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Tensorflow/tutoriel35/genere.py

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+import tensorflow as tf
+from tensorflow.keras import layers, models
+import time, threading
+import numpy as np
+import cv2
+import model_cond
+
+noise_dim=100
+
+generator=model_cond.generator_model()
+checkpoint=tf.train.Checkpoint(generator=generator)
+checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir='./training_checkpoints_gan_cond/'))
+
+marge=20
+
+while True:
+    chiffres=input("Entrez une serie de chiffre:")
+    try:
+        chiffres_int=int(chiffres)
+    except:
+        continue
+
+    liste_chiffres=[]
+    while (chiffres_int):
+        liste_chiffres.append(chiffres_int%10)
+        chiffres_int=int(chiffres_int/10)
+    seed=tf.random.normal([len(liste_chiffres), noise_dim])
+    labels=tf.one_hot(liste_chiffres, 10)
+    image=np.zeros(shape=(28+2*marge, len(liste_chiffres)*28+2*marge), dtype=np.float32)
+    prediction=generator([seed, labels], training=False)
+    for i in range(len(prediction)):
+        image[marge:marge+28, marge+i*28:marge+(i+1)*28]=prediction[len(liste_chiffres)-i-1, :, :, 0]*127.5+127.5
+    cv2.imshow("Image", image.astype(np.uint8))
+    key=cv2.waitKey(10)