Preparing your input image
The original ResNet50 model was trained with images of size 224 x 224 pixels and a number of preprocessing operations; like the subtraction of the mean pixel value in the training set for all training images. You need to pre-process the images you want to predict on in the same way.
When predicting on a single image you need it to fit the model's input shape, which in this case looks like this:
(batch-size, width, height, channels),np.expand_dims with parameter axis = 0 adds the batch-size dimension, representing that a single image will be passed to predict. This batch-size dimension value is 1, since we are only predicting on one image.
You will go over these preprocessing steps as you prepare this dog's (named Ivy) image into one that can be classified by ResNet50.
This is a part of the course
“Introduction to Deep Learning with Keras”
Exercise instructions
- Import
imagefromtensorflow.keras.preprocessingandpreprocess_inputfromtensorflow.keras.applications.resnet50. - Load the image with the right
target_sizefor your model. - Turn it into an array with
image.img_to_array(). - Pre-process
img_expandedthe same way the original ResNet50 training images were processed withpreprocess_input().
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import image and preprocess_input
from tensorflow.keras.____ import ____
from tensorflow.keras.____.____ import ____
# Load the image with the right target size for your model
img = image.load_img(img_path, target_size=(____, ____))
# Turn it into an array
img_array = image.____(____)
# Expand the dimensions of the image, this is so that it fits the expected model input format
img_expanded = np.expand_dims(img_array, axis = 0)
# Pre-process the img in the same way original images were
img_ready = ____(____)This exercise is part of the course
Introduction to Deep Learning with Keras
Learn to start developing deep learning models with Keras.
It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.
Exercise 1: Tensors, layers, and autoencodersExercise 2: It's a flow of tensorsExercise 3: Neural separationExercise 4: Building an autoencoderExercise 5: De-noising like an autoencoderExercise 6: Intro to CNNsExercise 7: Building a CNN modelExercise 8: Looking at convolutionsExercise 9: Preparing your input imageExercise 10: Using a real world modelExercise 11: Intro to LSTMsExercise 12: Text prediction with LSTMsExercise 13: Build your LSTM modelExercise 14: Decode your predictionsExercise 15: Test your model!Exercise 16: You're done!What is DataCamp?
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