joelouismarino

from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD

joelouismarino

from googlenet_custom_layers import PoolHelper, LRN
from keras.models import model_from_json

model = model_from_json(open('googlenet_architecture.json').read(), custom_objects={"PoolHelper": PoolHelper, "LRN": LRN})
model.load_weights('googlenet_weights.h5')

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name: "GoogleNet"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224

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input = Input(shape=(3, 224, 224))

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layer {
    name: "conv1/7x7_s2"
    type: "Convolution"
    bottom: "data"
    top: "conv1/7x7_s2"
    param {
	lr_mult: 1
	decay_mult: 1
    }
    param {

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conv1_7x7_s2 = Convolution2D(64, 7, 7, subsample=(2,2), border_mode='same', activation='relu', name='conv1/7x7_s2')(input)

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layer {
    name: "pool1/3x3_s2"
    type: "Pooling"
    bottom: "conv1/7x7_s2"
    top: "pool1/3x3_s2"
    pooling_param {
	pool: MAX
	kernel_size: 3
	stride: 2
    }

joelouismarino

pool1_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), border_mode='valid', name='pool1/3x3_s2')(conv1_7x7_s2)

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conv1_zero_pad = ZeroPadding2D(padding=(1, 1))(conv1_7x7_s2)
pool1_helper = PoolHelper()(conv1_zero_pad)
pool1_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), border_mode='valid', name='pool1/3x3_s2')(pool1_helper)

joelouismarino

class PoolHelper(Layer):
    def __init__(self, **kwargs):
	    super(PoolHelper, self).__init__(**kwargs)
    
    def call(self, x, mask=None):
	    return x[:,:,1:,1:]
    
    def get_config(self):
	    config = {}
	    base_config = super(PoolHelper, self).get_config()