4_2 cnn architecture.py

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, Convolution2D
from keras.layers import Dropout
from keras.layers.normalization import BatchNormalization
from keras import optimizers
import warnings
warnings.filterwarnings("ignore")

output_dim=10
batch_size = 128 
nb_epoch = 50
input_shape = (64, 64, 3)
#Initialize model
model = Sequential()

#CNN Layer 1
#input: 28X28 images with 1 channel -> (28X28X1) tensors.
#this applies 64 convolution filters of size 3X3 each.
model.add(ZeroPadding2D((1,1), input_shape=input_shape))
model.add(Convolution2D(filters=64, kernel_size=(3,3), activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.4))

#CNN Layer 2
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(filters=64, kernel_size=(3,3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Dropout(0.4))

#Flatten the output before feeding these to the fully connected Neurons                                    
model.add(Flatten())

#Fully connected Layers
model.add(Dense(256, activation='relu', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Dense(128, activation='relu', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid', kernel_initializer='he_normal'))

#Using adam as our optimzer
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

#Get a summary of the model.
print(model.summary())

#Compile the model
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])