King-Of-Knights
commented
5 years ago Hello! The function of generator will map latent variable into fake image. So, all you need to do is:
from keras.models import load_model
import matplotlib.pyplot as plt
import numpy as np
def vis_square(data, padsize=1, padval=0):
# force the number of filters to be square
n = int(np.ceil(np.sqrt(data.shape[0])))
padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)
data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))
# tile the filters into an image
data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
return data
noise = np.random.normal(0, 0.5, (1, 110))
gen = load_model('generator.hdf5') # Modified to your generator hdf5 file
noise = np.random.normal(0, 0.5, (100, latent_size))
sampled_labels = np.array([[i] * 10 for i in range(10)]).reshape(-1, 1)
generated_images = generator.predict([noise, sampled_labels]).transpose(0, 2, 3, 1)
generated_images = np.asarray((generated_images * 127.5 + 127.5).astype(np.uint8))
img = vis_square(generated_images)
plt.imshow(img)
plt.savefig('generator_image.png')
plt.show() # if you wish to see themthen you set a loop if you want to generate more! I hope this works for u! :)
yrg23
thank for your awesome work, after whole training complete how can i generate examples and save them one by one. thanks...