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program18_Keras_CNN.py
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program18_Keras_CNN.py
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# we use CNNs, we use Keras
# import Keras layers
from keras import layers
# import Keras models
from keras import models
# we use Sequential
model = models.Sequential()
# we use: "input_shape = (28, 28, 1)"
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
#print(model.summary())
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
print(model.summary())
# we use the MNIST dataset
# import MNIST
from keras.datasets import mnist
# import categorical
from keras.utils import to_categorical
# define the training data and the test data
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
#from keras.datasets import mnist
# we use tuples, (..., ...)
#(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
#print('')
#print(train_images.ndim)
#print(train_images.shape)
#print(train_images.dtype)
#digit = train_images[4]
#import matplotlib.pyplot as plt
#plt.imshow(digit, cmap=plt.cm.binary)
#plt.show()
#plt.close()
#my_slice = train_images[10:100]
#print('')
#print(my_slice.shape)
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
# we find the accuracy
test_loss, test_acc = model.evaluate(test_images, test_labels)
# accuracy
print(test_acc)
# Deep Generative Models
# GANs and VAEs, Generative Models
# random noise
# from random noise to a tensor
# We use batch normalisation.
# GANs are very difficult to train. Super-deep models. This is why we use batch normalisation.
# GANs and LSTM RNNs
# use LSTM RNNs together with GANs
# combine the power of LSTM RNNs and GANs
# it is possible to use LSTM RNN together with GANs
# https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
# https://github.com/life-efficient/Academy-of-AI/tree/master/Lecture%2013%20-%20Generative%20Models
# https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
# Anomaly detection (AD)
# Unsupervised machine learning
# GANs for super-resolution
# Generative Adversarial Networks, GANs
# the BigGAN dataset
# BigGAN => massive dataset
# latent space, BigGAN, GANs
# down-sampling, sub-sample, pooling
# throw away samples, pooling, max-pooling
# partial derivatives
# loss function and partial derivatives
# https://github.com/Students-for-AI/The-Academy-of-AI
# https://github.com/life-efficient/Academy-of-AI/tree/master/Lecture%2013%20-%20Generative%20Models
# Generator G and Discriminator D
# the loss function of the Generator G
# up-convolution
# We use a filter we do up-convolution with.
# use batch normalisation
# GANs are very difficult to train and this is why we use batch normalisation.
# We normalize across a batch.
# Mean across a batch. We use batches. Normalize across a batch.
# the ReLU activation function
# ReLU is the most common activation function. We use ReLU.
# use: https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
# we use PyTorch
import torch
#import torch
import torchvision
from torchvision import datasets, transforms
# use matplotlib
import matplotlib.pyplot as plt
#import torch
#import torchvision
#from torchvision import transforms, datasets
import torch.nn.functional as F
#import matplotlib.pyplot as plt
#batch_size = 128
# download the training dataset
#train_data = datasets.FashionMNIST(root='fashiondata/',
# transform=transforms.ToTensor(),
# train=True,
# download=True)
# we create the train data loader
#train_loader = torch.utils.data.DataLoader(train_data,
# shuffle=True,
# batch_size=batch_size)
# define the batch size
batch_size = 100
train_data = datasets.FashionMNIST(root='fashiondata/',
transform=transforms.ToTensor(),
train=True,
download=True
)
train_samples = torch.utils.data.DataLoader(dataset=train_data,
batch_size=batch_size,
shuffle=True
)
# combine the power of LSTM RNNs and GANs
# it is possible to use LSTM RNN together with GANs
# GANs and LSTM RNNs
# use LSTM RNNs together with GANs
# class for D and G
# we train the discriminator and the generator
# we make the discriminator
class discriminator(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 64, kernel_size=4, stride=2, padding=1) # 1x28x28-> 64x14x14
self.conv2 = torch.nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1) # 64x14x14-> 128x7x7
self.dense1 = torch.nn.Linear(128 * 7 * 7, 1)
self.bn1 = torch.nn.BatchNorm2d(64)
self.bn2 = torch.nn.BatchNorm2d(128)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x))).view(-1, 128 * 7 * 7)
x = F.sigmoid(self.dense1(x))
return x
# this was for the discriminator
# we now do the same for the generator
# Generator G
class generator(torch.nn.Module):
def __init__(self):
super().__init__()
self.dense1 = torch.nn.Linear(128, 256)
self.dense2 = torch.nn.Linear(256, 1024)
self.dense3 = torch.nn.Linear(1024, 128 * 7 * 7)
self.uconv1 = torch.nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1) # 128x7x7 -> 64x14x14
self.uconv2 = torch.nn.ConvTranspose2d(64, 1, kernel_size=4, stride=2, padding=1) # 64x14x14 -> 1x28x28
self.bn1 = torch.nn.BatchNorm1d(256)
self.bn2 = torch.nn.BatchNorm1d(1024)
self.bn3 = torch.nn.BatchNorm1d(128 * 7 * 7)
self.bn4 = torch.nn.BatchNorm2d(64)
def forward(self, x):
x = F.relu(self.bn1(self.dense1(x)))
x = F.relu(self.bn2(self.dense2(x)))
x = F.relu(self.bn3(self.dense3(x))).view(-1, 128, 7, 7)
x = F.relu(self.bn4(self.uconv1(x)))
x = F.sigmoid(self.uconv2(x))
return x
# https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
# use: https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
# instantiate the model
d = discriminator()
g = generator()
# training hyperparameters
#epochs = 100
#epochs = 100
epochs = 10
# learning rate
#dlr = 0.0003
#glr = 0.0003
dlr = 0.003
glr = 0.003
d_optimizer = torch.optim.Adam(d.parameters(), lr=dlr)
g_optimizer = torch.optim.Adam(g.parameters(), lr=glr)
dcosts = []
gcosts = []
plt.ion()
fig = plt.figure()
loss_ax = fig.add_subplot(121)
loss_ax.set_xlabel('Batch')
loss_ax.set_ylabel('Cost')
loss_ax.set_ylim(0, 0.2)
generated_img = fig.add_subplot(122)
plt.show()
# https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
# https://github.com/life-efficient/Academy-of-AI/tree/master/Lecture%2013%20-%20Generative%20Models
# use: https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
def train(epochs, glr, dlr):
g_losses = []
d_losses = []
for epoch in range(epochs):
# iteratre over mini-batches
for batch_idx, (real_images, _) in enumerate(train_samples):
z = torch.randn(batch_size, 128) # generate random latent variable to generate images from
generated_images = g.forward(z) # generate images
gen_pred = d.forward(generated_images) # prediction of discriminator on generated batch
real_pred = d.forward(real_images) # prediction of discriminator on real batch
dcost = -torch.sum(torch.log(real_pred)) - torch.sum(torch.log(1 - gen_pred)) # cost of discriminator
gcost = -torch.sum(torch.log(gen_pred)) / batch_size # cost of generator
# train discriminator
d_optimizer.zero_grad()
dcost.backward(retain_graph=True) # retain the computational graph so we can train generator after
d_optimizer.step()
# train generator
g_optimizer.zero_grad()
gcost.backward()
g_optimizer.step()
# give us an example of a generated image after every 10000 images produced
#if batch_idx * batch_size % 10000 == 0:
# give us an example of a generated image after every 20 images produced
if batch_idx % 20 == 0:
g.eval() # put in evaluation mode
noise_input = torch.randn(1, 128)
generated_image = g.forward(noise_input)
generated_img.imshow(generated_image.detach().squeeze(), cmap='gray_r')
# pause for some seconds
plt.pause(5)
# put back into training mode
g.train()
dcost /= batch_size
gcost /= batch_size
print('Epoch: ', epoch, 'Batch idx:', batch_idx, '\tDisciminator cost: ', dcost.item(),
'\tGenerator cost: ', gcost.item())
dcosts.append(dcost)
gcosts.append(gcost)
loss_ax.plot(dcosts, 'b')
loss_ax.plot(gcosts, 'r')
fig.canvas.draw()
#print(torch.__version__)
train(epochs, glr, dlr)
# We obtain:
# Epoch: 0 Batch idx: 0 Disciminator cost: 1.3832124471664429 Generator cost: 0.006555716972798109
# Epoch: 0 Batch idx: 1 Disciminator cost: 1.0811840295791626 Generator cost: 0.008780254982411861
# Epoch: 0 Batch idx: 2 Disciminator cost: 0.8481155633926392 Generator cost: 0.011281056329607964
# Epoch: 0 Batch idx: 3 Disciminator cost: 0.6556042432785034 Generator cost: 0.013879001140594482
# Epoch: 0 Batch idx: 4 Disciminator cost: 0.5069876909255981 Generator cost: 0.016225570812821388
# Epoch: 0 Batch idx: 5 Disciminator cost: 0.4130948781967163 Generator cost: 0.018286770209670067
# Epoch: 0 Batch idx: 6 Disciminator cost: 0.33445805311203003 Generator cost: 0.02015063539147377
# Epoch: 0 Batch idx: 7 Disciminator cost: 0.279323011636734 Generator cost: 0.021849267184734344
# Epoch: 0 Batch idx: 8 Disciminator cost: 0.2245958000421524 Generator cost: 0.02352861315011978
# Epoch: 0 Batch idx: 9 Disciminator cost: 0.18664218485355377 Generator cost: 0.025215130299329758
# Epoch: 0 Batch idx: 10 Disciminator cost: 0.14700829982757568 Generator cost: 0.02692217379808426
# Epoch: 0 Batch idx: 32 Disciminator cost: 0.2818330228328705 Generator cost: 0.022729918360710144
# Epoch: 0 Batch idx: 33 Disciminator cost: 0.7310256361961365 Generator cost: 0.05649786815047264
# Epoch: 0 Batch idx: 34 Disciminator cost: 0.31759023666381836 Generator cost: 0.02075548656284809
# Epoch: 0 Batch idx: 35 Disciminator cost: 0.35554683208465576 Generator cost: 0.018939709290862083
# Epoch: 0 Batch idx: 36 Disciminator cost: 0.07700302451848984 Generator cost: 0.04144695773720741
# Epoch: 0 Batch idx: 37 Disciminator cost: 0.08900360018014908 Generator cost: 0.05888563022017479
# Epoch: 0 Batch idx: 38 Disciminator cost: 0.0921328067779541 Generator cost: 0.0593753345310688
# Epoch: 0 Batch idx: 39 Disciminator cost: 0.09943853318691254 Generator cost: 0.05279992148280144
# Epoch: 0 Batch idx: 40 Disciminator cost: 0.2455407679080963 Generator cost: 0.036564696580171585
# Epoch: 0 Batch idx: 41 Disciminator cost: 0.10074597597122192 Generator cost: 0.03721988573670387
# Epoch: 0 Batch idx: 42 Disciminator cost: 0.07906078547239304 Generator cost: 0.04363853484392166
# Epoch: 0 Batch idx: 444 Disciminator cost: 0.06787727028131485 Generator cost: 0.04046594724059105
# Epoch: 0 Batch idx: 445 Disciminator cost: 0.07139576226472855 Generator cost: 0.03837932273745537
# Epoch: 0 Batch idx: 446 Disciminator cost: 0.08202749490737915 Generator cost: 0.039551254361867905
# Epoch: 0 Batch idx: 447 Disciminator cost: 0.12328958511352539 Generator cost: 0.03817861154675484
# Epoch: 0 Batch idx: 448 Disciminator cost: 0.06865841150283813 Generator cost: 0.03938257694244385
# generate random latent variable to generate images
z = torch.randn(batch_size, 128)
# generate images
im = g.forward(z)
# use "forward(.)"
plt.imshow(im)