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I am using Convolutional Neural Networking for vehicle identification, my first time. Currently, I am working with just 2 classes(bike and car). Training set: 420 car images and 825 bike images. Test set: 44 car images and 110 bike images Car and Bike images are in different format(bmp,jpg). In single prediction, I am always getting 'bike'. I have tried using the Sigmoid function in the output layer. Then I get only 'car'. My code is like following: ``

from keras.models import Sequential

from keras.layers import Conv2D

from keras.layers import MaxPooling2D

from keras.layers import Flatten

from keras.layers import Dense,Dropout







classifier = Sequential()





classifier.add(Conv2D(32, (3, 3), input_shape = (128, 128, 3), activation = 'relu'))





classifier.add(MaxPooling2D(pool_size = (3, 3)))



# Adding a second convolutional layer

classifier.add(Conv2D(32, (3, 3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size = (3, 3)))



# Step 3 - Flattening

classifier.add(Flatten())



# Step 4 - Full connection

classifier.add(Dropout(0.3))

classifier.add(Dense(units = 128, activation = 'relu'))

classifier.add(Dense(units = 1, activation = 'sigmoid'))



# Compiling the CNN

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])



# Part 2 - Fitting the CNN to the images



from keras.preprocessing.image import ImageDataGenerator



train_datagen = ImageDataGenerator(rescale = 1./255,

                                   shear_range = 0.2,

                                   zoom_range = 0.2,

                                   rotation_range= 3,

                                   fill_mode = 'nearest',

                                   horizontal_flip = True)



test_datagen = ImageDataGenerator(rescale = 1./255,

                                   shear_range = 0.2,

                                   zoom_range = 0.2,

                                   rotation_range= 3,

                                   fill_mode = 'nearest',

                                   horizontal_flip = True)



training_set = train_datagen.flow_from_directory('dataset/training_set',

                                                 target_size = (128, 128),

                                                 batch_size = 10,

                                                 class_mode = 'binary')



test_set = test_datagen.flow_from_directory('dataset/test_set',

                                            target_size = (128, 128),

                                            batch_size = 10,

                                            class_mode = 'binary')



classifier.fit_generator(training_set,

                         steps_per_epoch = 1092//10,

                         epochs = 3,

                         validation_data = test_set,

                         validation_steps = 20)



classifier.save("car_bike.h5")

And I wanted to test a single image like the following:

test_image = image.load_img('dataset/single_prediction/download (3).jpg', target_size = (128, 128))

test_image = image.img_to_array(test_image)

test_image *= (1/255.0)

test_image = np.expand_dims(test_image, axis = 0)

result = classifier.predict(test_image)

if result[0][0] == 1:

    prediction = 'bike'

else:

    prediction = 'car'



print(" {}".format(prediction))

If you print your result matrix you'll see that it doesn't have only 1s and 0s but floats between these numbers. You may pick a threshold and set values that exceed it to 1 and everything else to 0.