Excuse me sir i am bit new to programming, so kindly be clear in wording.
# CNN import keras from keras.preprocessing.image import ImageDataGenerator from keras_tqdm import TQDMNotebookCallback from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import Flatten from keras.constraints import maxnorm from keras.optimizers import SGD from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils from keras.callbacks import Callback model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(32, (3, 3), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(64, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(2, activation='softmax')) epochs = 50 lrate = 0.01 decay = lrate/epochs sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False) model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])