27 lines
989 B
Python
27 lines
989 B
Python
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import numpy as np
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import tensorflow as tf
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from keras.layers import Input, Dense, Conv2D
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from keras.models import Model
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import settings
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#model
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input_shape = settings.midi_resolution*128
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input_img = tf.keras.layers.Input(shape=(input_shape,))
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encoded = tf.keras.layers.Dense(160, activation='relu')(input_img)
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decoded = tf.keras.layers.Dense(input_shape, activation='sigmoid')(encoded)
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autoencoder = tf.keras.models.Model(input_img, decoded)
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autoencoder.compile(optimizer='adadelta',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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# load weights into new model
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autoencoder.load_weights(settings.model_path)
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print("Loaded model from {}".format(settings.model_path))
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# generate_seed = np.random.rand(12288).reshape(1,12288)
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generate_seed = np.load(settings.samples_path)['arr_0'][15].reshape(1,12288)
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generated_sample = autoencoder.predict(generate_seed)
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np.savez_compressed(settings.generated_sample_path, generated_sample)
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