open(options['file_arch'], 'w').write(json_string)
model.save_weights(options['file_weight'])
save(self.generator, "generator")
save(self.discriminator, "discriminator")
if __name__ == '__main__':
cgan = CGAN()
#cgan.discriminator.load_weights("C:/Users/ZhenjuYin/Documents/Python Scripts/emotic/class/cgan/discriminatornew_weights.h5")
#cgan.generator.load_weights("C:/Users/ZhenjuYin/Documents/Python Scripts/emotic/class/cgan/generatornew_weights.h5")
cgan.classifier.load_weights("C:/Users/ZhenjuYin/Documents/Python Scripts/emotic/class/saved/model4_weights.h5")
cgan.train(epochs=500, batch_size=16, sample_interval=20)
data = np.load('C:/Users/ZhenjuYin/Documents/Python Scripts/emotic/test_image_data.npy',allow_pickle=True)
image = (data[0:1].astype(np.float32)- 127.5) / 127.5
f = Model(inputs=cgan.classifier.input,outputs=cgan.classifier.get_layer('seq').get_layer('m').output)(image)
with tf.Session() as sess:
f = f.eval()
f = 0.5*f+0.5
r, c = 4,4
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(f[0,:,:,cnt])
axs[i,j].axis('off')
cnt += 1
fig.savefig("C:/Users/ZhenjuYin/Documents/Python Scripts/emotic/class/cgan/imagesnew/c.png" )
x = x.unsqueeze(0)
x.shape
preds = model(x)
preds.flatten().argsort()[:5]
# lets look at the model
model
model.conv_head
model.conv_head.weight
# How do we freeze weights
model.eval()
for parameter in model.parameters():
print(f'{parameter.shape}\t\t\t\t\tTrainable {parameter.requires_grad}')
model
# Change the number of output classes
model.classifier = torch.nn.Linear(1536, 5)
out = model(x)
out.shape
out
dir(model)
# Make the conv_stem kernel_size 1 and stride 1
model.conv_stem