import numpy as np
from keras.layers import Input, Dense, Conv2D
from keras.models import Model
GENERATED_BEAT_PATH = 'data/output/generated_bar'
MODEL_PATH = 'data/autoencoder_model.h5'
SAMPLES_PATH = 'data/samples.npz'

input = Input(shape=(1,96,128))
encoded = Conv2D(filters = 32, kernel_size = 1)(input)
decoded = Conv2D(filters = 128, kernel_size = 1)(encoded)
autoencoder = Model(input, decoded)

# load weights into new model
autoencoder.load_weights(MODEL_PATH)
print("Loaded model from disk")

# generate_seed = np.random.rand(1,1,96,128)

generate_seed = np.load(SAMPLES_PATH)['arr_0'][0:]

generated_beat = autoencoder.predict(generate_seed)
np.savez_compressed(GENERATED_BEAT_PATH, generated_beat)