How to predict input image using trained model in Keras?

Question

I'm only beginning with keras and machine learning in general.

I trained a model to classify images from 2 classes and saved it using model.save(). Here is the code I used:

from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D from keras.layers import Activation, Dropout, Flatten, Dense from keras import backend as K   # dimensions of our images. img_width, img_height = 320, 240  train_data_dir = 'data/train' validation_data_dir = 'data/validation' nb_train_samples = 200  #total nb_validation_samples = 10  # total epochs = 6 batch_size = 10  if K.image_data_format() == 'channels_first':     input_shape = (3, img_width, img_height) else:     input_shape = (img_width, img_height, 3)  model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid'))  model.compile(loss='binary_crossentropy',               optimizer='rmsprop',               metrics=['accuracy'])  # this is the augmentation configuration we will use for training train_datagen = ImageDataGenerator(     rescale=1. / 255,     shear_range=0.2,     zoom_range=0.2,     horizontal_flip=True)  # this is the augmentation configuration we will use for testing: # only rescaling test_datagen = ImageDataGenerator(rescale=1. / 255)  train_generator = train_datagen.flow_from_directory(     train_data_dir,     target_size=(img_width, img_height),     batch_size=batch_size,     class_mode='binary')  validation_generator = test_datagen.flow_from_directory(     validation_data_dir,     target_size=(img_width, img_height),     batch_size=batch_size,     class_mode='binary')  model.fit_generator(     train_generator,     steps_per_epoch=nb_train_samples // batch_size,     epochs=epochs,     validation_data=validation_generator,     validation_steps=5)  model.save('model.h5') 

It successfully trained with 0.98 accuracy which is pretty good. To load and test this model on new images, I used the below code:

from keras.models import load_model import cv2 import numpy as np  model = load_model('model.h5')  model.compile(loss='binary_crossentropy',               optimizer='rmsprop',               metrics=['accuracy'])  img = cv2.imread('test.jpg') img = cv2.resize(img,(320,240)) img = np.reshape(img,[1,320,240,3])  classes = model.predict_classes(img)  print classes 

It outputs:

[[0]]

Why wouldn't it give out the actual name of the class and why [[0]]?

Thanks in advance.

Answer 1

If someone is still struggling to make predictions on images, here is the optimized code to load the saved model and make predictions:

# Modify 'test1.jpg' and 'test2.jpg' to the images you want to predict on  from keras.models import load_model from keras.preprocessing import image import numpy as np  # dimensions of our images img_width, img_height = 320, 240  # load the model we saved model = load_model('model.h5') model.compile(loss='binary_crossentropy',               optimizer='rmsprop',               metrics=['accuracy'])  # predicting images img = image.load_img('test1.jpg', target_size=(img_width, img_height)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0)  images = np.vstack([x]) classes = model.predict_classes(images, batch_size=10) print classes  # predicting multiple images at once img = image.load_img('test2.jpg', target_size=(img_width, img_height)) y = image.img_to_array(img) y = np.expand_dims(y, axis=0)  # pass the list of multiple images np.vstack() images = np.vstack([x, y]) classes = model.predict_classes(images, batch_size=10)  # print the classes, the images belong to print classes print classes[0] print classes[0][0]