binary_classification.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split


def load_data():
    dataframe = pd.read_csv('data/z-pushed-210810.csv')
    train_df, test_df = train_test_split(dataframe, test_size=0.2, shuffle=True)
    y_train_df = train_df.pop('pushed')
    y_test_df = test_df.pop('pushed')
    x_train = train_df.values
    y_train = y_train_df.values
    x_test = test_df.values
    y_test = y_test_df.values
    return (x_train, y_train), (x_test, y_test)


(x_train, y_train), (x_test, y_test) = load_data()

print(x_train[0:5])
print(y_train[0:5])

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(10, activation='relu', input_shape=[25]))
model.add(tf.keras.layers.Dense(20, activation='relu'))
model.add(tf.keras.layers.Dense(5, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[tf.keras.metrics.BinaryAccuracy()])

model.fit(x_train, y_train, epochs=100)

result = model.evaluate(x_test, y_test, return_dict=True)
print(result)
print(model.summary())

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converted_model = converter.convert()
with tf.io.gfile.GFile('model/ring_keras_model_210810.tflite', 'wb') as f:
    f.write(converted_model)