solaris33

in_a = tf.placeholder(dtype=tf.float32, shape=(2))
in_b = tf.placeholder(dtype=tf.float32, shape=(2))

def forward(x):
  with tf.variable_scope("matmul", reuse=tf.AUTO_REUSE):
    W = tf.get_variable("W", initializer=tf.ones(shape=(2,2)),
                        regularizer=tf.contrib.layers.l2_regularizer(0.04))
    b = tf.get_variable("b", initializer=tf.zeros(shape=(2)))
    return W * x + b

solaris33

# TensorFlow 1.X
outputs = session.run(f(placeholder), feed_dict={placeholder: input})
# TensorFlow 2.0
outputs = f(input)

solaris33

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import imgaug as ia
import imgaug.augmenters as iaa

# draw single image
def drawImage(figureName, image):
  plt.figure(num=figureName)
  plt.imshow(image / 255) # 0-1 float normalize

solaris33

"""
Cartpole Policy Gradient Example using TensorFlow 2.0

Reference : https://github.com/awjuliani/DeepRL-Agents/blob/master/Vanilla-Policy.ipynb

Author : solaris33
Project URL : http://solarisailab.com/archives/2652
"""

import tensorflow as tf

solaris33

# -*- coding: utf-8 -*-
# Convolutional Neural Networks(CNN)을 이용한 MNIST 분류기(Classifier) - Keras API를 이용한 구현

import tensorflow as tf

# MNIST 데이터를 다운로드 합니다.
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# 이미지들을 float32 데이터 타입으로 변경합니다.
x_train, x_test = x_train.astype('float32'), x_test.astype('float32')
# 28*28 형태의 이미지를 784차원으로 flattening 합니다.

solaris33

#######################################################################
# Copyright (C)                                                       #
# 2016-2018 Shangtong Zhang([email protected])             #
# 2016 Kenta Shimada([email protected])                         #
# Permission given to modify the code as long as you keep this        #
# declaration at the top                                              #
#######################################################################
# Refactoring : solaris33

import matplotlib

solaris33

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import functools
from PIL import Image

"""
TensorFlow Data Augmentation Example

Reference : https://github.com/tensorflow/models/blob/master/research/object_detection/core/preprocessor.py

solaris33

# reference : https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py

'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''

from __future__ import print_function

solaris33

#-*- coding: utf-8 -*-
"""
TFRecords Example

Reference : https://www.tensorflow.org/tutorials/load_data/tf_records

Author : solaris33
Project URL : http://solarisailab.com/archives/2603
"""
from __future__ import absolute_import, division, print_function, unicode_literals

solaris33

import tensorflow as tf

i = tf.constant(1)
j = tf.constant(1)
k = tf.constant(1)

def cond(i, j, k):
  return tf.less(i, 10)

def body(i, j, k):