simleek
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
from displayarray import display
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import numpy as np
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from tensorflow.keras import layers, models
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import tensorflow as tf
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from examples.videos import test_video_2
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for gpu in tf.config.experimental.list_physical_devices("GPU"):
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tf.compat.v2.config.experimental.set_memory_growth(gpu, True)
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displayer = display(test_video_2)
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displayer.wait_for_init()
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autoencoder = models.Sequential()
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autoencoder.add(
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layers.Conv2D(
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20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape
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)
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)
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autoencoder.add(
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layers.Conv2D(
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20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape
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)
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)
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autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
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autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
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autoencoder.compile(loss="mse", optimizer="adam")
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while displayer:
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displayer.update()
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grab = tf.convert_to_tensor(
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next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(np.float32)
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/ 255.0
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)
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grab_noise = tf.convert_to_tensor(
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(
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(
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next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(
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np.float32
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)
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+ np.random.uniform(0, 255, grab.shape)
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)
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/ 2
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)
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/ 255.0
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)
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displayer.update(
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(grab_noise.numpy()[0] * 255.0).astype(np.uint8), "uid for grab noise"
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)
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autoencoder.fit(grab_noise, grab, steps_per_epoch=1, epochs=1)
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output_image = autoencoder.predict(grab, steps=1)
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displayer.update(
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(output_image[0] * 255.0).astype(np.uint8), "uid for autoencoder output"
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)
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get_3rd_layer_output = tf.keras.backend.function(
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[autoencoder.layers[0].input], [autoencoder.layers[1].output]
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)
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layer_output = get_3rd_layer_output([grab_noise])[0]
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displayer.update(layer_output[0], "conv 1")
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