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keras_cnn.py
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keras_cnn.py
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import numpy as np
import pickle
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, BatchNormalization, Conv2D, MaxPool2D, Flatten
import sys
import os
def main():
train_sizes = np.arange(50e3, 250e3, 25e3, dtype=int)
cv_sizes = np.arange(25e3, 125e3, 25e3, dtype=int)
train_size=int(sys.argv[1])
cv_size=int(sys.argv[2])
use_full=bool(int(sys.argv[3]))
if not use_full and (train_size not in train_sizes or cv_size not in cv_sizes):
print('Invalid size fed')
exit(1)
batch_size=32
epochs=150
if use_full:
path='train_data/'
else:
path='train_data_reduced/'
#path_binary='/data/My Drive/Colab Notebooks/image forgery detection/k64 binary 25percent stride8/train_data/'
#x_train_grayscale=np.load(path_grayscale+'x_train.npy')
#x_cv_grayscale=np.load(path_grayscale+'x_cv.npy')
#y_train_grayscale=np.load(path_grayscale+'y_train.npy')
#y_cv_grayscale=np.load(path_grayscale+'y_cv.npy')
if use_full:
x_train=np.load(path+'x_train.npy')
x_cv=np.load(path+'x_cv.npy')
y_train=np.load(path+'y_train.npy')
y_cv=np.load(path+'y_cv.npy')
else:
x_train = np.load(path + 'x_train_'+str(train_size)+'.npy')
x_cv = np.load(path + 'x_cv_'+str(cv_size)+'.npy')
y_train = np.load(path + 'y_train_'+str(train_size)+'.npy')
y_cv = np.load(path + 'y_cv_'+str(cv_size)+'.npy')
# Normalise
x_train = x_train/255
x_cv = x_cv/255
cnn_model=Sequential()
cnn_model.add(Conv2D(input_shape=(64, 64, 3), filters=20, kernel_size=4, strides=2, padding='valid',
activation='relu', data_format='channels_last'))
cnn_model.add(Conv2D(filters=15, kernel_size=3, strides=1, padding='valid', activation='relu',
data_format='channels_last'))
cnn_model.add(MaxPool2D(pool_size=3, data_format='channels_last'))
cnn_model.add(Conv2D(filters=20, kernel_size=4, strides=2, padding='valid', activation='relu',
data_format='channels_last'))
cnn_model.add(MaxPool2D(pool_size=2, data_format='channels_last'))
# cnn_model.add(Conv2D(filters=15, kernel_size=2, strides=1, padding='valid', activation='relu',
# data_format='channels_last'))
# cnn_model.add(Conv2D(filters=16, kernel_size=3, strides=1, padding='valid', activation='relu',
# data_format='channels_last'))
# cnn_model.add(Conv2D(filters=16, kernel_size=3, strides=1, padding='valid', activation='relu',
# kernel_initializer='he_normal', data_format='channels_last'))
cnn_model.add(Flatten())
cnn_model.add(Dropout(0.2))
cnn_model.add(Dense(1, activation='sigmoid'))
cnn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
cnn_model.summary()
history = cnn_model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1,
validation_data=(x_cv, y_cv))
cnn_model.save('keras_cnn_model_redone.h5')
if __name__=='__main__':
main()