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Fiifi1/googlenet.py

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GoogLeNet in Keras
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readme.md

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.

Raw
googlenet.py
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)
Raw
lrn.py
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()))
Raw
pool_helper.py
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()))
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