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  • 인공지능 과 디지털 데이터 생성 실험실
  • 인공지능 과 자동화
  • 생성형 AI 실험실

4Z1 - Artificial Intelligence/인공지능 개론 - 예제로 구축4

ConvNetJS CIFAR-10 demo https://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html ConvNetJS CIFAR-10 demo cs.stanford.edu 2020. 8. 31.
ML lab10: NN, ReLu, Xavier, Dropout, and Adam ㅇㅇ 2020. 8. 28.
Tensor Board로 딥네트웍 들여다보기 # Lab 9 XOR import tensorflow as tf import numpy as np tf.set_random_seed(777) # for reproducibility x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32) y_data = np.array([[0], [1], [1], [0]], dtype=np.float32) X = tf.placeholder(tf.float32, [None, 2], name="x") Y = tf.placeholder(tf.float32, [None, 1], name="y") with tf.name_scope("Layer1"): W1 = tf.Variable(tf.random_normal([.. 2020. 8. 27.
MNIST Dataset -손글씨 데이터-Tensoflow-cpu 본 샘플은 기본 과정을 다이해 했을때 소스의 구조를 이해 하게되는 예제 이다. Training epoch/batch In the neural network terminology: one epoch = one forward pass and one backward pass of all the training examples batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need. number of iterations = number of passes, each pass using [batch size] number of e.. 2020. 8. 27.

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