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Machine Learning

Posted by Simran Bansari
Simran Bansari
4,517 Points

Google colab, CNN classifier

def neural_model(): model = Sequential() model.add(Conv2D(60, (5, 5), input_shape = (32, 32, 1), activation = 'relu')) model.add(Conv2D(60, (5, 5), input_shape = (32, 32, 1), activation = 'relu')) model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Conv2D(30, (3, 3), activation = 'relu'))
model.add(Conv2D(30, (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

#model.add(Dropout(0.5))

model.add(Flatten())
model.add(Dense(500, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))
model.compile(Adam(lr = 0.001), loss = 'categorical_crossentropy', metrics = ['accuracy'])
return model

what do these parts/layers specifically mean and what are they meant to do in the neural network?