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GoogLeNet in Keras

GoogLeNet in Keras

Here is a Keras model of GoogLeNet (a.k.a Inception V1). I created it by converting the GoogLeNet model from Caffe.

GoogLeNet paper:

Going deeper with convolutions.
Szegedy, Christian, et al. 
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

Requirements

The code now runs with Python 3.6, Keras 2.2.4, and either Theano 1.0.4 or Tensorflow 1.14.0. You will also need to install the following:

pip install pillow numpy imageio

To switch to the Theano backend, change your ~/.keras/keras.json file to

{"epsilon": 1e-07, "floatx": "float32", "backend": "theano", "image_data_format": "channels_first"}

Or for the Tensorflow backend,

{"epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow", "image_data_format": "channels_first"}

Note that in either case, the code requires the channels_first option for image_data_format.

Running the Demo (googlenet.py)

To create a GoogLeNet model, call the following from within Python:

from googlenet import create_googlenet
model = create_googlenet()

googlenet.py also contains a demo image classification. To run the demo, you will need to install the pre-trained weights and the class labels. You will also need this test image. Once these are downloaded and moved to the working directory, you can run googlenet.py from the terminal:

$ python googlenet.py

which will output the predicted class label for the image.

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
from pool_helper import PoolHelper
from lrn import LRN
if keras.backend.backend() == 'tensorflow':
from keras import backend as K
import tensorflow as tf
from keras.utils.conv_utils import convert_kernel
def create_googlenet(weights_path=None):
# creates GoogLeNet a.k.a. Inception v1 (Szegedy, 2015)
input = Input(shape=(3, 224, 224))
input_pad = ZeroPadding2D(padding=(3, 3))(input)
conv1_7x7_s2 = Conv2D(64, (7,7), strides=(2,2), padding='valid', activation='relu', name='conv1/7x7_s2', kernel_regularizer=l2(0.0002))(input_pad)
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), padding='valid', name='pool1/3x3_s2')(pool1_helper)
pool1_norm1 = LRN(name='pool1/norm1')(pool1_3x3_s2)
conv2_3x3_reduce = Conv2D(64, (1,1), padding='same', activation='relu', name='conv2/3x3_reduce', kernel_regularizer=l2(0.0002))(pool1_norm1)
conv2_3x3 = Conv2D(192, (3,3), padding='same', activation='relu', name='conv2/3x3', kernel_regularizer=l2(0.0002))(conv2_3x3_reduce)
conv2_norm2 = LRN(name='conv2/norm2')(conv2_3x3)
conv2_zero_pad = ZeroPadding2D(padding=(1, 1))(conv2_norm2)
pool2_helper = PoolHelper()(conv2_zero_pad)
pool2_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid', name='pool2/3x3_s2')(pool2_helper)
inception_3a_1x1 = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_3a/1x1', kernel_regularizer=l2(0.0002))(pool2_3x3_s2)
inception_3a_3x3_reduce = Conv2D(96, (1,1), padding='same', activation='relu', name='inception_3a/3x3_reduce', kernel_regularizer=l2(0.0002))(pool2_3x3_s2)
inception_3a_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3_reduce)
inception_3a_3x3 = Conv2D(128, (3,3), padding='valid', activation='relu', name='inception_3a/3x3', kernel_regularizer=l2(0.0002))(inception_3a_3x3_pad)
inception_3a_5x5_reduce = Conv2D(16, (1,1), padding='same', activation='relu', name='inception_3a/5x5_reduce', kernel_regularizer=l2(0.0002))(pool2_3x3_s2)
inception_3a_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5_reduce)
inception_3a_5x5 = Conv2D(32, (5,5), padding='valid', activation='relu', name='inception_3a/5x5', kernel_regularizer=l2(0.0002))(inception_3a_5x5_pad)
inception_3a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_3a/pool')(pool2_3x3_s2)
inception_3a_pool_proj = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_3a/pool_proj', kernel_regularizer=l2(0.0002))(inception_3a_pool)
inception_3a_output = Concatenate(axis=1, name='inception_3a/output')([inception_3a_1x1,inception_3a_3x3,inception_3a_5x5,inception_3a_pool_proj])
inception_3b_1x1 = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_3b/1x1', kernel_regularizer=l2(0.0002))(inception_3a_output)
inception_3b_3x3_reduce = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_3b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_3a_output)
inception_3b_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3_reduce)
inception_3b_3x3 = Conv2D(192, (3,3), padding='valid', activation='relu', name='inception_3b/3x3', kernel_regularizer=l2(0.0002))(inception_3b_3x3_pad)
inception_3b_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_3b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_3a_output)
inception_3b_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5_reduce)
inception_3b_5x5 = Conv2D(96, (5,5), padding='valid', activation='relu', name='inception_3b/5x5', kernel_regularizer=l2(0.0002))(inception_3b_5x5_pad)
inception_3b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_3b/pool')(inception_3a_output)
inception_3b_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_3b/pool_proj', kernel_regularizer=l2(0.0002))(inception_3b_pool)
inception_3b_output = Concatenate(axis=1, name='inception_3b/output')([inception_3b_1x1,inception_3b_3x3,inception_3b_5x5,inception_3b_pool_proj])
inception_3b_output_zero_pad = ZeroPadding2D(padding=(1, 1))(inception_3b_output)
pool3_helper = PoolHelper()(inception_3b_output_zero_pad)
pool3_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid', name='pool3/3x3_s2')(pool3_helper)
inception_4a_1x1 = Conv2D(192, (1,1), padding='same', activation='relu', name='inception_4a/1x1', kernel_regularizer=l2(0.0002))(pool3_3x3_s2)
inception_4a_3x3_reduce = Conv2D(96, (1,1), padding='same', activation='relu', name='inception_4a/3x3_reduce', kernel_regularizer=l2(0.0002))(pool3_3x3_s2)
inception_4a_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3_reduce)
inception_4a_3x3 = Conv2D(208, (3,3), padding='valid', activation='relu', name='inception_4a/3x3' ,kernel_regularizer=l2(0.0002))(inception_4a_3x3_pad)
inception_4a_5x5_reduce = Conv2D(16, (1,1), padding='same', activation='relu', name='inception_4a/5x5_reduce', kernel_regularizer=l2(0.0002))(pool3_3x3_s2)
inception_4a_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5_reduce)
inception_4a_5x5 = Conv2D(48, (5,5), padding='valid', activation='relu', name='inception_4a/5x5', kernel_regularizer=l2(0.0002))(inception_4a_5x5_pad)
inception_4a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4a/pool')(pool3_3x3_s2)
inception_4a_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4a/pool_proj', kernel_regularizer=l2(0.0002))(inception_4a_pool)
inception_4a_output = Concatenate(axis=1, name='inception_4a/output')([inception_4a_1x1,inception_4a_3x3,inception_4a_5x5,inception_4a_pool_proj])
loss1_ave_pool = AveragePooling2D(pool_size=(5,5), strides=(3,3), name='loss1/ave_pool')(inception_4a_output)
loss1_conv = Conv2D(128, (1,1), padding='same', activation='relu', name='loss1/conv', kernel_regularizer=l2(0.0002))(loss1_ave_pool)
loss1_flat = Flatten()(loss1_conv)
loss1_fc = Dense(1024, activation='relu', name='loss1/fc', kernel_regularizer=l2(0.0002))(loss1_flat)
loss1_drop_fc = Dropout(rate=0.7)(loss1_fc)
loss1_classifier = Dense(1000, name='loss1/classifier', kernel_regularizer=l2(0.0002))(loss1_drop_fc)
loss1_classifier_act = Activation('softmax')(loss1_classifier)
inception_4b_1x1 = Conv2D(160, (1,1), padding='same', activation='relu', name='inception_4b/1x1', kernel_regularizer=l2(0.0002))(inception_4a_output)
inception_4b_3x3_reduce = Conv2D(112, (1,1), padding='same', activation='relu', name='inception_4b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4a_output)
inception_4b_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4b_3x3_reduce)
inception_4b_3x3 = Conv2D(224, (3,3), padding='valid', activation='relu', name='inception_4b/3x3', kernel_regularizer=l2(0.0002))(inception_4b_3x3_pad)
inception_4b_5x5_reduce = Conv2D(24, (1,1), padding='same', activation='relu', name='inception_4b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4a_output)
inception_4b_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4b_5x5_reduce)
inception_4b_5x5 = Conv2D(64, (5,5), padding='valid', activation='relu', name='inception_4b/5x5', kernel_regularizer=l2(0.0002))(inception_4b_5x5_pad)
inception_4b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4b/pool')(inception_4a_output)
inception_4b_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4b/pool_proj', kernel_regularizer=l2(0.0002))(inception_4b_pool)
inception_4b_output = Concatenate(axis=1, name='inception_4b/output')([inception_4b_1x1,inception_4b_3x3,inception_4b_5x5,inception_4b_pool_proj])
inception_4c_1x1 = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_4c/1x1', kernel_regularizer=l2(0.0002))(inception_4b_output)
inception_4c_3x3_reduce = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_4c/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4b_output)
inception_4c_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4c_3x3_reduce)
inception_4c_3x3 = Conv2D(256, (3,3), padding='valid', activation='relu', name='inception_4c/3x3', kernel_regularizer=l2(0.0002))(inception_4c_3x3_pad)
inception_4c_5x5_reduce = Conv2D(24, (1,1), padding='same', activation='relu', name='inception_4c/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4b_output)
inception_4c_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4c_5x5_reduce)
inception_4c_5x5 = Conv2D(64, (5,5), padding='valid', activation='relu', name='inception_4c/5x5', kernel_regularizer=l2(0.0002))(inception_4c_5x5_pad)
inception_4c_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4c/pool')(inception_4b_output)
inception_4c_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4c/pool_proj', kernel_regularizer=l2(0.0002))(inception_4c_pool)
inception_4c_output = Concatenate(axis=1, name='inception_4c/output')([inception_4c_1x1,inception_4c_3x3,inception_4c_5x5,inception_4c_pool_proj])
inception_4d_1x1 = Conv2D(112, (1,1), padding='same', activation='relu', name='inception_4d/1x1', kernel_regularizer=l2(0.0002))(inception_4c_output)
inception_4d_3x3_reduce = Conv2D(144, (1,1), padding='same', activation='relu', name='inception_4d/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4c_output)
inception_4d_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4d_3x3_reduce)
inception_4d_3x3 = Conv2D(288, (3,3), padding='valid', activation='relu', name='inception_4d/3x3', kernel_regularizer=l2(0.0002))(inception_4d_3x3_pad)
inception_4d_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_4d/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4c_output)
inception_4d_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4d_5x5_reduce)
inception_4d_5x5 = Conv2D(64, (5,5), padding='valid', activation='relu', name='inception_4d/5x5', kernel_regularizer=l2(0.0002))(inception_4d_5x5_pad)
inception_4d_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4d/pool')(inception_4c_output)
inception_4d_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4d/pool_proj', kernel_regularizer=l2(0.0002))(inception_4d_pool)
inception_4d_output = Concatenate(axis=1, name='inception_4d/output')([inception_4d_1x1,inception_4d_3x3,inception_4d_5x5,inception_4d_pool_proj])
loss2_ave_pool = AveragePooling2D(pool_size=(5,5), strides=(3,3), name='loss2/ave_pool')(inception_4d_output)
loss2_conv = Conv2D(128, (1,1), padding='same', activation='relu', name='loss2/conv', kernel_regularizer=l2(0.0002))(loss2_ave_pool)
loss2_flat = Flatten()(loss2_conv)
loss2_fc = Dense(1024, activation='relu', name='loss2/fc', kernel_regularizer=l2(0.0002))(loss2_flat)
loss2_drop_fc = Dropout(rate=0.7)(loss2_fc)
loss2_classifier = Dense(1000, name='loss2/classifier', kernel_regularizer=l2(0.0002))(loss2_drop_fc)
loss2_classifier_act = Activation('softmax')(loss2_classifier)
inception_4e_1x1 = Conv2D(256, (1,1), padding='same', activation='relu', name='inception_4e/1x1', kernel_regularizer=l2(0.0002))(inception_4d_output)
inception_4e_3x3_reduce = Conv2D(160, (1,1), padding='same', activation='relu', name='inception_4e/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4d_output)
inception_4e_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3_reduce)
inception_4e_3x3 = Conv2D(320, (3,3), padding='valid', activation='relu', name='inception_4e/3x3', kernel_regularizer=l2(0.0002))(inception_4e_3x3_pad)
inception_4e_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_4e/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4d_output)
inception_4e_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5_reduce)
inception_4e_5x5 = Conv2D(128, (5,5), padding='valid', activation='relu', name='inception_4e/5x5', kernel_regularizer=l2(0.0002))(inception_4e_5x5_pad)
inception_4e_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4e/pool')(inception_4d_output)
inception_4e_pool_proj = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_4e/pool_proj', kernel_regularizer=l2(0.0002))(inception_4e_pool)
inception_4e_output = Concatenate(axis=1, name='inception_4e/output')([inception_4e_1x1,inception_4e_3x3,inception_4e_5x5,inception_4e_pool_proj])
inception_4e_output_zero_pad = ZeroPadding2D(padding=(1, 1))(inception_4e_output)
pool4_helper = PoolHelper()(inception_4e_output_zero_pad)
pool4_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid', name='pool4/3x3_s2')(pool4_helper)
inception_5a_1x1 = Conv2D(256, (1,1), padding='same', activation='relu', name='inception_5a/1x1', kernel_regularizer=l2(0.0002))(pool4_3x3_s2)
inception_5a_3x3_reduce = Conv2D(160, (1,1), padding='same', activation='relu', name='inception_5a/3x3_reduce', kernel_regularizer=l2(0.0002))(pool4_3x3_s2)
inception_5a_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3_reduce)
inception_5a_3x3 = Conv2D(320, (3,3), padding='valid', activation='relu', name='inception_5a/3x3', kernel_regularizer=l2(0.0002))(inception_5a_3x3_pad)
inception_5a_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_5a/5x5_reduce', kernel_regularizer=l2(0.0002))(pool4_3x3_s2)
inception_5a_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_5a_5x5_reduce)
inception_5a_5x5 = Conv2D(128, (5,5), padding='valid', activation='relu', name='inception_5a/5x5', kernel_regularizer=l2(0.0002))(inception_5a_5x5_pad)
inception_5a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_5a/pool')(pool4_3x3_s2)
inception_5a_pool_proj = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_5a/pool_proj', kernel_regularizer=l2(0.0002))(inception_5a_pool)
inception_5a_output = Concatenate(axis=1, name='inception_5a/output')([inception_5a_1x1,inception_5a_3x3,inception_5a_5x5,inception_5a_pool_proj])
inception_5b_1x1 = Conv2D(384, (1,1), padding='same', activation='relu', name='inception_5b/1x1', kernel_regularizer=l2(0.0002))(inception_5a_output)
inception_5b_3x3_reduce = Conv2D(192, (1,1), padding='same', activation='relu', name='inception_5b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_5a_output)
inception_5b_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3_reduce)
inception_5b_3x3 = Conv2D(384, (3,3), padding='valid', activation='relu', name='inception_5b/3x3', kernel_regularizer=l2(0.0002))(inception_5b_3x3_pad)
inception_5b_5x5_reduce = Conv2D(48, (1,1), padding='same', activation='relu', name='inception_5b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_5a_output)
inception_5b_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_5b_5x5_reduce)
inception_5b_5x5 = Conv2D(128, (5,5), padding='valid', activation='relu', name='inception_5b/5x5', kernel_regularizer=l2(0.0002))(inception_5b_5x5_pad)
inception_5b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_5b/pool')(inception_5a_output)
inception_5b_pool_proj = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_5b/pool_proj', kernel_regularizer=l2(0.0002))(inception_5b_pool)
inception_5b_output = Concatenate(axis=1, name='inception_5b/output')([inception_5b_1x1,inception_5b_3x3,inception_5b_5x5,inception_5b_pool_proj])
pool5_7x7_s1 = AveragePooling2D(pool_size=(7,7), strides=(1,1), name='pool5/7x7_s2')(inception_5b_output)
loss3_flat = Flatten()(pool5_7x7_s1)
pool5_drop_7x7_s1 = Dropout(rate=0.4)(loss3_flat)
loss3_classifier = Dense(1000, name='loss3/classifier', kernel_regularizer=l2(0.0002))(pool5_drop_7x7_s1)
loss3_classifier_act = Activation('softmax', name='prob')(loss3_classifier)
googlenet = Model(inputs=input, outputs=[loss1_classifier_act,loss2_classifier_act,loss3_classifier_act])
if weights_path:
googlenet.load_weights(weights_path)
if keras.backend.backend() == 'tensorflow':
# convert the convolutional kernels for tensorflow
ops = []
for layer in googlenet.layers:
if layer.__class__.__name__ == 'Conv2D':
original_w = K.get_value(layer.kernel)
converted_w = convert_kernel(original_w)
ops.append(tf.assign(layer.kernel, converted_w).op)
K.get_session().run(ops)
return googlenet
if __name__ == "__main__":
img = imageio.imread('cat.jpg', pilmode='RGB')
img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32)
img[:, :, 0] -= 123.68
img[:, :, 1] -= 116.779
img[:, :, 2] -= 103.939
img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, axis=0)
# Test pretrained model
model = create_googlenet('googlenet_weights.h5')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
out = model.predict(img) # note: the model has three outputs
labels = np.loadtxt('synset_words.txt', str, delimiter='\t')
predicted_label = np.argmax(out[2])
predicted_class_name = labels[predicted_label]
print('Predicted Class: ', predicted_label, ', Class Name: ', predicted_class_name)
from keras.layers.core import Layer
from keras import backend as K
if K.backend() == 'theano':
import theano.tensor as T
elif K.backend() == 'tensorflow':
import tensorflow as tf
else:
raise NotImplementedError
class LRN(Layer):
def __init__(self, alpha=0.0001, k=1, beta=0.75, n=5, **kwargs):
self.alpha = alpha
self.k = k
self.beta = beta
self.n = n
super(LRN, self).__init__(**kwargs)
def call(self, x, mask=None):
b, ch, r, c = x.shape
half_n = self.n // 2 # half the local region
input_sqr = K.square(x) # square the input
if K.backend() == 'theano':
# make an empty tensor with zero pads along channel dimension
zeros = T.alloc(0., b, ch + 2*half_n, r, c)
# set the center to be the squared input
input_sqr = T.set_subtensor(zeros[:, half_n:half_n+ch, :, :], input_sqr)
else:
input_sqr = tf.pad(input_sqr, [[0, 0], [half_n, half_n], [0, 0], [0, 0]])
scale = self.k # offset for the scale
norm_alpha = self.alpha / self.n # normalized alpha
for i in range(self.n):
scale += norm_alpha * input_sqr[:, i:i+ch, :, :]
scale = scale ** self.beta
x = x / scale
return x
def get_config(self):
config = {"alpha": self.alpha,
"k": self.k,
"beta": self.beta,
"n": self.n}
base_config = super(LRN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
from keras.layers.core import Layer
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()
return dict(list(base_config.items()) + list(config.items()))
@debaditya-unimelb
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debaditya-unimelb commented Sep 18, 2020

@swghosh Also, I notice that I could perhaps remove all the ZeroPadding2D paddings, and replace the next convolutional layer with padding='same', and the code will still work. Will it be wrong to do so? If not what will be the difference?

@mikechen66
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mikechen66 commented Oct 5, 2020

I change the whole code to comply with TensorFlow 2.x as follows. But users need to generate the new googlenet weights in the h5 format in order to adapt the above TensorFlow data_format.

1. Change the data_format setting based on TensorFlow 2.x

Change "axis = 1" to "axis = -1" to comply with TensorFlow. It corresponds to "channel_dim = -1 if K.image_data_format() == 'channels_last' ". It is very annoying to change the parameter data_format=='channels_first' in keras.json regularly because most of the applications being developed by TensorFlow. It is the most popular setting in the Machine Learning. '

2. Remove flatten layers

The flatten layers indirectly help to generate huge parameters int the dense layers in both the main and auxiliary classifiers. After removing the expensive flatten layers, I get the correct total size of 9+ million parameters. it is very close to the total size that Google has announced.

3. Change the padding parameter

Change the parameter 'padding=valid' to 'padding='same' in the Inception section to completely comply with the official GoogleNet Paper.

4. Delete the parameter of zero-padding

The function of ZeroPadding2D is not useful in the TF realization as follows.

5. GoogleNet weights

The original googlenet weights could not be used i the following new script since the incompatible data_format. Therefore, users need to generate the new googlenet weights in the h5 format in order to adapt to TensorFlow (width, height, channels).

6. The script is modified as follows.

import imageio
from PIL import Image
import numpy as np
import tensorflow as tf

import keras
from keras.models import Model
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, \
    Dropout, Flatten, Concatenate, Reshape, Activation

from keras.regularizers import l2
from keras.optimizers import SGD
from lrn import LRN 
from keras import backend


# Set up the GPU to avoid the runtime error: Could not create cuDNN handle...
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

# Define the Googlenet class 
class Googlenet(object):

    # Adopt the static method to enbale the elegant realization of the model  
    @staticmethod
    # Build the GoogLeNet Inveption v1
    def build(input_shape, num_classes):

        input = Input(shape=input_shape)
 
        conv1_7x7_s2 = Conv2D(64, kernel_size=(7,7), strides=(2,2), padding='same', activation='relu', name='conv1/7x7_s2', kernel_regularizer=l2(0.0002))(input)
        pool1_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same', name='pool1/3x3_s2')(conv1_7x7_s2)
        pool1_norm1 = LRN(name='pool1/norm1')( pool1_3x3_s2)
        conv2_3x3_reduce = Conv2D(64, kernel_size=(1,1), padding='valid', activation='relu', name='conv2/3x3_reduce', kernel_regularizer=l2(0.0002))(pool1_norm1)
        conv2_3x3 = Conv2D(192, kernel_size=(3,3), padding='same', activation='relu', name='conv2/3x3', kernel_regularizer=l2(0.0002))(conv2_3x3_reduce)
        conv2_norm2 = LRN(name='conv2/norm2')(conv2_3x3)
        pool2_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same', name='pool2/3x3_s2')(conv2_norm2)

        inception_3a_1x1 = Conv2D(64, kernel_size=(1,1), padding='same', activation='relu', name='inception_3a/1x1', kernel_regularizer=l2(0.0002))(pool2_3x3_s2)
        inception_3a_3x3_reduce = Conv2D(96, kernel_size=(1,1), padding='same', activation='relu', name='inception_3a/3x3_reduce', kernel_regularizer=l2(0.0002))(pool2_3x3_s2)
        inception_3a_3x3 = Conv2D(128, kernel_size=(3,3), padding='same', activation='relu', name='inception_3a/3x3', kernel_regularizer=l2(0.0002))(inception_3a_3x3_reduce)
        inception_3a_5x5_reduce = Conv2D(16, kernel_size=(1,1), padding='same', activation='relu', name='inception_3a/5x5_reduce', kernel_regularizer=l2(0.0002))(pool2_3x3_s2)
        inception_3a_5x5 = Conv2D(32, kernel_size=(5,5), padding='same', activation='relu', name='inception_3a/5x5', kernel_regularizer=l2(0.0002))(inception_3a_5x5_reduce)
        inception_3a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_3a/pool')(pool2_3x3_s2)
        inception_3a_pool_proj = Conv2D(32, kernel_size=(1,1), padding='same', activation='relu', name='inception_3a/pool_proj', kernel_regularizer=l2(0.0002))(inception_3a_pool)
        inception_3a_output = Concatenate(axis=-1, name='inception_3a/output')([inception_3a_1x1, inception_3a_3x3, inception_3a_5x5, inception_3a_pool_proj])

        inception_3b_1x1 = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='inception_3b/1x1', kernel_regularizer=l2(0.0002))(inception_3a_output)
        inception_3b_3x3_reduce = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='inception_3b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_3a_output)
        inception_3b_3x3 = Conv2D(192, kernel_size=(3,3), padding='same', activation='relu', name='inception_3b/3x3', kernel_regularizer=l2(0.0002))(inception_3b_3x3_reduce)
        inception_3b_5x5_reduce = Conv2D(32, kernel_size=(1,1), padding='same', activation='relu', name='inception_3b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_3a_output)
        inception_3b_5x5 = Conv2D(96, kernel_size=(5,5), padding='same', activation='relu', name='inception_3b/5x5', kernel_regularizer=l2(0.0002))(inception_3b_5x5_reduce)
        inception_3b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_3b/pool')(inception_3a_output)
        inception_3b_pool_proj = Conv2D(64, kernel_size=(1,1), padding='same', activation='relu', name='inception_3b/pool_proj', kernel_regularizer=l2(0.0002))(inception_3b_pool)
        inception_3b_output = Concatenate(axis=-1, name='inception_3b/output')([inception_3b_1x1, inception_3b_3x3, inception_3b_5x5, inception_3b_pool_proj])

        inception_4a_1x1 = Conv2D(192, kernel_size=(1,1), padding='same', activation='relu', name='inception_4a/1x1', kernel_regularizer=l2(0.0002))(inception_3b_output)
        inception_4a_3x3_reduce = Conv2D(96, kernel_size=(1,1), padding='same', activation='relu', name='inception_4a/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_3b_output)
        inception_4a_3x3 = Conv2D(208,kernel_size=(3,3), padding='same', activation='relu', name='inception_4a/3x3' ,kernel_regularizer=l2(0.0002))(inception_4a_3x3_reduce)
        inception_4a_5x5_reduce = Conv2D(16, kernel_size=(1,1), padding='same', activation='relu', name='inception_4a/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_3b_output)
        inception_4a_5x5 = Conv2D(48, kernel_size=(5,5), padding='same', activation='relu', name='inception_4a/5x5', kernel_regularizer=l2(0.0002))(inception_4a_5x5_reduce)
        inception_4a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4a/pool')(inception_3b_output)
        inception_4a_pool_proj = Conv2D(64, kernel_size=(1,1), padding='same', activation='relu', name='inception_4a/pool_proj', kernel_regularizer=l2(0.0002))(inception_4a_pool)
        inception_4a_output = Concatenate(axis=-1, name='inception_4a/output')([inception_4a_1x1, inception_4a_3x3, inception_4a_5x5, inception_4a_pool_proj])

        loss1_ave_pool = AveragePooling2D(pool_size=(5,5), strides=(3,3), name='loss1/ave_pool')(inception_4a_output)
        loss1_conv = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='loss1/conv', kernel_regularizer=l2(0.0002))(loss1_ave_pool)
        loss1_fc = Dense(1024, activation='relu', name='loss1/fc', kernel_regularizer=l2(0.0002))(loss1_conv)
        loss1_drop_fc = Dropout(rate=0.7)(loss1_fc)
        loss1_classifier = Dense(num_classes, name='loss1/classifier', kernel_regularizer=l2(0.0002))(loss1_drop_fc)
        loss1_classifier_act = Activation('softmax')(loss1_classifier)

        inception_4b_1x1 = Conv2D(160, kernel_size=(1,1), padding='same', activation='relu', name='inception_4b/1x1', kernel_regularizer=l2(0.0002))(inception_4a_output)
        inception_4b_3x3_reduce = Conv2D(112, kernel_size=(1,1), padding='same', activation='relu', name='inception_4b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4a_output)
        inception_4b_3x3 = Conv2D(224, kernel_size=(3,3), padding='same', activation='relu', name='inception_4b/3x3', kernel_regularizer=l2(0.0002))(inception_4b_3x3_reduce)
        inception_4b_5x5_reduce = Conv2D(24, kernel_size=(1,1), padding='same', activation='relu', name='inception_4b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4a_output)
        inception_4b_5x5 = Conv2D(64, kernel_size=(5,5), padding='same', activation='relu', name='inception_4b/5x5', kernel_regularizer=l2(0.0002))(inception_4b_5x5_reduce)
        inception_4b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4b/pool')(inception_4a_output)
        inception_4b_pool_proj = Conv2D(64, kernel_size=(1,1), padding='same', activation='relu', name='inception_4b/pool_proj', kernel_regularizer=l2(0.0002))(inception_4b_pool)
        inception_4b_output = Concatenate(axis=-1, name='inception_4b/output')([inception_4b_1x1, inception_4b_3x3, inception_4b_5x5, inception_4b_pool_proj])
        
        inception_4c_1x1 = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='inception_4c/1x1', kernel_regularizer=l2(0.0002))(inception_4b_output)
        inception_4c_3x3_reduce = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='inception_4c/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4b_output)
        inception_4c_3x3 = Conv2D(256, kernel_size=(3,3), padding='same', activation='relu', name='inception_4c/3x3', kernel_regularizer=l2(0.0002))(inception_4c_3x3_reduce)
        inception_4c_5x5_reduce = Conv2D(24, kernel_size=(1,1), padding='same', activation='relu', name='inception_4c/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4b_output)
        inception_4c_5x5 = Conv2D(64, kernel_size=(5,5), padding='same', activation='relu', name='inception_4c/5x5', kernel_regularizer=l2(0.0002))(inception_4c_5x5_reduce)
        inception_4c_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4c/pool')(inception_4b_output)
        inception_4c_pool_proj = Conv2D(64, kernel_size=(1,1), padding='same', activation='relu', name='inception_4c/pool_proj', kernel_regularizer=l2(0.0002))(inception_4c_pool)
        inception_4c_output = Concatenate(axis=-1, name='inception_4c/output')([inception_4c_1x1, inception_4c_3x3, inception_4c_5x5, inception_4c_pool_proj])

        inception_4d_1x1 = Conv2D(112, kernel_size=(1,1), padding='same', activation='relu', name='inception_4d/1x1', kernel_regularizer=l2(0.0002))(inception_4c_output)
        inception_4d_3x3_reduce = Conv2D(144, kernel_size=(1,1), padding='same', activation='relu', name='inception_4d/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4c_output)
        inception_4d_3x3 = Conv2D(288, kernel_size=(3,3), padding='same', activation='relu', name='inception_4d/3x3', kernel_regularizer=l2(0.0002))(inception_4d_3x3_reduce)
        inception_4d_5x5_reduce = Conv2D(32, kernel_size=(1,1), padding='same', activation='relu', name='inception_4d/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4c_output)
        inception_4d_5x5 = Conv2D(64, kernel_size=(5,5), padding='same', activation='relu', name='inception_4d/5x5', kernel_regularizer=l2(0.0002))(inception_4d_5x5_reduce)
        inception_4d_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4d/pool')(inception_4c_output)
        inception_4d_pool_proj = Conv2D(64, kernel_size=(1,1), padding='same', activation='relu', name='inception_4d/pool_proj', kernel_regularizer=l2(0.0002))(inception_4d_pool)
        inception_4d_output = Concatenate(axis=-1, name='inception_4d/output')([inception_4d_1x1, inception_4d_3x3, inception_4d_5x5, inception_4d_pool_proj])
    
        loss2_ave_pool = AveragePooling2D(pool_size=(5,5), strides=(3,3), name='loss2/ave_pool')(inception_4d_output)
        loss2_conv = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='loss2/conv', kernel_regularizer=l2(0.0002))(loss2_ave_pool)
        loss2_fc = Dense(1024, activation='relu', name='loss2/fc', kernel_regularizer=l2(0.0002))(loss2_conv)
        loss2_drop_fc = Dropout(rate=0.7)(loss2_fc)
        loss2_classifier = Dense(num_classes, name='loss2/classifier', kernel_regularizer=l2(0.0002))(loss2_drop_fc)
        loss2_classifier_act = Activation('softmax')(loss2_classifier)


        inception_4e_1x1 = Conv2D(256, kernel_size=(1,1), padding='same', activation='relu', name='inception_4e/1x1', kernel_regularizer=l2(0.0002))(inception_4d_output)
        inception_4e_3x3_reduce = Conv2D(160, kernel_size=(1,1), padding='same', activation='relu', name='inception_4e/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4d_output)
        inception_4e_3x3 = Conv2D(320, kernel_size=(3,3), padding='same', activation='relu', name='inception_4e/3x3', kernel_regularizer=l2(0.0002))(inception_4e_3x3_reduce)
        inception_4e_5x5_reduce = Conv2D(32, kernel_size=(1,1), padding='same', activation='relu', name='inception_4e/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4d_output)
        inception_4e_5x5 = Conv2D(128, kernel_size=(5,5), padding='same', activation='relu', name='inception_4e/5x5', kernel_regularizer=l2(0.0002))(inception_4e_5x5_reduce)
        inception_4e_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4e/pool')(inception_4d_output)
        inception_4e_pool_proj = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='inception_4e/pool_proj', kernel_regularizer=l2(0.0002))(inception_4e_pool)
        inception_4e_output = Concatenate(axis=-1, name='inception_4e/output')([inception_4e_1x1, inception_4e_3x3, inception_4e_5x5, inception_4e_pool_proj])


        inception_5a_1x1 = Conv2D(256, kernel_size=(1,1), padding='same', activation='relu', name='inception_5a/1x1', kernel_regularizer=l2(0.0002))(inception_4e_output)
        inception_5a_3x3_reduce = Conv2D(160, kernel_size=(1,1), padding='same', activation='relu', name='inception_5a/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4e_output)
        inception_5a_3x3 = Conv2D(320, kernel_size=(3,3), padding='same', activation='relu', name='inception_5a/3x3', kernel_regularizer=l2(0.0002))(inception_5a_3x3_reduce)
        inception_5a_5x5_reduce = Conv2D(32, kernel_size=(1,1), padding='same', activation='relu', name='inception_5a/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4e_output)
        inception_5a_5x5 = Conv2D(128, kernel_size=(5,5), padding='same', activation='relu', name='inception_5a/5x5', kernel_regularizer=l2(0.0002))(inception_5a_5x5_reduce)
        inception_5a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_5a/pool')(inception_4e_output)
        inception_5a_pool_proj = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='inception_5a/pool_proj', kernel_regularizer=l2(0.0002))(inception_5a_pool)
        inception_5a_output = Concatenate(axis=-1, name='inception_5a/output')([inception_5a_1x1, inception_5a_3x3, inception_5a_5x5, inception_5a_pool_proj])


        inception_5b_1x1 = Conv2D(384, kernel_size=(1,1), padding='same', activation='relu', name='inception_5b/1x1', kernel_regularizer=l2(0.0002))(inception_5a_output)
        inception_5b_3x3_reduce = Conv2D(192, kernel_size=(1,1), padding='same', activation='relu', name='inception_5b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_5a_output)
        inception_5b_3x3 = Conv2D(384, kernel_size=(3,3), padding='same', activation='relu', name='inception_5b/3x3', kernel_regularizer=l2(0.0002))(inception_5b_3x3_reduce)
        inception_5b_5x5_reduce = Conv2D(48, kernel_size=(1,1), padding='same', activation='relu', name='inception_5b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_5a_output)
        inception_5b_5x5 = Conv2D(128, kernel_size=(5,5), padding='same', activation='relu', name='inception_5b/5x5', kernel_regularizer=l2(0.0002))(inception_5b_5x5_reduce)
        inception_5b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_5b/pool')(inception_5a_output)
        inception_5b_pool_proj = Conv2D(128, kernel_size=(1,1), padding='same', activation='relu', name='inception_5b/pool_proj', kernel_regularizer=l2(0.0002))(inception_5b_pool)
        inception_5b_output = Concatenate(axis=-1, name='inception_5b/output')([inception_5b_1x1, inception_5b_3x3, inception_5b_5x5, inception_5b_pool_proj])


        pool5_7x7_s1 = AveragePooling2D(pool_size=(7,7), strides=(1,1), name='pool5/7x7_s2')(inception_5b_output)
        pool5_drop_7x7_s1 = Dropout(rate=0.4)(pool5_7x7_s1)
        loss3_classifier = Dense(num_classes, name='loss3/classifier', kernel_regularizer=l2(0.0002))(pool5_drop_7x7_s1)
        loss3_classifier_act = Activation('softmax', name='prob')(loss3_classifier)

        inception_v1 = Model(inputs=input, outputs=[loss1_classifier_act, loss2_classifier_act, loss3_classifier_act])

        return inception_v1


if __name__ == "__main__":

    input_shape = (224, 224, 3)
    num_classes = 1000

    inception_v1 = Googlenet.build(input_shape, num_classes)

    inception_v1.summary()

@mikechen66
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mikechen66 commented Oct 5, 2020

Even though the plain model of Inception v1 has the detailed description of the layers, I prefer to the simplified model with the total size of 6+ million parameters(removing auxiliary classifiers) as follows. Since swghosh provided the googlenet_custom_layers.py, I has changed its name to lrn.py as a library.

import tensorflow as tf 
from tensorflow.keras.layers import Input, Conv2D, Dense, Dropout, MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2
from lrn import LRN 

# Set up the GPU to avoid the runtime error: Could not create cuDNN handle...
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

def googlenet(input_shape, num_classes):

    input = Input(shape=input_shape)

    conv1_7x7 = Conv2D(filters=64, kernel_size=(7,7), strides=(2,2), padding='same', activation='relu', 
                       kernel_regularizer=l2(0.01))(input)
    maxpool1_3x3 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(conv1_7x7)
    pool1_norm1 = LRN()(maxpool1_3x3)
    conv2_3x3_reduce = Conv2D(filters=64, kernel_size=(1,1),  strides=(1,1), padding='valid', activation='relu', 
                       kernel_regularizer=l2(0.01))(pool1_norm1)
    conv2_3x3 = Conv2D(filters=192, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', 
                       kernel_regularizer=l2(0.01))(conv2_3x3_reduce)
    conv2_norm2 = LRN()(conv2_3x3)
    maxpool2_3x3 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(conv2_norm2)

    inception_3a = inception(input=maxpool2_3x3, axis=3, params=[(64,),(96,128),(16,32),(32,)])
    inception_3b = inception(input=inception_3a, axis=3, params=[(128,),(128,192),(32,96),(64,)])
    maxpool3_3x3 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(inception_3b)

    inception_4a = inception(input=maxpool3_3x3, axis=3, params=[(192,),(96,208),(16,48),(64,)])
    inception_4b = inception(input=inception_4a, axis=3, params=[(160,),(112,224),(24,64),(64,)])
    inception_4c = inception(input=inception_4b, axis=3, params=[(128,),(128,256),(24,64),(64,)])
    inception_4d = inception(input=inception_4c, axis=3, params=[(112,),(144,288),(32,64),(64,)])
    inception_4e = inception(input=inception_4d, axis=3, params=[(256,),(160,320),(32,128),(128,)])
    maxpool4_3x3 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(inception_4e)

    inception_5a = inception(input=maxpool4_3x3, axis=3, params=[(256,),(160,320),(32,128),(128,)])
    inception_5b = inception(input=inception_5a, axis=3, params=[(384,),(192,384),(48,128),(128,)]) 
    avgpool1_7x7 = AveragePooling2D(pool_size=(7,7), strides=(7,7), padding='same')(inception_5b)

    drop = Dropout(rate=0.4)(avgpool1_7x7)
    linear = Dense(num_classes, activation='softmax', kernel_regularizer=l2(0.01))(drop)
    
    model = Model(inputs=input, outputs=linear)

    return model 

def inception(input, axis, params):

    # Bind the vertical cells tegother for an elegant realization 
    [branch1, branch2, branch3, branch4] = params

    conv_11 = Conv2D(filters=branch1[0], kernel_size=(1,1), padding='same', activation='relu', 
                     kernel_regularizer=l2(0.01))(input)

    conv_12 = Conv2D(filters=branch2[0], kernel_size=(1,1), padding='same', activation='relu', 
                     kernel_regularizer=l2(0.01))(input)
    conv_22 = Conv2D(filters=branch2[1], kernel_size=(3,3), padding='same', activation='relu', 
                     kernel_regularizer=l2(0.01))(conv_12)

    conv_13 = Conv2D(filters=branch3[0], kernel_size=(1,1), padding='same', activation='relu', 
                     kernel_regularizer=l2(0.01))(input)
    conv_23 = Conv2D(filters=branch3[1], kernel_size=(5,5), padding='same', activation='relu', 
                     kernel_regularizer=l2(0.01))(conv_13)

    maxpool_14 = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(input)
    maxpool_proj_24 = Conv2D(filters=branch4[0], kernel_size=(1,1), strides=(1,1), padding='same', 
                             activation='relu', kernel_regularizer=l2(0.01))(maxpool_14)

    inception_output = concatenate([conv_11, conv_22, conv_23, maxpool_proj_24], axis=3)  

    return inception_output

if __name__ == "__main__":

    input_shape = (224, 224, 3)
    num_classes = 1000

    # Assign the values 
    model = googlenet(input_shape, num_classes)

    model.summary()

Cheers!

@babloogpb1
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babloogpb1 commented Oct 29, 2020

Thanks for your reply. I use Keras 2.4.3 and TensorFlow 2.3. It might be a reason for the issue of version compatibility. After updating the following lines of code to adapt to the above-mentioned environment, I can run the script with the correct classification. But I get the wrong total parameter number while running googlenet.summary().

1. Modify the script to adapt to TensorFlow 2.4.3 and Keras 2.4

Predicted Class: 282 , Class Name: n02123159 tiger cat

I make the following modification.

Modify the import statements

if keras.backend.backend() == 'tensorflow':
    # -from keras import backend as K
    from keras import backend
    # -import tensorflow as tf
    import tensorflow.compat.v1 as tf
    tf.compat.v1.disable_eager_execution()
    from keras.utils.conv_utils import convert_kernel

Set up the GPU to avoid the runtime error: Could not create cuDNN handle...

gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

Delete the following lines of code highlighted with "# -" and add the new lines of code below the highlighted code.

    if keras.backend.backend() == 'tensorflow':
        # convert the convolutional kernels for tensorflow
        ops = []
        for layer in googlenet.layers:
            if layer.__class__.__name__ == 'Conv2D':
                # -original_w = K.get_value(layer.kernel)
                original_w = keras.backend.get_value(layer.kernel)
                converted_w = convert_kernel(original_w)
                # -ops.append(tf.assign(layer.kernel, converted_w).op)
                ops.append(tf.compat.v1.assign(layer.kernel, converted_w).op)
        # -K.get_session().run(ops)
        tf.compat.v1.keras.backend.get_session().run(ops)

2. Total parameter number(wrong)

I get the total parameter number of 13,378,280. But the original GoogLeNet Inception v1 has 5.79+ million parameters in total. What's wrong with the huge gap of the total parameter numbers?

After adding the three line of code and deleting the sections including the sections from "if weights_path..." to the main section.

input = Input(shape=(3, 224, 224))
googlenet = create_googlenet(input)
googlenet.summary()

Reference:
Google Inception v1 Paper: Page: 5/9
https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf

Cheers

Thank you for the help!

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