Initial commit

Tensorflow/tutoriel24/GAN_gen.py

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+from __future__ import absolute_import, division, print_function, unicode_literals
+import tensorflow as tf
+from tensorflow.keras import layers
+import numpy as np
+import os
+import cv2
+import time
+import math
+import re
+
+batch_size=128
+epochs=10000
+noise_dim=100
+num_examples_to_generate=36
+
+def make_generator_model():
+    model=tf.keras.Sequential()
+
+    model.add(layers.Dense(4*4*1024, use_bias=False, input_shape=(noise_dim,)))
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+    
+    model.add(layers.Reshape((4, 4, 1024)))
+
+    model.add(layers.Conv2DTranspose(512, (5, 5), strides=(2, 2), padding='same', use_bias=False))
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Conv2DTranspose(256, (5, 5), strides=(2, 2), padding='same', use_bias=False))
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(2, 2), padding='same', use_bias=False))
+    model.add(layers.BatchNormalization())
+
+    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
+    model.add(layers.BatchNormalization())
+
+    model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='sigmoid'))
+
+    return model
+
+generator=make_generator_model()
+checkpoint=tf.train.Checkpoint(generator=generator)
+
+checkpoint.restore("./training_checkpoints/ckpt-90")
+
+d=50
+flag=1
+while True:
+    if flag:
+        bruit=tf.random.normal([1, noise_dim])
+    test_images=generator(bruit, training=False)
+    v=bruit[0][d]
+    img=np.float32(test_images[0])
+    print(">>> {}:{:6.8f}".format(d, v))
+    cv2.imshow("image", img)
+    flag=1
+    key=cv2.waitKey()
+    if key==ord('q'):
+        quit()
+    if key==ord('o'):
+        d=min(100, d+1)
+        flag=0
+    if key==ord('l'):
+        d=max(1, d-1)
+        flag=0
+    if key==ord('p'):
+        u=np.zeros([1, noise_dim], dtype=np.float32)
+        u[0][d]=0.5
+        tu=tf.convert_to_tensor(u, dtype=tf.float32)
+        bruit=tf.add(bruit, tu)
+        flag=0
+    if key==ord('m'):
+        u=np.zeros([1, noise_dim], dtype=np.float32)
+        u[0][d]=-0.5
+        tu=tf.convert_to_tensor(u, dtype=tf.float32)
+        bruit=tf.add(bruit, tu)
+        flag=0
+