lab:TensorFlow로 Fancy Softmax Classification의 구현하기
February 28, 2019
해당 게시물은 Edwith에서 제공하는
머신러닝과 딥러닝 BASIC을 듣고 요약 정리한 글입니다.
Animal classification
동물의 특징에 따라 동물이 어떤 종인지 예측
import tensorflow as tf
import numpy as np/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_convertersLoad data file
# 다양한 특징들을 기반으로 동물의 종을 예측
xy = np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.float32)
x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]
print(x_data.shape, y_data.shape)(101, 16) (101, 1)tf.one_hot and reshape
[[0]. [3]]의 데이터가 one hot과정을 거치게 되면 한 차원들
더해 [[[1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]]]의 데이터가 된다.
기존의 데이터가 2차원일 경우 3차원의 데이터가 된다.
따라서 reshape함수를 사용해 차원을 맞춰준다.
reshape후의 데이터는 [[1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]]이다.
(?, 1) -> (?, 1, 7) -> (?, 7)로 shape이 변경된다.
nb_classes = 7 # 0 ~ 6
# 동물의 특징 데이터 16가지
X = tf.placeholder(tf.float32, [None, 16])
# 동물의 종 7가지
Y = tf.placeholder(tf.int32, [None, 1]) # 0 ~ 6, shape=(?, 1)
Y_one_hot = tf.one_hot(Y, nb_classes) # one hot shape=(?, 1, 7)
print("one_hot", Y_one_hot)
Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes]) # shape=(?, 7)
print("reshape", Y_one_hot)one_hot Tensor("one_hot:0", shape=(?, 1, 7), dtype=float32)
reshape Tensor("Reshape:0", shape=(?, 7), dtype=float32)Logits & Hypothesis
W = tf.Variable(tf.random_normal([16, nb_classes]), name='weight')
b = tf.Variable(tf.random_normal([nb_classes]), name='bias')
# tf.nn.softmax함수가 softmax 연산 진행, 아래 식과 동일
# softmax = exp(logits) / reduce_sum(exp(logits), dim)
logits = tf.matmul(X, W) + b
hypothesis = tf.nn.softmax(logits)Cross entropy cost / Loss Function
cost_i = tf.nn.softmax_cross_entropy_with_logits(logits=logits,
labels=Y_one_hot)
cost = tf.reduce_mean(cost_i)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)WARNING:tensorflow:From <ipython-input-5-bc56557890b4>:2: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See `tf.nn.softmax_cross_entropy_with_logits_v2`.Training
prediction = tf.argmax(hypothesis, 1)
correct_prediction = tf.equal(prediction, tf.argmax(Y_one_hot, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 학습 시작
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(2000):
sess.run(optimizer, feed_dict={X: x_data, Y: y_data})
if step % 100 == 0:
loss, acc = sess.run([cost, accuracy], feed_dict={
X: x_data,
Y: y_data,
})
print("Step : {:5}\tLoss : {:.3f}\tAcc : {:.2%}"\
.format(step, loss, acc))
# 예츨 결과 확인
pred = sess.run(prediction, feed_dict={X: x_data})Step : 0 Loss : 7.064 Acc : 1.98%
Step : 100 Loss : 0.940 Acc : 83.17%
Step : 200 Loss : 0.515 Acc : 85.15%
Step : 300 Loss : 0.362 Acc : 88.12%
Step : 400 Loss : 0.284 Acc : 92.08%
Step : 500 Loss : 0.233 Acc : 93.07%
Step : 600 Loss : 0.197 Acc : 94.06%
Step : 700 Loss : 0.171 Acc : 95.05%
Step : 800 Loss : 0.150 Acc : 95.05%
Step : 900 Loss : 0.133 Acc : 97.03%
Step : 1000 Loss : 0.120 Acc : 98.02%
Step : 1100 Loss : 0.109 Acc : 98.02%
Step : 1200 Loss : 0.100 Acc : 99.01%
Step : 1300 Loss : 0.092 Acc : 100.00%
Step : 1400 Loss : 0.085 Acc : 100.00%
Step : 1500 Loss : 0.079 Acc : 100.00%
Step : 1600 Loss : 0.074 Acc : 100.00%
Step : 1700 Loss : 0.070 Acc : 100.00%
Step : 1800 Loss : 0.066 Acc : 100.00%
Step : 1900 Loss : 0.062 Acc : 100.00%Check Predictive Results
# y_data: (N,1) = flatten => (N, ) matches pred.shape
for p, y in zip(pred, y_data.flatten()):
print("[{}] Prediction : {} True Y : {}".format(p == int(y), p, int(y)))[True] Prediction : 0 True Y : 0
[True] Prediction : 0 True Y : 0
...
[True] Prediction : 0 True Y : 0
[True] Prediction : 6 True Y : 6
[True] Prediction : 1 True Y : 1
