CNN(Convolutional Neural Network) - Machine Learning - part 2

We have understood basics of CNN in detail in my previous post post now this is time to write our own code and start create our own CNN model. Please have a look on below images to understand high level CNN architecture.

We shown in picture we need multiple layers in CNN so we will create multiple layers for different purpose. So let's start writing code -
First layer - Convolution layer
Second layer - pooling layer (For minimize the size)
Third layer - Flatten layer (To convert data from 2D to 1D)
Fourth layer - Dense layer (For neural network)
from keras.layers import Convolution2D

from keras.layers import MaxPooling2D

from keras.layers import Flatten

from keras.layers import Dense
model = Sequential()

model.add(Convolution2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(64,64,3) ) )

model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       
=================================================================
Total params: 896
Trainable params: 896
Non-trainable params: 0

model.add(MaxPooling2D(pool_size=(2, 2)))
model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         
=================================================================
Total params: 896
Trainable params: 896
Non-trainable params: 0

model.add(Flatten())
model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 30752)             0         
=================================================================
Total params: 896
Trainable params: 896
Non-trainable params: 0

model.add(Dense(units=128, activation='relu'))
model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 30752)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               3936384   
=================================================================
Total params: 3,937,280
Trainable params: 3,937,280
Non-trainable params: 0

model.add(Dense(units=1,activation='sigmoid'))
model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 30752)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               3936384   
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 129       
=================================================================
Total params: 3,937,409
Trainable params: 3,937,409
Non-trainable params: 0

So finally we have created convolutional layer and fully connected model. here initial 3 layers are convolution and next two are for fully connected. Complete layers or network is known as ConNet(Convolution Network).
Convolution -

_________________________________________________________________

Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 30752)             0         
_________________________________________________________________

Fully connected -
_________________________________________________________________

Layer (type)                 Output Shape              Param #   
=================================================================

dense_1 (Dense)              (None, 128)               3936384   
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 129       

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Soon I'll add remaining code......cont......