Keras入門——（3）生成式對抗網絡GAN

時間 2019-11-12

原文原文鏈接

導入 matplotlib 模塊：html

import matplotlib

查看本身版本所支持的backends：app

print(matplotlib.rcsetup.all_backends)

返回信息：dom

['GTK3Agg', 'GTK3Cairo', 'MacOSX', 'nbAgg', 'Qt4Agg', 'Qt4Cairo', 'Qt5Agg', 'Qt5Cairo', 'TkAgg', 'TkCairo', 'WebAgg', 'WX', 'WXAgg', 'WXCairo', 'agg', 'cairo', 'pdf', 'pgf', 'ps', 'svg', 'template']

查看當前工做的matplotlibrc文件是哪一個：svg

print(matplotlib.matplotlib_fname())

返回信息：spa

D:\ProgramData\Anaconda2\lib\site-packages\matplotlib\mpl-data\matplotlibrc

打開 matplotlibrc 查看相應內容： .net

將 backend 修改成 TkAgg：3d

執行以下代碼：code

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from keras.models import Model
from keras.layers import Dense, Activation, Input, Reshape
from keras.layers import Conv1D, Flatten, Dropout
from keras.optimizers import SGD, Adam

def sample_data(n_samples=10000, x_vals=np.arange(0, 5, .1), max_offset=100, mul_range=[1, 2]):
    vectors = []
    for i in range(n_samples):
        offset = np.random.random() * max_offset
        mul = mul_range[0] + np.random.random() * (mul_range[1] - mul_range[0])
        vectors.append(
            np.sin(offset + x_vals * mul) / 2 + .5
        )
    return np.array(vectors)

ax = pd.DataFrame(np.transpose(sample_data(5))).plot()
plt.show()

生成圖像：orm

執行代碼：htm

def get_generative(G_in, dense_dim=200, out_dim=50, lr=1e-3):
    x = Dense(dense_dim)(G_in)
    x = Activation('tanh')(x)
    G_out = Dense(out_dim, activation='tanh')(x)
    G = Model(G_in, G_out)
    opt = SGD(lr=lr)
    G.compile(loss='binary_crossentropy', optimizer=opt)
    return G, G_out

G_in = Input(shape=[10])
G, G_out = get_generative(G_in)
G.summary()

生成圖像：

執行代碼：

def get_discriminative(D_in, lr=1e-3, drate=.25, n_channels=50, conv_sz=5, leak=.2):
    x = Reshape((-1, 1))(D_in)
    x = Conv1D(n_channels, conv_sz, activation='relu')(x)
    x = Dropout(drate)(x)
    x = Flatten()(x)
    x = Dense(n_channels)(x)
    D_out = Dense(2, activation='sigmoid')(x)
    D = Model(D_in, D_out)
    dopt = Adam(lr=lr)
    D.compile(loss='binary_crossentropy', optimizer=dopt)
    return D, D_out

D_in = Input(shape=[50])
D, D_out = get_discriminative(D_in)
D.summary()

生成圖像：

執行代碼：

def set_trainability(model, trainable=False):
    model.trainable = trainable
    for layer in model.layers:
        layer.trainable = trainable
        
def make_gan(GAN_in, G, D):
    set_trainability(D, False)
    x = G(GAN_in)
    GAN_out = D(x)
    GAN = Model(GAN_in, GAN_out)
    GAN.compile(loss='binary_crossentropy', optimizer=G.optimizer)
    return GAN, GAN_out

GAN_in = Input([10])
GAN, GAN_out = make_gan(GAN_in, G, D)
GAN.summary()

生成圖像：

執行代碼：

def sample_data_and_gen(G, noise_dim=10, n_samples=10000):
    XT = sample_data(n_samples=n_samples)
    XN_noise = np.random.uniform(0, 1, size=[n_samples, noise_dim])
    XN = G.predict(XN_noise)
    X = np.concatenate((XT, XN))
    y = np.zeros((2*n_samples, 2))
    y[:n_samples, 1] = 1
    y[n_samples:, 0] = 1
    return X, y

def pretrain(G, D, noise_dim=10, n_samples=10000, batch_size=32):
    X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
    set_trainability(D, True)
    D.fit(X, y, epochs=1, batch_size=batch_size)

pretrain(G, D)

返回信息：

Epoch 1/1

   32/20000 [..............................] - ETA: 6:42 - loss: 0.7347
  288/20000 [..............................] - ETA: 47s - loss: 0.4808 
  544/20000 [..............................] - ETA: 26s - loss: 0.3318
  800/20000 [>.............................] - ETA: 19s - loss: 0.2359
 1056/20000 [>.............................] - ETA: 15s - loss: 0.1805
 1312/20000 [>.............................] - ETA: 12s - loss: 0.1459
 1568/20000 [=>............................] - ETA: 11s - loss: 0.1223
 1824/20000 [=>............................] - ETA: 10s - loss: 0.1053
 2048/20000 [==>...........................] - ETA: 9s - loss: 0.0938 
 2272/20000 [==>...........................] - ETA: 8s - loss: 0.0847
 2528/20000 [==>...........................] - ETA: 8s - loss: 0.0761
 2784/20000 [===>..........................] - ETA: 7s - loss: 0.0692
 3040/20000 [===>..........................] - ETA: 7s - loss: 0.0634
 3296/20000 [===>..........................] - ETA: 6s - loss: 0.0585
 3552/20000 [====>.........................] - ETA: 6s - loss: 0.0543
 3808/20000 [====>.........................] - ETA: 6s - loss: 0.0507
 4064/20000 [=====>........................] - ETA: 5s - loss: 0.0475
 4352/20000 [=====>........................] - ETA: 5s - loss: 0.0444
 4608/20000 [=====>........................] - ETA: 5s - loss: 0.0420
 4864/20000 [======>.......................] - ETA: 5s - loss: 0.0398
 5120/20000 [======>.......................] - ETA: 4s - loss: 0.0378
 5376/20000 [=======>......................] - ETA: 4s - loss: 0.0360
 5632/20000 [=======>......................] - ETA: 4s - loss: 0.0344
 5888/20000 [=======>......................] - ETA: 4s - loss: 0.0329
 6144/20000 [========>.....................] - ETA: 4s - loss: 0.0315
 6400/20000 [========>.....................] - ETA: 4s - loss: 0.0303
 6656/20000 [========>.....................] - ETA: 4s - loss: 0.0291
 6880/20000 [=========>....................] - ETA: 3s - loss: 0.0282
 7136/20000 [=========>....................] - ETA: 3s - loss: 0.0272
 7392/20000 [==========>...................] - ETA: 3s - loss: 0.0262
 7648/20000 [==========>...................] - ETA: 3s - loss: 0.0254
 7904/20000 [==========>...................] - ETA: 3s - loss: 0.0246
 8160/20000 [===========>..................] - ETA: 3s - loss: 0.0238
 8416/20000 [===========>..................] - ETA: 3s - loss: 0.0231
 8672/20000 [============>.................] - ETA: 3s - loss: 0.0224
 8928/20000 [============>.................] - ETA: 3s - loss: 0.0218
 9184/20000 [============>.................] - ETA: 2s - loss: 0.0212
 9440/20000 [=============>................] - ETA: 2s - loss: 0.0206
 9696/20000 [=============>................] - ETA: 2s - loss: 0.0200
 9952/20000 [=============>................] - ETA: 2s - loss: 0.0195
10208/20000 [==============>...............] - ETA: 2s - loss: 0.0190
10464/20000 [==============>...............] - ETA: 2s - loss: 0.0186
10720/20000 [===============>..............] - ETA: 2s - loss: 0.0181
10976/20000 [===============>..............] - ETA: 2s - loss: 0.0177
11232/20000 [===============>..............] - ETA: 2s - loss: 0.0173
11488/20000 [================>.............] - ETA: 2s - loss: 0.0169
11712/20000 [================>.............] - ETA: 2s - loss: 0.0166
11968/20000 [================>.............] - ETA: 2s - loss: 0.0163
12224/20000 [=================>............] - ETA: 2s - loss: 0.0159
12480/20000 [=================>............] - ETA: 1s - loss: 0.0156
12736/20000 [==================>...........] - ETA: 1s - loss: 0.0153
12992/20000 [==================>...........] - ETA: 1s - loss: 0.0150
13248/20000 [==================>...........] - ETA: 1s - loss: 0.0147
13504/20000 [===================>..........] - ETA: 1s - loss: 0.0144
13760/20000 [===================>..........] - ETA: 1s - loss: 0.0141
14016/20000 [====================>.........] - ETA: 1s - loss: 0.0139
14272/20000 [====================>.........] - ETA: 1s - loss: 0.0136
14528/20000 [====================>.........] - ETA: 1s - loss: 0.0134
14784/20000 [=====================>........] - ETA: 1s - loss: 0.0132
15040/20000 [=====================>........] - ETA: 1s - loss: 0.0129
15296/20000 [=====================>........] - ETA: 1s - loss: 0.0127
15552/20000 [======================>.......] - ETA: 1s - loss: 0.0125
15808/20000 [======================>.......] - ETA: 1s - loss: 0.0123
16064/20000 [=======================>......] - ETA: 0s - loss: 0.0121
16320/20000 [=======================>......] - ETA: 0s - loss: 0.0119
16576/20000 [=======================>......] - ETA: 0s - loss: 0.0118
16832/20000 [========================>.....] - ETA: 0s - loss: 0.0116
17088/20000 [========================>.....] - ETA: 0s - loss: 0.0114
17344/20000 [=========================>....] - ETA: 0s - loss: 0.0112
17600/20000 [=========================>....] - ETA: 0s - loss: 0.0111
17856/20000 [=========================>....] - ETA: 0s - loss: 0.0109
18144/20000 [==========================>...] - ETA: 0s - loss: 0.0107
18400/20000 [==========================>...] - ETA: 0s - loss: 0.0106
18656/20000 [==========================>...] - ETA: 0s - loss: 0.0104
18912/20000 [===========================>..] - ETA: 0s - loss: 0.0103
19168/20000 [===========================>..] - ETA: 0s - loss: 0.0102
19456/20000 [============================>.] - ETA: 0s - loss: 0.0100
19712/20000 [============================>.] - ETA: 0s - loss: 0.0099
19968/20000 [============================>.] - ETA: 0s - loss: 0.0098
20000/20000 [==============================] - 5s 236us/step - loss: 0.0097

引入模塊：

from tqdm import tqdm_notebook as tqdm

執行代碼：

def sample_noise(G, noise_dim=10, n_samples=10000):
    X = np.random.uniform(0, 1, size=[n_samples, noise_dim])
    y = np.zeros((n_samples, 2))
    y[:, 1] = 1
    return X, y


def train(GAN, G, D, epochs=200, n_samples=10000, noise_dim=10, batch_size=32, verbose=False, v_freq=50):
    d_loss = []
    g_loss = []
    e_range = range(epochs)
    if verbose:
        e_range = tqdm(e_range)
    for epoch in e_range:
        X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
        set_trainability(D, True)
        d_loss.append(D.train_on_batch(X, y))

        X, y = sample_noise(G, n_samples=n_samples, noise_dim=noise_dim)
        set_trainability(D, False)
        g_loss.append(GAN.train_on_batch(X, y))
        if verbose and (epoch + 1) % v_freq == 0:
            print("Epoch #{}: Generative Loss: {}, Discriminative Loss: {}".format(epoch + 1, g_loss[-1], d_loss[-1]))
    return d_loss, g_loss


d_loss, g_loss = train(GAN, G, D, verbose=True)

返回信息：

HBox(children=(IntProgress(value=0, max=200), HTML(value='')))
Epoch #50: Generative Loss: 5.842154026031494, Discriminative Loss: 0.4683375060558319
Epoch #100: Generative Loss: 3.4111320972442627, Discriminative Loss: 0.13123030960559845
Epoch #150: Generative Loss: 5.5205817222595215, Discriminative Loss: 0.03762095794081688
Epoch #200: Generative Loss: 4.994686603546143, Discriminative Loss: 0.045186348259449005

執行代碼：

ax = pd.DataFrame(
    {
        'Generative Loss': g_loss,
        'Discriminative Loss': d_loss,
    }
).plot(title='Training loss', logy=True)
ax.set_xlabel("Epochs")
ax.set_ylabel("Loss")
plt.show()

生成圖像：

執行代碼：

N_VIEWED_SAMPLES = 2
data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)
pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).plot()
plt.show()

生成圖像：

執行代碼：

N_VIEWED_SAMPLES = 2
data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)
pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).rolling(5).mean()[5:].plot()
plt.show()

生成圖像：

完整代碼以下：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from keras.models import Model
from keras.layers import Dense, Activation, Input, Reshape
from keras.layers import Conv1D, Flatten, Dropout
from keras.optimizers import SGD, Adam

from tqdm import tqdm_notebook as tqdm

#sec
def sample_data(n_samples=10000, x_vals=np.arange(0, 5, .1), max_offset=100, mul_range=[1, 2]):
    vectors = []
    for i in range(n_samples):
        offset = np.random.random() * max_offset
        mul = mul_range[0] + np.random.random() * (mul_range[1] - mul_range[0])
        vectors.append(
            np.sin(offset + x_vals * mul) / 2 + .5
        )
    return np.array(vectors)

ax = pd.DataFrame(np.transpose(sample_data(5))).plot()
plt.show()

#sec
def get_generative(G_in, dense_dim=200, out_dim=50, lr=1e-3):
    x = Dense(dense_dim)(G_in)
    x = Activation('tanh')(x)
    G_out = Dense(out_dim, activation='tanh')(x)
    G = Model(G_in, G_out)
    opt = SGD(lr=lr)
    G.compile(loss='binary_crossentropy', optimizer=opt)
    return G, G_out

G_in = Input(shape=[10])
G, G_out = get_generative(G_in)
G.summary()

#sec
def get_discriminative(D_in, lr=1e-3, drate=.25, n_channels=50, conv_sz=5, leak=.2):
    x = Reshape((-1, 1))(D_in)
    x = Conv1D(n_channels, conv_sz, activation='relu')(x)
    x = Dropout(drate)(x)
    x = Flatten()(x)
    x = Dense(n_channels)(x)
    D_out = Dense(2, activation='sigmoid')(x)
    D = Model(D_in, D_out)
    dopt = Adam(lr=lr)
    D.compile(loss='binary_crossentropy', optimizer=dopt)
    return D, D_out

D_in = Input(shape=[50])
D, D_out = get_discriminative(D_in)
D.summary()

#sec
def set_trainability(model, trainable=False):
    model.trainable = trainable
    for layer in model.layers:
        layer.trainable = trainable


def make_gan(GAN_in, G, D):
    set_trainability(D, False)
    x = G(GAN_in)
    GAN_out = D(x)
    GAN = Model(GAN_in, GAN_out)
    GAN.compile(loss='binary_crossentropy', optimizer=G.optimizer)
    return GAN, GAN_out


GAN_in = Input([10])
GAN, GAN_out = make_gan(GAN_in, G, D)
GAN.summary()

#sec
def sample_data_and_gen(G, noise_dim=10, n_samples=10000):
    XT = sample_data(n_samples=n_samples)
    XN_noise = np.random.uniform(0, 1, size=[n_samples, noise_dim])
    XN = G.predict(XN_noise)
    X = np.concatenate((XT, XN))
    y = np.zeros((2*n_samples, 2))
    y[:n_samples, 1] = 1
    y[n_samples:, 0] = 1
    return X, y

def pretrain(G, D, noise_dim=10, n_samples=10000, batch_size=32):
    X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
    set_trainability(D, True)
    D.fit(X, y, epochs=1, batch_size=batch_size)

pretrain(G, D)

#sec
def sample_noise(G, noise_dim=10, n_samples=10000):
    X = np.random.uniform(0, 1, size=[n_samples, noise_dim])
    y = np.zeros((n_samples, 2))
    y[:, 1] = 1
    return X, y


def train(GAN, G, D, epochs=200, n_samples=10000, noise_dim=10, batch_size=32, verbose=False, v_freq=50):
    d_loss = []
    g_loss = []
    e_range = range(epochs)
    if verbose:
        e_range = tqdm(e_range)
    for epoch in e_range:
        X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
        set_trainability(D, True)
        d_loss.append(D.train_on_batch(X, y))

        X, y = sample_noise(G, n_samples=n_samples, noise_dim=noise_dim)
        set_trainability(D, False)
        g_loss.append(GAN.train_on_batch(X, y))
        if verbose and (epoch + 1) % v_freq == 0:
            print("Epoch #{}: Generative Loss: {}, Discriminative Loss: {}".format(epoch + 1, g_loss[-1], d_loss[-1]))
    return d_loss, g_loss


d_loss, g_loss = train(GAN, G, D, verbose=True)

#sec
ax = pd.DataFrame(
    {
        'Generative Loss': g_loss,
        'Discriminative Loss': d_loss,
    }
).plot(title='Training loss', logy=True)
ax.set_xlabel("Epochs")
ax.set_ylabel("Loss")
plt.show()

#sec
N_VIEWED_SAMPLES = 2
data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)
pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).plot()
plt.show()

#sec
N_VIEWED_SAMPLES = 2
data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)
pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).rolling(5).mean()[5:].plot()
plt.show()