ML with Tensorflow lab - GitHub 2017. 10. 2.¢  Fei-Fei Li & Andrej Karpathy &...

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Transcript of ML with Tensorflow lab - GitHub 2017. 10. 2.¢  Fei-Fei Li & Andrej Karpathy &...

  • Lab 11 CNN

    Sung Kim http://hunkim.github.io/ml/

  • https://github.com/nlintz/TensorFlow-Tutorials

  • CNN

    http://parse.ele.tue.nl/cluster/2/CNNArchitecture.jpg

  • MNIST 28x28x1 image

    32 filters (3x3x1)

    Convolutional layers

  • 28x28x1 image

    32 filters (3x3x1) w=tf.Variable(tf.random_normal([3,3,1,32], stddev=0.01))

    Convolutional layers

  • 28x28x1 image

    32 filters (3x3x1) w=tf.Variable(tf.random_normal([3,3,1,32], stddev=0.01))

    Convolutional layers

  • 28x28x1 image

    32 filters (3x3x1) activation maps

    (?, ?, 32)

    Convolutional layers

  • ReLU

  • Pooling layer (sampling)

    conv layer

    l1 = tf.nn.max_pool(c1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

  • Lecture 7 - 27 Jan 2016Fei-Fei Li & Andrej Karpathy & Justin JohnsonFei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - 27 Jan 201655

    1 1 2 4

    5 6 7 8

    3 2 1 0

    1 2 3 4

    Single depth slice

    x

    y

    max pool with 2x2 filters and stride 2 6 8

    3 4

    MAX POOLING

  • Pooling layer (sampling)

    conv layer

    l1 = tf.nn.max_pool(c1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

    28x28x32

    ? x ? x 32

  • Tensor("MaxPool:0", shape=(?, 14, 14, 32), dtype=float32)

    Shape not sure? Print tensor

    Tensor("Conv2D:0", shape=(?, 28, 28, 32), dtype=float32)

  • X = trX.reshape(-1, 28, 28, 1)

  • X = trX.reshape(-1, 28, 28, 1)

    dropout

  • CNN

    http://parse.ele.tue.nl/cluster/2/CNNArchitecture.jpg

  • Fully connected net

  • cost and optimization

    https://www.tensorflow.org/versions/r0.8/api_docs/python/train.html#RMSPropOptimizer

  • Other TF optimizers

    https://www.tensorflow.org/versions/r0.8/api_docs/python/train.html#RMSPropOptimizer

  • Train and testing

  • Train and testing 0 128

    128 256 256 384 384 512

  • Train and testing

  • https://github.com/nlintz/TensorFlow-Tutorials