Code Implementation of AlexNet

!pip install tflearn 

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)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential 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'))
# Pooling 
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation before passing it to the next layer
model.add(BatchNormalization())

# 2nd Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# 3rd Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Batch Normalisation
model.add(BatchNormalization())

# 4th Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Batch Normalisation
model.add(BatchNormalization())

# 5th Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(4096, input_shape=(224*224*3,)))
model.add(Activation('relu'))
# Add Dropout to prevent overfitting
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# 2nd Dense Layer
model.add(Dense(4096))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# 3rd Dense Layer
model.add(Dense(1000))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

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

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam',\
 metrics=['accuracy'])

# (5) Train
model.fit(x, y, batch_size=64, epochs=1, verbose=1, \
validation_split=0.2, shuffle=True)

WARNING:tensorflow:From C:\Users\DELL\anaconda3\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
curses is not supported on this machine (please install/reinstall curses for an optimal experience)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 54, 54, 96)        34944     
_________________________________________________________________
activation (Activation)      (None, 54, 54, 96)        0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 27, 27, 96)        0         
_________________________________________________________________
batch_normalization (BatchNo (None, 27, 27, 96)        384       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 17, 17, 256)       2973952   
_________________________________________________________________
activation_1 (Activation)    (None, 17, 17, 256)       0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 256)         0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 8, 8, 256)         1024      
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 6, 6, 384)         885120    
_________________________________________________________________
activation_2 (Activation)    (None, 6, 6, 384)         0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 6, 6, 384)         1536      
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 4, 384)         1327488   
_________________________________________________________________
activation_3 (Activation)    (None, 4, 4, 384)         0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 4, 4, 384)         1536      
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 2, 2, 256)         884992    
_________________________________________________________________
activation_4 (Activation)    (None, 2, 2, 256)         0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 1, 1, 256)         0         
_________________________________________________________________
batch_normalization_4 (Batch (None, 1, 1, 256)         1024      
_________________________________________________________________
flatten (Flatten)            (None, 256)               0         
_________________________________________________________________
dense (Dense)                (None, 4096)              1052672   
_________________________________________________________________
activation_5 (Activation)    (None, 4096)              0         
_________________________________________________________________
dropout (Dropout)            (None, 4096)              0         
_________________________________________________________________
batch_normalization_5 (Batch (None, 4096)              16384     
_________________________________________________________________
dense_1 (Dense)              (None, 4096)              16781312  
_________________________________________________________________
activation_6 (Activation)    (None, 4096)              0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 4096)              0         
_________________________________________________________________
batch_normalization_6 (Batch (None, 4096)              16384     
_________________________________________________________________
dense_2 (Dense)              (None, 1000)              4097000   
_________________________________________________________________
activation_7 (Activation)    (None, 1000)              0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 1000)              0         
_________________________________________________________________
batch_normalization_7 (Batch (None, 1000)              4000      
_________________________________________________________________
dense_3 (Dense)              (None, 17)                17017     
_________________________________________________________________
activation_8 (Activation)    (None, 17)                0         
=================================================================
Total params: 28,096,769
Trainable params: 28,075,633
Non-trainable params: 21,136
_________________________________________________________________
Train on 1088 samples, validate on 272 samples
1088/1088 [==============================] - ETA: 0s - loss: 2.9011 - acc: 0.2436WARNING:tensorflow:From C:\Users\DELL\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
1088/1088 [==============================] - 123s 113ms/sample - loss: 2.9011 - acc: 0.2436 - val_loss: 12.1804 - val_acc: 0.0809

<tensorflow.python.keras.callbacks.History at 0x1b979a6b1c0>