Files
ai_formatter/main.py
2024-10-02 19:52:22 +02:00

99 lines
2.2 KiB
Python

import subprocess
import os
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow
from tensorflow import keras
from keras import layers
LINE_WIDTH = 80
MAX_SHIMS = LINE_WIDTH - 1
def get_data():
r = []
def get_source(path : str) -> [str]:
'''returns source file 3 line batches'''
r = []
with open(path, 'r') as file:
lines = []
for line in file:
lines.append(line.strip())
r = [lines[i:i + 3] for i in range(0, len(lines), 3)]
return r
def source_to_np_array(source_batches : []) -> np.array:
r = []
for s in source_batches:
ascii_list = []
for l in s:
l = l[:LINE_WIDTH]
l = l.ljust(LINE_WIDTH)
l = [ord(i) for i in l]
ascii_list += l
n = np.reshape(ascii_list, (3, -1, 1))
n = np.expand_dims(n, axis=0)
r.append(n)
return r
def get_whitespace(path : str) -> [int]:
'''XXX returns the whitespace list of every middle line'''
r = []
output_file = "muf_file.txt"
process = subprocess.Popen(
"converter.out accumulate " + path + " > " + output_file,
shell=True,
)
with open(output_file, 'r') as file:
for n, line in enumerate(file):
if ((n + 2) % 3) != 0: continue
r.append(eval(line))
return r
source = source_to_np_array(get_source("in/xop.c"))
whitespace = get_whitespace("in/xop.c")
whitespace = [np.array(i) for i in whitespace]
r = {'in': source, 'out': whitespace}
return r
data = get_data()
assert len(data['in']) == len(data['in']), "data in and out sizes were inconsistent."
print(data['in'], data['out'])
model = keras.Sequential([
layers.Conv2D(
filters=16,
kernel_size=(3,3),
strides=(1,1),
activation='relu',
padding='valid',
input_shape=(3,LINE_WIDTH,1)
),
#layers.Conv2D(
# filters=32,
# kernel_size=(3,7),
# activation='relu',
# padding='valid'
#),
#layers.Conv2D(
# filters=64,
# kernel_size=(3,13),
# activation='relu',
# padding='valid'
#),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(MAX_SHIMS, activation='softmax')
])
model.compile(
optimizer='adam',
loss='mse',
metrics=['accuracy']
)
model.fit(data['in'], data['out'],
verbose=2,
batch_size=10,
epochs=50,
shuffle=True,
)