CaffeNet

Info#

Only one version of CaffeNet has been built

@article{ding2014theano,
  title={Theano-based Large-Scale Visual Recognition with Multiple GPUs},
  author={Ding, Weiguang and Wang, Ruoyan and Mao, Fei and Taylor, Graham},
  journal={arXiv preprint arXiv:1412.2302},
  year={2014}
}

Keras Model Visulisation#

CaffeNet

Keras Model Builds#

# Channel 1 - Convolution Net Input Layer
x = conv2D_lrn2d(
    img_input, 3, 11, 11, subsample=(
        1, 1), border_mode='same')
x = ZeroPadding2D(padding=(1, 1), dim_ordering=DIM_ORDERING)(x)

# Channel 1 - Convolution Net Layer 1
x = conv2D_lrn2d(x, 96, 55, 55, subsample=(1, 1), border_mode='same')
x = MaxPooling2D(
    strides=(
        2, 2), pool_size=(
            2, 2), dim_ordering=DIM_ORDERING)(x)
x = LRN2D(alpha=ALPHA, beta=BETA)(x)
x = ZeroPadding2D(padding=(1, 1), dim_ordering=DIM_ORDERING)(x)

# Channel 1 - Convolution Net Layer 2
x = conv2D_lrn2d(x, 192, 27, 27, subsample=(1, 1), border_mode='same')
x = MaxPooling2D(
    strides=(
        2, 2), pool_size=(
            2, 2), dim_ordering=DIM_ORDERING)(x)
x = LRN2D(alpha=ALPHA, beta=BETA)(x)
x = ZeroPadding2D(padding=(1, 1), dim_ordering=DIM_ORDERING)(x)

# Channel 1 - Convolution Net Layer 3
x = conv2D_lrn2d(x, 288, 13, 13, subsample=(1, 1), border_mode='same')
x = ZeroPadding2D(padding=(1, 1), dim_ordering=DIM_ORDERING)(x)

# Channel 1 - Convolution Net Layer 4
x = conv2D_lrn2d(x, 288, 13, 13, subsample=(1, 1), border_mode='same')
x = ZeroPadding2D(padding=(1, 1), dim_ordering=DIM_ORDERING)(x)

# Channel 1 - Convolution Net Layer 5
x = conv2D_lrn2d(x, 256, 13, 13, subsample=(1, 1), border_mode='same')
x = MaxPooling2D(
    strides=(
        2, 2), pool_size=(
            2, 2), dim_ordering=DIM_ORDERING)(x)
x = ZeroPadding2D(padding=(1, 1), dim_ordering=DIM_ORDERING)(x)

# Channel 1 - Cov Net Layer 7
x = Flatten()(x)
x = Dense(4096, activation='relu')(x)
x = Dropout(DROPOUT)(x)

# Channel 1 - Cov Net Layer 8
x = Dense(4096, activation='relu')(x)
x = Dropout(DROPOUT)(x)

# Final Channel - Cov Net 9
x = Dense(output_dim=NB_CLASS,
          activation='softmax')(x)