SAMPLES_PATH = 'data/samples.npz'
MODEL_PATH = 'data/autoencoder_model.h5'
EPOCHS = 100

import tensorflow as tf
from tensorflow.keras import layers
from keras.layers import Input, Dense, Conv2D, Flatten
from keras.models import Model
import numpy as np
from sys import exit
import pickle

print('Reading samples from: {}'.format(SAMPLES_PATH))

train_samples = np.load(SAMPLES_PATH)['arr_0']
train_samples = train_samples.reshape(train_samples.shape[0], 1*96*128)
# input = Input(shape=(1,96,128))
# encoded = Conv2D(filters = 32, kernel_size = 1, activation='relu')(input)
# decoded = Conv2D(filters = 128, kernel_size = 1, activation='sigmoid')(encoded)
# autoencoder = Model(input, decoded)
#
# autoencoder.compile(optimizer='adadelta',
#               loss='binary_crossentropy',
#               metrics=['accuracy'])


encoded = Dense(128, input_shape=(1*96*128))
decoded = Dense(96*128)(encoded)
autoencoder = Model(input,decoded)

autoencoder.compile(optimizer='adadelta',
              loss='binary_crossentropy',
              metrics=['accuracy'])

autoencoder.fit(train_samples, train_samples, epochs=EPOCHS, batch_size=150)

autoencoder.save_weights(MODEL_PATH)
print("Saved model to disk")