AlexNet
Tags: aipool, ai, keras, alexnet
Published: 2021-05-29 16:01:14

### ImageNet Classification with Deep Convolutional Neural Networks

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.

[Paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)

# Implementations
In 2012, AlexNet significantly outperformed all the prior competitors and won the challenge by reducing the top-5 error from 26% to 15.3%. The second place top-5 error rate, which was not a CNN variation, was around 26.2%.

```python
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
np.random.seed(1000)
#Instantiate an empty model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=96, input_shape=(224,224,3), kernel_size=(11,11), strides=(4,4), padding=’valid’))
model.add(Activation(‘relu’))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding=’valid’))

# 2nd Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding=’valid’))
model.add(Activation(‘relu’))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding=’valid’))

# 3rd Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding=’valid’))
model.add(Activation(‘relu’))

# 4th Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding=’valid’))
model.add(Activation(‘relu’))

# 5th Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding=’valid’))
model.add(Activation(‘relu’))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding=’valid’))

# Passing it to a Fully Connected layer
model.add(Flatten())
# 1st Fully Connected Layer
model.add(Dense(4096, input_shape=(224*224*3,)))
model.add(Activation(‘relu’))
# Add Dropout to prevent overfitting
model.add(Dropout(0.4))

# 2nd Fully Connected Layer
model.add(Dense(4096))
model.add(Activation(‘relu’))
# Add Dropout
model.add(Dropout(0.4))

# 3rd Fully Connected Layer
model.add(Dense(1000))
model.add(Activation(‘relu’))
# Add Dropout
model.add(Dropout(0.4))

# Output Layer
model.add(Dense(17))
model.add(Activation(‘softmax’))

model.summary()

# Compile the model
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=’adam’, metrics=[“accuracy”])
```
*Author of the implementation: @engmrk*

[AlexNet](https://ai-pool.com/m/alexnet-1564424451)

### Other Resources
* [MobileNetV2](https://ai-pool.com/m/mobilenetv2-1555182846)
* [ENet](https://ai-pool.com/m/enet-1555434151)
* [SegNet](https://ai-pool.com/m/segnet-1555409707)
* [CCNet](https://ai-pool.com/m/ccnet-1555951522)
* [FPN](https://ai-pool.com/m/fpn-1555775451)