125_126_GAN_predict_mnist.py

#!/usr/bin/env python
__author__ = "Sreenivas Bhattiprolu"
__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"


# https://youtu.be/xBX2VlDgd4I   #Introduction video
# https://youtu.be/Mng57Tj18pc   #Keras implementation video. 

"""
References from the video: 
https://www.thispersondoesnotexist.com/
http://www.wisdom.weizmann.ac.il/~vision/courses/2018_2/Advanced_Topics_in_Computer_Vision/files/DomainTransfer.pdf
"""

#FOr single image
# example of generating an image for a specific point in the latent space
from keras.models import load_model
from numpy import asarray
from matplotlib import pyplot
from numpy.random import randn

# load model
model = load_model('generator_model_100K.h5')

#To create same image, suppy same vector each time
# all 0s
#vector = asarray([[0. for _ in range(100)]])  #Vector of all zeros

#To create random images each time...
vector = randn(100) #Vector of random numbers (creates a column, need to reshape)
vector = vector.reshape(1, 100)

# generate image
X = model.predict(vector)

# plot the result
pyplot.imshow(X[0, :, :, 0], cmap='gray_r')
pyplot.show()

"""
#Uncomment to run this part of the code....
##############################################

# example of loading the generator model and generating images
from keras.models import load_model
from numpy.random import randn
from matplotlib import pyplot as plt


# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):
	# generate points in the latent space
	x_input = randn(latent_dim * n_samples)
	# reshape into a batch of inputs for the network
	x_input = x_input.reshape(n_samples, latent_dim)
	return x_input

# create and save a plot of generated images (reversed grayscale)
def save_plot(examples, n):
	# plot images
	for i in range(n * n):
		# define subplot
		plt.subplot(n, n, 1 + i)
		# turn off axis
		plt.axis('off')
		# plot raw pixel data
		plt.imshow(examples[i, :, :, 0], cmap='gray_r')
	plt.show()

# load model
model = load_model('generator_model_100K.h5')
# generate images
#Generate 16 images, each image provide a vector of size 100 as input
latent_points = generate_latent_points(100, 16) 
# generate images
X = model.predict(latent_points)
# plot the result
save_plot(X, 4)  #Plot 4x4 grid (Change to 5 if generating 25 images)

"""