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Layers in Deep Learning
&
Caffe layers
(model architecture )
Farshid PirahanSiah
Dec 2016
Layers in Deep Learning
• Convolution
• 2D
• Activation
• Sigmoid
• Tanh
• ReLU
• Pooling
• Max
• Average
• elementwise
• Sum
• Product
• Max
• Two tensors
• LRN
• Cross-channel
• Fully-connected
• With bias
• Without bias
• softMax
• Cross-channel
• Deconvolution
• Output
• Prob
• Softmax
Layers in Caffe
• Vision Layers
• particular operation to some region of the input to produce a corresponding region of
the output.
• other layers (with few exceptions) ignore the spatial structure of the input
• Loss Layers
• comparing an output to a target and assigning cost to minimize.
• Activation / Neuron Layers
• element-wise operators, taking one bottom blob and producing one top blob of the
same size
• Data Layers
• (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files
on disk in HDF5 or common image formats
• Common Layers
Layers in Caffe
• Vision Layers
• Convolution
• Pooling
• Local Response Normalization (LRN)
• Im2col
• Loss Layers
• Softmax
• Sum-of-Squares / Euclidean
• Hinge / Margin
• Sigmoid Cross-Entropy
• Infogain
• Accuracy and Top-k
• Activation / Neuron Layers
• ReLU / Rectified-Linear and Leaky-ReLU
• Sigmoid
• TanH / Hyperbolic Tangent
• Absolute Value
• Power
• BNLL
• Data Layers
• Database
• In-Memory
• HDF5 Input
• HDF5 Output
• Images
• Windows
• Dummy
• Common Layers
• Inner Product
• Splitting
• Flattening
• Reshape
• Concatenation
• Slicing
• Elementwise Operations
• Argmax
• Softmax
• Mean-Variance Normalization
Layer Activations
• “Activation functions transform the weighted sum of inputs that goes into
the artificial neurons. “
• “These functions should be non-linear to encode complex patterns of the
data. “
Convolution Layer
• “This layer consists of a set of learnable filters that we slide over the image“
Convolutional Neural Networks Architecture
• Simple CNN architecture contents
• INPUT: an input layer (images)
• CONV: convolutional layers (learnable features extractors) [have weights]
• RELU: ReLU activation functions (learnable features extractors)
• POOL: pooling layers (learnable features extractors)
• FC: fully-connected layers (machine learning classifier) [have weights]
Reference
1. A Practical Introduction to Deep Learning with Caffe and Python
http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/
2. http://caffe.berkeleyvision.org/tutorial/layers.html
3. https://github.com/kjw0612/awesome-deep-vision
4. http://cs231n.github.io/neural-networks-1/
5. https://en.wikipedia.org/wiki/Convolutional_neural_network
6. http://cs231n.github.io/transfer-learning/
7. https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
8. http://cs231n.github.io/
9. https://developer.nvidia.com/tensorrt

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Layers in Deep Learning & Caffe layers (model architecture )

  • 1. Layers in Deep Learning & Caffe layers (model architecture ) Farshid PirahanSiah Dec 2016
  • 2. Layers in Deep Learning • Convolution • 2D • Activation • Sigmoid • Tanh • ReLU • Pooling • Max • Average • elementwise • Sum • Product • Max • Two tensors • LRN • Cross-channel • Fully-connected • With bias • Without bias • softMax • Cross-channel • Deconvolution • Output • Prob • Softmax
  • 3. Layers in Caffe • Vision Layers • particular operation to some region of the input to produce a corresponding region of the output. • other layers (with few exceptions) ignore the spatial structure of the input • Loss Layers • comparing an output to a target and assigning cost to minimize. • Activation / Neuron Layers • element-wise operators, taking one bottom blob and producing one top blob of the same size • Data Layers • (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats • Common Layers
  • 4. Layers in Caffe • Vision Layers • Convolution • Pooling • Local Response Normalization (LRN) • Im2col • Loss Layers • Softmax • Sum-of-Squares / Euclidean • Hinge / Margin • Sigmoid Cross-Entropy • Infogain • Accuracy and Top-k • Activation / Neuron Layers • ReLU / Rectified-Linear and Leaky-ReLU • Sigmoid • TanH / Hyperbolic Tangent • Absolute Value • Power • BNLL • Data Layers • Database • In-Memory • HDF5 Input • HDF5 Output • Images • Windows • Dummy • Common Layers • Inner Product • Splitting • Flattening • Reshape • Concatenation • Slicing • Elementwise Operations • Argmax • Softmax • Mean-Variance Normalization
  • 5. Layer Activations • “Activation functions transform the weighted sum of inputs that goes into the artificial neurons. “ • “These functions should be non-linear to encode complex patterns of the data. “
  • 6. Convolution Layer • “This layer consists of a set of learnable filters that we slide over the image“
  • 7. Convolutional Neural Networks Architecture • Simple CNN architecture contents • INPUT: an input layer (images) • CONV: convolutional layers (learnable features extractors) [have weights] • RELU: ReLU activation functions (learnable features extractors) • POOL: pooling layers (learnable features extractors) • FC: fully-connected layers (machine learning classifier) [have weights]
  • 8. Reference 1. A Practical Introduction to Deep Learning with Caffe and Python http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/ 2. http://caffe.berkeleyvision.org/tutorial/layers.html 3. https://github.com/kjw0612/awesome-deep-vision 4. http://cs231n.github.io/neural-networks-1/ 5. https://en.wikipedia.org/wiki/Convolutional_neural_network 6. http://cs231n.github.io/transfer-learning/ 7. https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ 8. http://cs231n.github.io/ 9. https://developer.nvidia.com/tensorrt