44874bed267e008be9bee5d85a4f4040b854d246,examples/images/dcgan.py,,,#,60

Before Change

        // Noise input.
        z = np.random.uniform(-1., 1., size=[1, z_dim])
        // Generate image from noise. Extend to 3 channels for matplot figure.
        temp = [[ii, ii, ii] for ii in list(gen.predict({"input_gen_noise": z})[0])]
        a[j][i].imshow(np.reshape(temp, (28, 28, 3)))
f.show()
plt.draw()

After Change

    g = np.array(gen.predict({"input_gen_noise": z}))
    for j in range(4):
        // Generate image from noise. Extend to 3 channels for matplot figure.
        img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2),
                         newshape=(28, 28, 3))
        a[j][i].imshow(img)

f.show()
plt.draw()

Instances