14.2 Generative Adversarial Networks - DCGAN
import math
import os
import numpy as np
np.random.seed(123)
print("NumPy:{}".format(np.__version__))
import pandas as pd
print("Pandas:{}".format(pd.__version__))
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
rcParams['figure.figsize']=15,10
print("Matplotlib:{}".format(mpl.__version__))
import tensorflow as tf
tf.set_random_seed(123)
print("TensorFlow:{}".format(tf.__version__))
import keras
print("Keras:{}".format(keras.__version__))
NumPy:1.13.1
Pandas:0.21.0
Matplotlib:2.1.0
TensorFlow:1.4.0
Using TensorFlow backend.
Keras:2.0.9
DATASETSLIB_HOME = '../datasetslib'
import sys
if not DATASETSLIB_HOME in sys.path:
sys.path.append(DATASETSLIB_HOME)
%reload_ext autoreload
%autoreload 2
import datasetslib
from datasetslib import util as dsu
datasetslib.datasets_root = os.path.join(os.path.expanduser('~'),'datasets')
Get the MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(os.path.join(datasetslib.datasets_root,'mnist'), one_hot=False)
x_train = mnist.train.images
x_test = mnist.test.images
y_train = mnist.train.labels
y_test = mnist.test.labels
pixel_size = 28
def norm(x):
return (x-0.5)/0.5
Extracting /home/armando/datasets/mnist/train-images-idx3-ubyte.gz
Extracting /home/armando/datasets/mnist/train-labels-idx1-ubyte.gz
Extracting /home/armando/datasets/mnist/t10k-images-idx3-ubyte.gz
Extracting /home/armando/datasets/mnist/t10k-labels-idx1-ubyte.gz
n_z = 256
z_test = np.random.uniform(-1.0,1.0,size=[8,n_z])
def display_images(images):
for i in range(images.shape[0]):
plt.subplot(1, 8, i + 1)
plt.imshow(images[i])
plt.axis('off')
plt.tight_layout()
plt.show()
DCGAN in Keras
import keras
from keras.layers import Dense, Input, LeakyReLU, Activation
from keras.layers import UpSampling2D, Conv2D, Reshape, Flatten, MaxPooling2D
from keras.models import Sequential, Model
tf.reset_default_graph()
keras.backend.clear_session()
g_learning_rate = 0.00001
d_learning_rate = 0.01
n_x = 784
g_n_layers = 3
d_n_layers = 1
g_n_filters = [64,32,16]
d_n_filters = [64]
n_width=28
n_height=28
n_depth=1
g_model = Sequential(name='g')
g_model.add(Dense(units=5*5*128,
input_shape=(n_z,),
name='g_in'
))
g_model.add(Activation('tanh',name='g_in_act'))
g_model.add(Reshape(target_shape=(5,5,128),
input_shape=(5*5*128,),
name='g_in_reshape'
)
)
for i in range(0,g_n_layers):
g_model.add(UpSampling2D(size=[2,2],
name='g_{}_up2d'.format(i)
))
g_model.add(Conv2D(filters=g_n_filters[i],
kernel_size=(5,5),
padding='same',
name='g_{}_conv2d'.format(i)
))
g_model.add(Activation('tanh',name='g_{}_act'.format(i)))
g_model.add(Flatten(name='g_out_flatten'))
g_model.add(Dense(units=n_x, activation='tanh',name='g_out'))
print('Generator:')
g_model.summary()
g_model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(lr=g_learning_rate)
)
d_model = Sequential(name='d')
d_model.add(Reshape(target_shape=(n_width,n_height,n_depth),
input_shape=(n_x,),
name='d_0_reshape'
)
)
for i in range(0,d_n_layers):
d_model.add(Conv2D(filters=d_n_filters[i],
kernel_size=(5,5),
padding='same',
name='d_{}_conv2d'.format(i)
)
)
d_model.add(Activation('tanh',name='d_{}_act'.format(i)))
d_model.add(MaxPooling2D(pool_size=(2,2),
strides=(2,2),
name='d_{}_maxpool'.format(i)
)
)
d_model.add(Flatten(name='d_out_flatten'))
d_model.add(Dense(units=1, activation='sigmoid',name='d_out'))
print('Discriminator:')
d_model.summary()
d_model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.SGD(lr=d_learning_rate)
)
d_model.trainable=False
z_in = Input(shape=(n_z,),name='z_in')
x_in = g_model(z_in)
gan_out = d_model(x_in)
gan_model = Model(inputs=z_in,outputs=gan_out,name='gan')
print('GAN:')
gan_model.summary()
gan_model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(lr=g_learning_rate)
)
Generator:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
g_in (Dense) (None, 3200) 822400
_________________________________________________________________
g_in_act (Activation) (None, 3200) 0
_________________________________________________________________
g_in_reshape (Reshape) (None, 5, 5, 128) 0
_________________________________________________________________
g_0_up2d (UpSampling2D) (None, 10, 10, 128) 0
_________________________________________________________________
g_0_conv2d (Conv2D) (None, 10, 10, 64) 204864
_________________________________________________________________
g_0_act (Activation) (None, 10, 10, 64) 0
_________________________________________________________________
g_1_up2d (UpSampling2D) (None, 20, 20, 64) 0
_________________________________________________________________
g_1_conv2d (Conv2D) (None, 20, 20, 32) 51232
_________________________________________________________________
g_1_act (Activation) (None, 20, 20, 32) 0
_________________________________________________________________
g_2_up2d (UpSampling2D) (None, 40, 40, 32) 0
_________________________________________________________________
g_2_conv2d (Conv2D) (None, 40, 40, 16) 12816
_________________________________________________________________
g_2_act (Activation) (None, 40, 40, 16) 0
_________________________________________________________________
g_out_flatten (Flatten) (None, 25600) 0
_________________________________________________________________
g_out (Dense) (None, 784) 20071184
=================================================================
Total params: 21,162,496
Trainable params: 21,162,496
Non-trainable params: 0
_________________________________________________________________
Discriminator:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
d_0_reshape (Reshape) (None, 28, 28, 1) 0
_________________________________________________________________
d_0_conv2d (Conv2D) (None, 28, 28, 64) 1664
_________________________________________________________________
d_0_act (Activation) (None, 28, 28, 64) 0
_________________________________________________________________
d_0_maxpool (MaxPooling2D) (None, 14, 14, 64) 0
_________________________________________________________________
d_out_flatten (Flatten) (None, 12544) 0
_________________________________________________________________
d_out (Dense) (None, 1) 12545
=================================================================
Total params: 14,209
Trainable params: 14,209
Non-trainable params: 0
_________________________________________________________________
GAN:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_in (InputLayer) (None, 256) 0
_________________________________________________________________
g (Sequential) (None, 784) 21162496
_________________________________________________________________
d (Sequential) (None, 1) 14209
=================================================================
Total params: 21,176,705
Trainable params: 21,162,496
Non-trainable params: 14,209
_________________________________________________________________
n_epochs = 400
batch_size = 100
n_batches = int(mnist.train.num_examples / batch_size)
n_epochs_print = 50
for epoch in range(n_epochs+1):
epoch_d_loss = 0.0
epoch_g_loss = 0.0
for batch in range(n_batches):
x_batch, _ = mnist.train.next_batch(batch_size)
x_batch = norm(x_batch)
z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])
g_batch = g_model.predict(z_batch)
x_in = np.concatenate([x_batch,g_batch])
y_out = np.ones(batch_size*2)
y_out[:batch_size]=0.9
y_out[batch_size:]=0.1
d_model.trainable=True
batch_d_loss = d_model.train_on_batch(x_in,y_out)
z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])
x_in=z_batch
y_out = np.ones(batch_size)
d_model.trainable=False
batch_g_loss = gan_model.train_on_batch(x_in,y_out)
epoch_d_loss += batch_d_loss
epoch_g_loss += batch_g_loss
if epoch%n_epochs_print == 0:
average_d_loss = epoch_d_loss / n_batches
average_g_loss = epoch_g_loss / n_batches
print('epoch: {0:04d} d_loss = {1:0.6f} g_loss = {2:0.6f}'
.format(epoch,average_d_loss,average_g_loss))
x_pred = g_model.predict(z_test)
display_images(x_pred.reshape(-1,pixel_size,pixel_size))
epoch: 0000 d_loss = 0.529010 g_loss = 1.180989
epoch: 0050 d_loss = 0.708614 g_loss = 0.753180
epoch: 0100 d_loss = 0.703850 g_loss = 0.693080
epoch: 0150 d_loss = 0.696364 g_loss = 0.698857
epoch: 0200 d_loss = 0.694226 g_loss = 0.700982
epoch: 0250 d_loss = 0.694548 g_loss = 0.699856
epoch: 0300 d_loss = 0.694598 g_loss = 0.697125
epoch: 0350 d_loss = 0.694443 g_loss = 0.695927
epoch: 0400 d_loss = 0.694062 g_loss = 0.695756
