test_datagen = ImageDataGenerator(rescale=1./255) train = train_datagen.flow_from_directory( 'Dataset Path', # Directory Name target_size=(256, 256), # Size of the image expected in our mode batch_size=32, # Size of the batch after which batch will be updated class_mode='binary') # Show the class mode of the result test = test_datagen.flow_from_directory( 'Dataset Path', # Directory Name target_size=(256, 256), # Image of our test set batch_size=32, # Size of the batch after which batch will be updated class_mode='binary') # Show the class mode of the result model.fit_generator( train, steps_per_epoch=2000, # Number of images in our train dataset epochs=25, # Number of epoch we need validation_data= test, # Test set validation_steps=800) # Number of images in our test dataset """ To improve the model you can Add a convolution Layer Increase the target size in train and test dataset so that more information of the pixel pattern is captured """
classifier.add(Dense(output_dim=1, activation='sigmoid')) #compliling classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) from keras.preprosessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1. / 255) training_set = train_datagen.flow_from_directory('dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary') test_set = test_datagen.flow_from_directory('data/validation', target_size=(64, 64), batch_size=32, class_mode='binary') classifier.fit_generator(training_set, steps_per_epoch=8000, epochs=25, validation_data=test_set, validation_steps=2000)