23 lines
733 B
Python
23 lines
733 B
Python
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import numpy as np
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from keras.layers import Input, Dense, Conv2D
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from keras.models import Model
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GENERATED_BEAT_PATH = 'data/output/generated_bar'
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MODEL_PATH = 'data/autoencoder_model.h5'
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SAMPLES_PATH = 'data/samples.npz'
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input = Input(shape=(1,96,128))
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encoded = Conv2D(filters = 32, kernel_size = 1)(input)
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decoded = Conv2D(filters = 128, kernel_size = 1)(encoded)
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autoencoder = Model(input, decoded)
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# load weights into new model
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autoencoder.load_weights(MODEL_PATH)
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print("Loaded model from disk")
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# generate_seed = np.random.rand(1,1,96,128)
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generate_seed = np.load(SAMPLES_PATH)['arr_0'][0:]
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generated_beat = autoencoder.predict(generate_seed)
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np.savez_compressed(GENERATED_BEAT_PATH, generated_beat)
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