IMAGE_WIDTH = 28
IMAGE_HEIGHT = 28
IMAGE_CHANNELS = 1
BATCH_SIZE = 128
LATENT_DIMENSION = 100
IMAGE_SHAPE = (IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)
EPOCHS = 8000DCGAN
computervision
deeplearning
keras
python
tensorflow
Implementation of Deep Convolutional GAN using Keras and Tensorflow
Project Repository: https://github.com/soumik12345/Adventures-with-GANS
::: {#cell-2 .cell _cell_guid=‘b1076dfc-b9ad-4769-8c92-a6c4dae69d19’ _uuid=‘8f2839f25d086af736a60e9eeb907d3b93b6e0e5’ execution_count=1}
import warnings
warnings.filterwarnings('ignore'):::
::: {#cell-3 .cell _cell_guid=‘79c7e3d0-c299-4dcb-8224-4455121ee9b0’ _uuid=‘d629ff2d2480ee46fbb7e2d37f6b5fab8052498a’ execution_count=2}
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import *
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam
from keras.utils.vis_utils import model_to_dot
from IPython.display import SVG
from tqdm import tqdm:::
def load_data():
(x_train, _), (_, _) = mnist.load_data()
x_train = x_train / 127.5 - 1.
x_train = np.expand_dims(x_train, axis = 3)
return x_trainx_train = load_data()
x_train.shapeDownloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step(60000, 28, 28, 1)def build_generator(latent_dimension, optimizer):
generator = Sequential([
Dense(256, input_dim = latent_dimension, activation = 'tanh'),
Dense(128 * 7 * 7),
BatchNormalization(),
Activation('tanh'),
Reshape((7, 7, 128)),
UpSampling2D(size = (2, 2)),
Conv2D(64, (5, 5), padding = 'same', activation = 'tanh'),
UpSampling2D(size = (2, 2)),
Conv2D(1, (5, 5), padding = 'same', activation = 'tanh')
])
generator.compile(loss = 'binary_crossentropy', optimizer = optimizer)
return generatordef build_discriminator(image_shape, optimizer):
discriminator = Sequential([
Conv2D(64, (5, 5), padding = 'same', input_shape = image_shape, activation = 'tanh'),
MaxPooling2D(pool_size = (2, 2)),
Conv2D(128, (5, 5), activation = 'tanh'),
MaxPooling2D(pool_size = (2, 2)),
Flatten(),
Dense(1024, activation = 'tanh'),
Dense(1, activation = 'sigmoid')
])
discriminator.compile(loss = 'binary_crossentropy', optimizer = optimizer)
return discriminatordef build_gan(generator, discriminator, latent_dimension, optimizer):
discriminator.trainable = False
gan_input = Input(shape = (latent_dimension, ))
x = generator(gan_input)
gan_output = discriminator(x)
gan = Model(gan_input, gan_output, name = 'GAN')
gan.compile(loss = 'binary_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
return ganoptimizer = Adam(0.0002, 0.5)WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.generator = build_generator(LATENT_DIMENSION, optimizer)
generator.summary()_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 256) 25856
_________________________________________________________________
dense_1 (Dense) (None, 6272) 1611904
_________________________________________________________________
batch_normalization_v1 (Batc (None, 6272) 25088
_________________________________________________________________
activation (Activation) (None, 6272) 0
_________________________________________________________________
reshape (Reshape) (None, 7, 7, 128) 0
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 14, 14, 128) 0
_________________________________________________________________
conv2d (Conv2D) (None, 14, 14, 64) 204864
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 28, 28, 64) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 28, 28, 1) 1601
=================================================================
Total params: 1,869,313
Trainable params: 1,856,769
Non-trainable params: 12,544
_________________________________________________________________SVG(model_to_dot(generator, show_shapes = True, show_layer_names = True).create(prog = 'dot', format = 'svg'))
discriminator = build_discriminator(IMAGE_SHAPE, optimizer)
discriminator.summary()_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 28, 28, 64) 1664
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 14, 14, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 10, 10, 128) 204928
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 128) 0
_________________________________________________________________
flatten (Flatten) (None, 3200) 0
_________________________________________________________________
dense_2 (Dense) (None, 1024) 3277824
_________________________________________________________________
dense_3 (Dense) (None, 1) 1025
=================================================================
Total params: 3,485,441
Trainable params: 3,485,441
Non-trainable params: 0
_________________________________________________________________SVG(model_to_dot(discriminator, show_shapes = True, show_layer_names = True).create(prog = 'dot', format = 'svg'))
gan = build_gan(generator, discriminator, LATENT_DIMENSION, optimizer)
gan.summary()_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 100) 0
_________________________________________________________________
sequential (Sequential) (None, 28, 28, 1) 1869313
_________________________________________________________________
sequential_1 (Sequential) (None, 1) 3485441
=================================================================
Total params: 5,354,754
Trainable params: 1,856,769
Non-trainable params: 3,497,985
_________________________________________________________________SVG(model_to_dot(gan, show_shapes = True, show_layer_names = True).create(prog = 'dot', format = 'svg'))
def plot_images(nrows, ncols, figsize, generator):
fig, axes = plt.subplots(nrows = nrows, ncols = ncols, figsize = figsize)
plt.setp(axes.flat, xticks = [], yticks = [])
noise = np.random.normal(0, 1, (nrows * ncols, LATENT_DIMENSION))
generated_images = generator.predict(noise).reshape(nrows * ncols, IMAGE_WIDTH, IMAGE_HEIGHT)
for i, ax in enumerate(axes.flat):
ax.imshow(generated_images[i], cmap = 'gray')
plt.show()generator_loss_history, discriminator_loss_history = [], []
for epoch in tqdm(range(1, EPOCHS + 1)):
# Select a random batch of images from training data
index = np.random.randint(0, x_train.shape[0], BATCH_SIZE)
batch_images = x_train[index]
# Adversarial Noise
noise = np.random.normal(0, 1, (BATCH_SIZE, LATENT_DIMENSION))
# Generate fake images
generated_images = generator.predict(noise)
# Construct batches of real and fake data
x = np.concatenate([batch_images, generated_images])
# Labels for training the discriminator
y_discriminator = np.zeros(2 * BATCH_SIZE)
y_discriminator[: BATCH_SIZE] = 0.9
# train the discrimator to distinguish between fake data and real data
discriminator.trainable = True
discriminator_loss = discriminator.train_on_batch(x, y_discriminator)
discriminator_loss_history.append(discriminator_loss)
discriminator.trainable = False
# Training the GAN
generator_loss = gan.train_on_batch(noise, np.ones(BATCH_SIZE))
generator_loss_history.append(generator_loss)
if epoch % 1000 == 0:
plot_images(1, 8, (16, 4), generator)WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
plot_images(2, 8, (16, 6), generator)
generator.save('./generator.h5')