Earlier we used h5 format to save our model when creating the checkpoint. The HDF5 format contains the model's architecture, weights values, and compile() information. The saved model can be loaded and used for prediction with keras.models.load_model(path/to/saved_model) method.
To evaluate the model and make prediction on test data, we'll need to create the same preprocessing steps for the image as we have done with train and validation data:
# Create image generator for test data
test_gen = ImageDataGenerator(preprocessing_function=preprocess_input)
# Path of test images directory
test_imgs_dir = '../input/mlzoomcampimageclassification/zoomcamp-image-classification/clothing-dataset-small/test'
# Load in test images to generator
test_ds = test_gen.flow_from_directory(directory=test_imgs_dir,
target_size=(299,299),
batch_size=32,
shuffle=False)
# Path of an image to make predictions
img_path = 'path/to/image'
# Load image
img = load_img(img_path, target_size=(299,299))
# Convert image to numpy array
x = np.array(img)
# Add batch dimension to the image
X = np.array([x])
# Preprocess the image
X = preprocess_input(X)The model performance can be evaluated on test data with model.evaluate(test_ds) and the prediction on the test image can be made using the method model.predict(X). We can then zip the class names and prediction to see the likelihood.
Classes, functions, attributes:
keras.models.load_model(): method to load saved modelmodel.evaluate(): method to evaluate the performance of the model based on the evaluation metricsmodel.predict(): method to make predictions of output depending on the input
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