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