Prediction_MNIST.py

# #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_38k.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()


# 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)