This repository contains the code I used for the Yelp Kaggle competition.
My primary motivation is to use the competition as a learning opportunity. I've developed an object-oriented model model which exposes the familiar scikit-learn API.
Beyond the application of standard development practices to data science, I've implemented the latest and greatest convolutional network in Keras.
The competition involves labeling each image from nine possible labels.
If we were to one-hot encode you'd have 2**9 = 512 classes, not far from the 1000 classes in the ImageNet competition.
Their deepest network required 11 billion FLOPs and a lot of time and resources to train. Consider yourself warned.
Glad you asked! Here's the 34-layer residual model.
>>> model = KerasGraphModel()
>>> model.build_residual_network()
>>> model.graph.summary()
--------------------------------------------------------------------------------
Layer (name) Output Shape Param #
--------------------------------------------------------------------------------
Layer (input) (None, 3, 224, 224) 0
Convolution2D (conv1_1) (None, 64, 112, 112) 9472
BatchNormalization (bn1_1) (None, 64, 112, 112) 224
Activation (relu1_1) (None, 64, 112, 112) 0
MaxPooling2D (pool1) (None, 64, 56, 56) 0
Convolution2D (conv2_1) (None, 64, 56, 56) 36928
BatchNormalization (bn2_1) (None, 64, 56, 56) 112
Activation (relu2_1) (None, 64, 56, 56) 0
Convolution2D (conv2_2) (None, 64, 56, 56) 36928
BatchNormalization (bn2_2) (None, 64, 56, 56) 112
Activation (relu2_2) (None, 64, 56, 56) 0
Convolution2D (conv2_3) (None, 64, 56, 56) 36928
BatchNormalization (bn2_3) (None, 64, 56, 56) 112
Activation (relu2_3) (None, 64, 56, 56) 0
Convolution2D (conv2_4) (None, 64, 56, 56) 36928
BatchNormalization (bn2_4) (None, 64, 56, 56) 112
Activation (relu2_4) (None, 64, 56, 56) 0
Convolution2D (conv2_5) (None, 64, 56, 56) 36928
BatchNormalization (bn2_5) (None, 64, 56, 56) 112
Activation (relu2_5) (None, 64, 56, 56) 0
Convolution2D (conv2_6) (None, 64, 56, 56) 36928
BatchNormalization (bn2_6) (None, 64, 56, 56) 112
Activation (relu2_6) (None, 64, 56, 56) 0
Convolution2D (conv3_1) (None, 128, 28, 28) 73856
BatchNormalization (bn3_1) (None, 128, 28, 28) 56
Activation (relu3_1) (None, 128, 28, 28) 0
Convolution2D (conv3_2) (None, 128, 28, 28) 147584
BatchNormalization (bn3_2) (None, 128, 28, 28) 56
Convolution2D (short3_1) (None, 32, 56, 56) 2080
BatchNormalization (short_bn3_(None, 32, 56, 56) 112
Reshape (short_reshape3_1) (None, 128, 28, 28) 0
Activation (relu3_2) (None, 128, 28, 28) 0
Convolution2D (conv3_3) (None, 128, 28, 28) 147584
BatchNormalization (bn3_3) (None, 128, 28, 28) 56
Activation (relu3_3) (None, 128, 28, 28) 0
Convolution2D (conv3_4) (None, 128, 28, 28) 147584
BatchNormalization (bn3_4) (None, 128, 28, 28) 56
Activation (relu3_4) (None, 128, 28, 28) 0
Convolution2D (conv3_5) (None, 128, 28, 28) 147584
BatchNormalization (bn3_5) (None, 128, 28, 28) 56
Activation (relu3_5) (None, 128, 28, 28) 0
Convolution2D (conv3_6) (None, 128, 28, 28) 147584
BatchNormalization (bn3_6) (None, 128, 28, 28) 56
Activation (relu3_6) (None, 128, 28, 28) 0
Convolution2D (conv3_7) (None, 128, 28, 28) 147584
BatchNormalization (bn3_7) (None, 128, 28, 28) 56
Activation (relu3_7) (None, 128, 28, 28) 0
Convolution2D (conv3_8) (None, 128, 28, 28) 147584
BatchNormalization (bn3_8) (None, 128, 28, 28) 56
Activation (relu3_8) (None, 128, 28, 28) 0
Convolution2D (conv4_1) (None, 256, 14, 14) 295168
BatchNormalization (bn4_1) (None, 256, 14, 14) 28
Activation (relu4_1) (None, 256, 14, 14) 0
Convolution2D (conv4_2) (None, 256, 14, 14) 590080
BatchNormalization (bn4_2) (None, 256, 14, 14) 28
Convolution2D (short4_1) (None, 64, 28, 28) 8256
BatchNormalization (short_bn4_(None, 64, 28, 28) 56
Reshape (short_reshape4_1) (None, 256, 14, 14) 0
Activation (relu4_2) (None, 256, 14, 14) 0
Convolution2D (conv4_3) (None, 256, 14, 14) 590080
BatchNormalization (bn4_3) (None, 256, 14, 14) 28
Activation (relu4_3) (None, 256, 14, 14) 0
Convolution2D (conv4_4) (None, 256, 14, 14) 590080
BatchNormalization (bn4_4) (None, 256, 14, 14) 28
Activation (relu4_4) (None, 256, 14, 14) 0
Convolution2D (conv4_5) (None, 256, 14, 14) 590080
BatchNormalization (bn4_5) (None, 256, 14, 14) 28
Activation (relu4_5) (None, 256, 14, 14) 0
Convolution2D (conv4_6) (None, 256, 14, 14) 590080
BatchNormalization (bn4_6) (None, 256, 14, 14) 28
Activation (relu4_6) (None, 256, 14, 14) 0
Convolution2D (conv4_7) (None, 256, 14, 14) 590080
BatchNormalization (bn4_7) (None, 256, 14, 14) 28
Activation (relu4_7) (None, 256, 14, 14) 0
Convolution2D (conv4_8) (None, 256, 14, 14) 590080
BatchNormalization (bn4_8) (None, 256, 14, 14) 28
Activation (relu4_8) (None, 256, 14, 14) 0
Convolution2D (conv4_9) (None, 256, 14, 14) 590080
BatchNormalization (bn4_9) (None, 256, 14, 14) 28
Activation (relu4_9) (None, 256, 14, 14) 0
Convolution2D (conv4_10) (None, 256, 14, 14) 590080
BatchNormalization (bn4_10) (None, 256, 14, 14) 28
Activation (relu4_10) (None, 256, 14, 14) 0
Convolution2D (conv4_11) (None, 256, 14, 14) 590080
BatchNormalization (bn4_11) (None, 256, 14, 14) 28
Activation (relu4_11) (None, 256, 14, 14) 0
Convolution2D (conv4_12) (None, 256, 14, 14) 590080
BatchNormalization (bn4_12) (None, 256, 14, 14) 28
Activation (relu4_12) (None, 256, 14, 14) 0
Convolution2D (conv5_1) (None, 512, 7, 7) 1180160
BatchNormalization (bn5_1) (None, 512, 7, 7) 14
Activation (relu5_1) (None, 512, 7, 7) 0
Convolution2D (conv5_2) (None, 512, 7, 7) 2359808
BatchNormalization (bn5_2) (None, 512, 7, 7) 14
Convolution2D (short5_1) (None, 128, 14, 14) 32896
BatchNormalization (short_bn5_(None, 128, 14, 14) 28
Reshape (short_reshape5_1) (None, 512, 7, 7) 0
Activation (relu5_2) (None, 512, 7, 7) 0
Convolution2D (conv5_3) (None, 512, 7, 7) 2359808
BatchNormalization (bn5_3) (None, 512, 7, 7) 14
Activation (relu5_3) (None, 512, 7, 7) 0
Convolution2D (conv5_4) (None, 512, 7, 7) 2359808
BatchNormalization (bn5_4) (None, 512, 7, 7) 14
Activation (relu5_4) (None, 512, 7, 7) 0
Convolution2D (conv5_5) (None, 512, 7, 7) 2359808
BatchNormalization (bn5_5) (None, 512, 7, 7) 14
Activation (relu5_5) (None, 512, 7, 7) 0
Convolution2D (conv5_6) (None, 512, 7, 7) 2359808
BatchNormalization (bn5_6) (None, 512, 7, 7) 14
Activation (relu5_6) (None, 512, 7, 7) 0
AveragePooling2D (pool2) (None, 512, 1, 1) 0
Flatten (flatten) (None, 512) 0
Dense (dense) (None, 9) 4617
Dense (output) (None, 9) 4617
--------------------------------------------------------------------------------
Total params: 21153041
--------------------------------------------------------------------------------