text_plain_output_1.png
from keras.layers import Conv2D, MaxPooling2D

from keras.layers import Dense, Dropout, Flatten, Activation

from keras.models import Sequential

img_width, img_height = (204, 204)
batch_size = 64
num_classes = 2
input_shape = (img_width, img_height, 3)
EPOCHS = 10

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy'])

model.summary()