        gen_imgs = gen_out.numpy()
        fig = plt.figure(figsize=(12, 12))
        for i in range(100):
            plt.subplot(10, 10, i+1)
            img = np.squeeze(gen_imgs[i])
            plt.imshow(img, cmap='gray')
        #plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
        plt.show()

model.fit(train_ds, epochs=20, callbacks=[tb_cb, GenerateImage()])

noise = tf.random.normal([100, 128])
onehot = tf.one_hot(tf.repeat(tf.range(10), repeats=10), depth=10)
gen_out = model.generator((noise, onehot), training=False)
gen_imgs = gen_out.numpy()
fig = plt.figure(figsize=(12, 12))
for i in range(100):
    plt.subplot(10, 10, i+1)
    img = np.squeeze(gen_imgs[i])
    plt.imshow(img, cmap='gray')
    #plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
plt.show()
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结果

epoch=18

epoch=20

使用K-means计算不规则图形面积

该代码来自b站up：耿大哥讲算法
后续可能会根据思路重新书写该代码

import numpy as np
from PIL import Image
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rc('font',family='Microsoft YaHei')
#导入图片 转为jpg格式
img=Image.open('洞庭湖.png')
p=Image.new('RGB', img.size, (255,255,255))
p.paste(img, (0,0,img.size[0],img.size[1]),img)

img_old=np.array(p)
hang,lie,dims=img_old.shape
print('这张图片的像素矩阵有'+str(hang)+'行,'+str(lie)+'列，'+str(dims)+'通道。')
#构建原始像素矩阵
R=img_old[:,:,0];G=img_old[:,:,1];B=img_old[:,:,2]
#像素拼接
list_R,list_G,list_B=[],[],[]
for i in range(hang):
    for j in range(lie):
        list_R.append(R[i,j]);list_G.append(G[i,j]);list_B.append(B[i,j])
print('像素拼接完成！')
#构建原始数据矩阵A
A=np.zeros(shape=(dims,len(list_G)))
for i in range(A.shape[1]):
    A[0,i]=list_R[i];A[1,i]=list_G[i];A[2,i]=list_B[i]
#k-means聚类
k=4
kmeans=KMeans(n_clusters=k,init='random',n_init=50,tol=0.001)
kmeans.fit(A.T)
label=list(kmeans.labels_)
core=np.array([[0,255,255,255],
               [255,0,102,204],
               [255,255,0,153]])
print('k-means聚类完成！')
#构建数据矩阵B
B=np.zeros(shape=(A.shape[0],A.shape[1]))
for i in range(A.shape[0]):
    for j in range(A.shape[1]):B[i,j]=core[i,label[j]]
R_new=np.zeros(shape=(hang,lie))
G_new=np.zeros(shape=(hang,lie))
B_new=np.zeros(shape=(hang,lie))
for i in range(hang):
    for j in range(lie):
        R_new[i,j]=B[0,lie*i+j];G_new[i,j]=B[1,lie*i+j];B_new[i,j]=B[2,lie*i+j]
img_new=np.zeros(shape=(hang,lie,dims))
for i in range(hang):
    for j in range(lie):
        img_new[i,j,0]=R_new[i,j];img_new[i,j,1]=G_new[i,j];img_new[i,j,2]=B_new[i,j]
print('新的像素矩阵合成完毕！')
print('图片总面积约等于'+str(60*50)+'平方公里。')
print('耕地面积约等于'+str('%.2f'%(60*50/(hang*lie)*label.count(label[1]))+'平方公里。'))
print('洪泽湖面积约等于'+str('%.2f'%(60*50-60*50/(hang*lie)*label.count(label[1]))+'平方公里。'))
#图片输出
plt.subplot(1,2,1);plt.xlabel('分割前的图像',fontsize=18);plt.imshow(img_old/255)
plt.subplot(1,2,2);plt.xlabel('分割后的图像',fontsize=18);plt.imshow(img_new/255)
plt.show()
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使用LSTM预测时间序列数据类别

网球挥拍3轴加速度时间序列

原理

代码

import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from keras.layers import Input, Masking, LSTM, Dropout, Dense
from tensorflow.compat.v1.keras.layers import CuDNNLSTM
from keras.models import Model
from keras.regularizers import l2
from keras.losses import SparseCategoricalCrossentropy
from keras.metrics import SparseCategoricalAccuracy
from keras.callbacks import TensorBoard, EarlyStopping, ReduceLROnPlateau
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from keras import initializers
def mk_file_list(base_dir):
    path_base_dir = pathlib.Path(base_dir)
    return [str(p) for p in sorted(path_base_dir.glob('*.txt'))]
list_train = mk_file_list('tennis/data/train')
list_valid = mk_file_list('tennis/data/valid')

# Try to visualize a swing data
data_file = list_train[10]
with open(data_file, 'r') as f:
    lines = f.readlines() # load data
data = [l.strip().split()[1:] for l in lines]
data = np.array(data, dtype=np.float32) # To NumPy

# Show graph
plt.plot(data[:, 0], label='x') # X axis
plt.plot(data[:, 1], label='y') # Y
plt.plot(data[:, 2], label='z') # Z
plt.legend()
plt.grid() # Grid
plt.title(data_file) # File name
plt.show()

def get_label(filename):
    return LABEL.index(pathlib.Path(filename).name.split('_')[0]) #可以优化成int8

def make_padding_data(data_files):
    batch_data = []
    label_data = []
    for d in data_files:
        filename = bytes.decode(d.numpy())
        with open(filename, 'r') as f:
            lines = f.readlines()
        data = [l.strip().split()[1:] for l in lines] #split() 默认参数是？
        data = np.array(data, dtype=np.float32)
        data = (data-np.mean(data))/np.std(data)
        batch_data.append(data)
        label_data.append(get_label(filename))
    batch_data = pad_sequences(batch_data, padding="post", value=0, dtype=np.float32)
    return batch_data, label_data
LABEL=['A','B','C','D','E','F','G','H','I','J']
batch_size=20

train_ds = tf.data.Dataset.list_files(list_train)
valid_ds = tf.data.Dataset.list_files(list_valid)
train_ds = train_ds.shuffle(len(list_train))
valid_ds = valid_ds.shuffle(len(list_valid))
train_ds = train_ds.batch(batch_size)
valid_ds = valid_ds.batch(batch_size)

train_ds = train_ds.map(lambda x: tf.py_function(
    make_padding_data, [x], Tout=[tf.float32, tf.int32]
))
valid_ds = valid_ds.map(lambda x: tf.py_function(
    make_padding_data, [x], Tout=[tf.float32, tf.int32]
))
input = Input((None, 3), name="input")
h = Masking(0)(input)
# h = LSTM(128, return_sequences=False, kernel_regularizer=l2(1e-4))(h)
h = LSTM(128, return_sequences=False, kernel_regularizer=l2(1e-4), kernel_initializer=initializers.HeNormal())(h)
h = Dropout(0.5)(h)
h = Dense(128, activation="relu", kernel_regularizer=l2(1e-4), kernel_initializer=initializers.GlorotNormal())(h)
h = Dropout(0.5)(h)
output = Dense(10, activation="softmax", kernel_regularizer=l2(1e-4))(h)
model = Model(input, output)
model.summary()
model.compile(Adam(1e-4),SparseCategoricalCrossentropy(), [SparseCategoricalAccuracy()])

cb_tb = TensorBoard(log_dir="logs_swing", histogram_freq=1)
cb_es = EarlyStopping("val_loss", patience=10)
reduce_lr = ReduceLROnPlateau("val_loss", 0.5, 5, 0, min_lr=1e-5)
model.fit(train_ds, epochs=100, validation_data=valid_ds, callbacks=[cb_tb, cb_es, reduce_lr], use_multiprocessing=True)

file = 'tennis/data/valid/A_10.txt'
with open(file, 'r') as f:
    lines = f.readlines()
data = [l.strip().split()[1:] for l in lines]
data = np.expand_dims(np.array(data, dtype=np.float32), axis=0)
data = (data - np.mean(data)) / np.std(data)
pred = model.predict(data)
LABEL[pred.argmax()]
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使用CNN与LSTM进行汇率预测

!pip install tensorflow_addons
import numpy as np
import csv
import tensorflow as tf
from keras.layers import Input, Conv2D, BatchNormalization, Dropout, Reshape, LSTM, Dense
from keras.regularizers import l2
from keras.models import Model
import tensorflow_addons as tfa
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.metrics import SparseCategoricalAccuracy
from tensorflow.keras.callbacks import TensorBoard, LearningRateScheduler
history = 100 # use 100 days history for prediction
N = 10 # Predict exchange 10 days after from the last day of the history
exclude_days = 30 # the last 30 days are excluded because of testing

data = [] # for store the loaded data
with open('usdjpy_d.csv', 'r') as f:
    reader = csv.reader(f) # CSV file open
    data = [row[1:] for row in reader] # Load data from the CSV file
    data.pop(0) # Remove the header
data = np.array(data, dtype=np.float32) # Convert to NumPy
orgdata = data
data = (data - data.min()) / (data.max() - data.min()) # Normalization
train_idx = np.arange(history+N, len(data)-1-exclude_days).astype(np.int32) # Make index file
np.random.shuffle(train_idx) # Random shuffle
train_idx, valid_idx = np.split(train_idx, [int(len(data) * 0.9)]) # Split dataset: 90% for training, 10% for testing

def make_inputdata(idx):
    inputs = []
    targets = []
    for i in idx:
        his_data = data[i.numpy()-N-history : i.numpy()-N]
        inputs.append(his_data)
        if data[i.numpy()][-1] < data[i.numpy()-N][-1]:
            targets.append(0)
        else:
            targets.append(1)
    inputs = np.expand_dims(np.array(inputs), -1)
    return inputs, targets
train_ds = tf.data.Dataset.from_tensor_slices(train_idx)
train_ds = train_ds.batch(50)
train_ds = train_ds.map(lambda x: tf.py_function(
    make_inputdata, [x], Tout=[tf.float32, tf.float32]
))
valid_ds = tf.data.Dataset.from_tensor_slices(valid_idx)
valid_ds = valid_ds.batch(100)
valid_ds = valid_ds.map(lambda x: tf.py_function(
    make_inputdata, [x], Tout=[tf.float32, tf.float32]
))

def ExchangeModel(history=100, u_units=256, seed=0):
    init = tf.keras.initializers.HeNormal(seed)
    input = Input((history, 4, 1), name="input")
    h = Conv2D(32, (20, 2), (4, 1), activation="linear", kernel_initializer=init, kernel_regularizer=l2(1e-4), name="cnn1")(input)
    h = BatchNormalization()(h)
    h = Dropout(0.5)(h)
    h = Conv2D(64, (10, 2), (1, 1), activation="linear", kernel_initializer=init, kernel_regularizer=l2(1e-4), name="cnn2")(h)
    h = BatchNormalization()(h)
    h = Dropout(0.5)(h)
    h = Conv2D(128, (5, 2), (1, 1), activation="linear", kernel_initializer=init, kernel_regularizer=l2(1e-4), name="cnn3")(h)
    h = BatchNormalization()(h)
    h = Dropout(0.5)(h)
    h = Reshape((8, 128), input_shape=(8, 1, 128))(h)
    h = LSTM(u_units, return_sequences=True, kernel_initializer=init, kernel_regularizer=l2(1e-4), name="lstm1")(h)
    h = Dropout(0.5)(h)
    h = LSTM(u_units, return_sequences=False, kernel_initializer=init, kernel_regularizer=l2(1e-4), name="lstm2")(h)
    h = Dense(u_units, "linear", kernel_initializer=init, kernel_regularizer=l2(1e-4), name="fc")(h)
    h = Dropout(0.5)(h)
    output = Dense(2, "softmax", kernel_initializer=init, kernel_regularizer=l2(1e-4), name="output")(h)
    model = Model(input, output)
    return model
model = ExchangeModel()
model.compile(optimizer=tfa.optimizers.LAMB(0.0001), loss=SparseCategoricalCrossentropy(), metrics=SparseCategoricalAccuracy())
model.summary()
def lr_schedule(epoch, lr): # Note: epoch starts at zero
    if epoch != 0 and ( epoch % 800 or epoch % 1000 ) == 0:
        return lr * 0.5
    else:
        return lr
lr_cb = LearningRateScheduler(lr_schedule, verbose=1) # dynamic learning rate change depending on epoch
tb_cb = TensorBoard(log_dir='exchange_logs', histogram_freq=1)
model.fit(train_ds, initial_epoch=0, epochs=1200, validation_data=valid_ds, callbacks=[tb_cb], use_multiprocessing=True)
data = []
days = []
with open('usdjpy_d.csv', 'r') as f:
    reader = csv.reader(f)
    for row in reader:
        days.append(row[0])
        data.append(row[1:])
data.pop(0) # remove the header
days.pop(0)

evaldata = []
label = []
cls =['up', 'down']
lastidx = len(data) - 1

for i in range(lastidx-exclude_days+1, lastidx+1): # from befor exlude_day to the latest
    his_data = data[i-history-N : i-N]
    if data[i-N][-1] > data[i][-1]:
        label.append(0) # Yen up
    else:
        label.append(1) # Yen down
    eval = np.expand_dims(np.array(his_data), -1)
    evaldata.append(eval)
evaldata = np.array(evaldata, dtype=np.float32) #
print(len(evaldata))
num_corr = 0 # num. of correction

evaldata = (evaldata - evaldata.min()) / (evaldata.max() - evaldata.min()) # Normalization
r = model.predict(evaldata)

for i, result in enumerate(model.predict(evaldata)):
    est = np.argmax(result)
    if est == label[i]: # if correct
        num_corr += 1
    print(f'Result for {days[lastidx-exclude_days+1+i]}: {cls[est]} (True: {cls[label[i]]})')
print('=== Prediction Result ===')
print(f'Num. of evaluation data: {i+1}, correction: {num_corr}, correct rate {num_corr/(i+1)*100}%')
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语音识别

import keras.backend
import tensorflow as tf
from keras.layers import StringLookup
import os
import glob
from scipy.io.wavfile import read
import librosa
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Input, Dense, Dropout, LSTM, Bidirectional, Masking
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import Callback, TensorBoard
import numpy as np

# The set of characters accepted in the transcription.
characters = [x for x in 'efghinorstuvwx'] # limited the alphabet set consisting of number words (from "one" to "nine")
# Mapping characters to integers
char_to_num = StringLookup(vocabulary=characters, oov_token='')
# Mapping integers back to original characters
num_to_char = StringLookup(vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True)

print(
    f'The vocabulary set is: {char_to_num.get_vocabulary()}'
    f' (size={char_to_num.vocabulary_size()})'
)

def get_filelist_and_label(dir):
    filelist = []
    labels = []
    for d in os.listdir(dir):
        for f in glob.glob(os.path.join(dir, d, "*.wav")):
            filelist.append(f)
            labels.append(d)
    return filelist, labels

train_list, train_labels = get_filelist_and_label(r"numvoice\train")
valid_list, valid_labels = get_filelist_and_label(r"numvoice\test")

def wav2mfcc(wav_files, labels):
    x=[]
    y=[]
    for wav in wav_files:
        file = bytes.decode(wav.numpy())
        rate, samples = read(file)
        feat = librosa.feature.mfcc(y=np.float32(samples), sr=rate, n_mfcc=13, n_fft=512, hop_length=160)
        f = np.average(feat)
        s = np.std(feat)
        feat = (feat.T-f)/s
        x.append(feat)
    for label in labels:
        label = tf.strings.unicode_split(label, "UTF-8")
        y.append(char_to_num(label))
    x = pad_sequences(x, padding="post", value=.0, dtype=np.float32)
    y = pad_sequences(y, padding="post", value=0, dtype=np.int32)
    return x, y

batch_size = 20
train_ds = tf.data.Dataset.from_tensor_slices((train_list, train_labels))
valid_ds = tf.data.Dataset.from_tensor_slices((valid_list, valid_labels))
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.padded_batch(batch_size) # make minibatch
train_ds = train_ds.map(lambda wav_files, labels: tf.py_function(
    wav2mfcc, [wav_files, labels], Tout=[tf.float32, tf.int32]
))
# x,y = next(iter(train_ds))
# single_wav2mfcc(x, y)
def asr_model():
    input = Input((None, 13), name="input")
    h = Masking(mask_value=0.0, input_shape=(None, 13))(input)
    h = Bidirectional(LSTM(256, return_sequences=True), name="bilstm")(h)
    h = Dropout(0.5)(h)
    h = Dense(512, "relu", name="fc")(h)
    h = Dropout(0.5)(h)
    output = Dense(16, "softmax", name="output")(h)
    return Model(input, output)

model = asr_model()
model.summary()

def CTC_Loss(y_true, y_pred):
    len_batch = tf.cast(tf.shape(y_true)[0], dtype="int32")
    len_input = tf.cast(tf.shape(y_pred)[1], dtype="int32")
    len_label = tf.cast(tf.shape(y_true)[1], dtype="int32")
    len_input = len_input * tf.ones((len_batch, 1), dtype="int32") 
    #shape=(len_batch, 1) elements are len_input 每个元素都需告诉长度
    len_label = len_label * tf.ones((len_batch, 1), dtype="int32")
    loss = keras.backend.ctc_batch_cost(y_true, y_pred, len_input, len_label)
    return loss

# Callback for evaluation at each epoch end

# Decording (best path search) from the output of the model
def decode_batch_predictions(pred):
    input_len = np.ones(pred.shape[0]) * pred.shape[1]
    # Use greedy search for get the best path
    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0]
    # Iterate over the results in the minibatch and get back the text
    output_text = []
    for result in results:
        result = tf.strings.reduce_join(num_to_char(result)).numpy().decode("utf-8")
        output_text.append(result)
    return output_text

class ASR_Test(Callback):
    # When an epoch ends, this function will be called
    def on_epoch_end(self, epoch, logs=None):
        predictions = [] # store prediction results
        targets = [] # ground truth
        for batch in valid_ds: # evaluate all the sentences in the validation dataset
            x, y = batch
            preds = model.predict(x) # mini-batch prediction
            preds = decode_batch_predictions(preds) # decode the model output
            predictions.extend(preds)
            for label in y:
                label = tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")
                targets.append(label)

        cer_score = cer(targets, predictions) # calculate phoneme error rate using the jiwer library
        print("-" * 100)
        print(f"Character Error Rate: {cer_score:.4f}")
        print("-" * 100)
        # Rondomly select 5 sentence from the validation dataset
        for i in np.random.randint(0, len(predictions), 5):
            print(f"Target    : {targets[i]}")
            print(f"Prediction: {predictions[i]}")
            print("-" * 100)
model.compile(optimizer=Adam(0.0001), loss=CTC_Loss) # set a optimizer and CTC loss

# run training
model.fit(train_ds, validation_data=valid_ds, initial_epoch=0, epochs=50, callbacks=[ASR_Test()])

# ASR test
file = 'numvoice/test/eight/15574821_nohash_1.wav'

def getmfcc(wav_file):
    rate, samples = read(wav_file)
    feat = librosa.feature.mfcc(y=np.float32(samples), sr=rate, n_mfcc=13, n_fft=512, hop_length=160)
    f = np.average(feat)
    s = np.std(feat)
    feat = (feat.T - f) / s # transformation and normalization
    return feat

feat = getmfcc(file)
feat = np.expand_dims(feat, axis=0) # add batch dim.
preds = model2.predict(feat) # mini-batch prediction
preds = decode_batch_predictions(preds) # decode the model output
print('ASR result:', preds)
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使用多头注意力机制进行ASR

import tensorflow as tf
import numpy as np
import librosa
import matplotlib.pyplot as plt # for draw images
from tensorflow import keras
from tensorflow.keras.layers import Input, Dense, Dropout, LSTM, Bidirectional, Masking, MultiHeadAttention, LayerNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint
from tensorflow.keras.callbacks import LearningRateScheduler
from tensorflow.keras.layers import StringLookup # A preprocessing layer which maps string features to integer indices
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.callbacks import Callback, TensorBoard

import matplotlib.pyplot as plt
from scipy.io.wavfile import read
import os
import glob

from jiwer import cer # for caluculating character error rate

tf.test.gpu_device_name() # confirm for using GPU
# The set of characters accepted in the transcription.
characters = [x for x in 'efghinorstuvwxz'] # limited the alphabet set consisting of number words (from "one" to "nine")
# Mapping characters to integers
char_to_num = StringLookup(vocabulary=characters, oov_token='')
# Mapping integers back to original characters
num_to_char = StringLookup(vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True)

print(
    f'The vocabulary set is: {char_to_num.get_vocabulary()}'
    f' (size={char_to_num.vocabulary_size()})'
)

# Get the file list and labels
def get_filelist_and_label(dir):
    file_list = []
    label_list = []
    for d in os.listdir(dir):
        for f in glob.glob(os.path.join(dir, d, '*.wav')):
            file_list.append(f)
            label_list.append(d)
    return file_list, label_list

train_list, train_labels = get_filelist_and_label('numvoice/train')
valid_list, valid_labels = get_filelist_and_label('numvoice/test')
print(train_labels)

# wave file loading and calculate MFCC
def wav2mfcc(wav_files, labels):
    x = [] # for MFCC data record
    y = [] # for label data record
    for wav in wav_files:
        file = bytes.decode(wav.numpy())
        rate, samples = read(file)
        mfcc = librosa.feature.mfcc(y=np.float32(samples), sr=rate, n_mfcc=13, n_fft=512, hop_length=160)
        m = np.average(mfcc)
        s = np.std(mfcc)
        mfcc = (mfcc.T - m) / s # transformation and normalization
        x.append(mfcc)
    for label in labels:
        label = tf.strings.unicode_split(label, input_encoding='UTF-8')
        y.append(char_to_num(label))
    x = pad_sequences(x, padding='post', value=0.0, dtype=np.float32) # zero padding
    y = pad_sequences(y, padding='post', value=0, dtype=np.int32) # zero padding
    return x, y

batch_size = 20
train_ds = tf.data.Dataset.from_tensor_slices((train_list, train_labels))
valid_ds = tf.data.Dataset.from_tensor_slices((valid_list, valid_labels))
train_ds = train_ds.shuffle(len(train_ds))
train_ds = train_ds.padded_batch(batch_size)
train_ds = train_ds.map(lambda wav_files, labels: tf.py_function(
    wav2mfcc, [wav_files, labels], Tout=[tf.float32, tf.int32]
))
valid_ds = valid_ds.padded_batch(batch_size)
valid_ds = valid_ds.map(lambda wav_files, labels: tf.py_function(
    wav2mfcc, [wav_files, labels], Tout=[tf.float32, tf.int32]
))
def asr_model():
    input = Input((None, 13), name="input")
    h = Masking(mask_value=0.0, input_shape=(None, 3))(input)
    h = Dense(128)(h)
    mha = MultiHeadAttention(num_heads=4, key_dim=128)(h, h)
    mha = Dropout(0.1)(mha)
    out1 = LayerNormalization(epsilon=1e-6)(mha+h) #mha+h
    ffn = Dense(128)(out1)
    ffn = Dropout(0.1)(ffn)
    out2 = LayerNormalization(epsilon=1e-6)(ffn+out1)

    h = Bidirectional(LSTM(128, return_sequences=True))(out2)
    h = Dropout(0.1)(h)
    h = Dense(128, "relu")(h)
    h = Dropout(0.1)(h)
    output = Dense(16, "softmax")(h)
    return Model(input, output)

model = asr_model()
model.summary()
tf.keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, to_file='asr_transformer.png')

# Definition of CTC loss
def CTC_Loss(y_true, y_pred):
    # Compute the training-time loss value
    batch_len = tf.cast(tf.shape(y_true)[0], dtype='int32') # calcurate batch_len with tensor format
    input_length = tf.cast(tf.shape(y_pred)[1], dtype='int32')
    label_length = tf.cast(tf.shape(y_true)[1], dtype='int32')
    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype='int32')
    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype='int32')
    # CTC loss calculation: using the ctc function
    loss = keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)
    return loss

# Callback for evaluation at each epoch end

# Decording (best path search) from the output of the model
def decode_batch_predictions(pred):
    input_len = np.ones(pred.shape[0]) * pred.shape[1]
    # Use greedy search
    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0]
    # Iterate over the results in the minibatch and get back the text
    output_text = []
    for result in results:
        result = tf.strings.reduce_join(num_to_char(result)).numpy().decode("utf-8")
        output_text.append(result)
    return output_text

class ASR_Test(Callback):
    # When an epoch ends, this function will be called
    def on_epoch_end(self, epoch, logs=None):
        predictions = [] # store prediction results
        targets = [] # ground truth
        for batch in valid_ds: # evaluate all the sentences in the validation dataset
            x, y = batch
            preds = model.predict(x) # mini-batch prediction
            preds = decode_batch_predictions(preds) # decode the model output
            predictions.extend(preds)
            for label in y:
                label = tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")
                targets.append(label)

        cer_score = cer(targets, predictions) # calculate phoneme error rate using the jiwer library
        print("-" * 100)
        print(f"Character Error Rate: {cer_score:.4f}")
        print("-" * 100)
        # Rondomly select 5 sentence from the validation dataset
        for i in np.random.randint(0, len(predictions), 5):
            print(f"Target    : {targets[i]}")
            print(f"Prediction: {predictions[i]}")
            print("-" * 100)

# training loop
model.compile(optimizer=Adam(0.0001), loss=CTC_Loss)
model.fit(train_ds, validation_data=valid_ds, initial_epoch=0, epochs=50, callbacks=[ASR_Test()])
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1. 变分推断初探 ↩︎