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auto.py
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auto.py
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from keras.layers import Input, Dense
from keras.models import Model
import json
import numpy as np
import os
fn = "annotations/distribution-noppl-multi.json"
edgeDim = 80
if "-noppl-" in fn:
edgeDim = 79
for i in range(1, 16):
encodedDim = i
input_img = Input(shape=(edgeDim,))
encoded = Dense(encodedDim, activation='relu')(input_img)
decoded = Dense(edgeDim, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)
encoder = Model(input_img, encoded)
encoded_input = Input(shape=(encodedDim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
data = []
test = []
train = []
with open(fn) as f:
data = json.load(f)
ratio = 5
for i in range(len(data)):
if i % ratio == 0:
test.append(data[i])
else:
train.append(data[i])
testDataset = np.array(test, dtype=np.float32)
trainDataset = np.array(train, dtype=np.float32)
autoencoder.fit(trainDataset, trainDataset, epochs=1500, batch_size=256, shuffle=True, validation_data=(testDataset, testDataset))
try:
os.mkdir("models")
except:
pass
autoencoder.save("models/autoencoder-" + str(edgeDim) + "-" + str(encodedDim) + ".h5")