TensorFlow: Enhancing Image Classification with Inception
Introduction
This article delves into the process of expanding the capabilities of a pre-trained Inception model in TensorFlow. We’ll demonstrate how to add a new class to the model’s existing classification repertoire and obtain the complete image hierarchy for predictions.
Setting the Stage
1. Importing Libraries
import tensorflow as tf from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input from tensorflow.keras.layers import Dense, GlobalAveragePooling2D from tensorflow.keras.models import Model from tensorflow.keras.preprocessing import image from tensorflow.keras.preprocessing.image import ImageDataGenerator import numpy as np
2. Loading the Pre-trained Inception Model
base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299, 299, 3))
Adding a New Class
1. Freezing Base Layers
for layer in base_model.layers: layer.trainable = False
2. Building the Classifier
x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) x = Dense(512, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x) model = Model(inputs=base_model.input, outputs=predictions)
3. Compiling the Model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
Data Preparation
1. Image Data Generator
train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True ) test_datagen = ImageDataGenerator(rescale=1./255)
2. Loading Training and Validation Data
train_set = train_datagen.flow_from_directory( 'path/to/train', target_size=(299, 299), batch_size=32, class_mode='binary' ) test_set = test_datagen.flow_from_directory( 'path/to/test', target_size=(299, 299), batch_size=32, class_mode='binary' )
Training the Model
model.fit_generator( train_set, epochs=10, validation_data=test_set )
Outputting the Full Image Hierarchy
1. Retrieving Inception’s Output
inception_output = Model(inputs=base_model.input, outputs=base_model.output)
2. Making Predictions
image_path = 'path/to/image.jpg' img = image.load_img(image_path, target_size=(299, 299)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) # Get Inception's output inception_features = inception_output.predict(x) # Get model prediction prediction = model.predict(x)
3. Accessing Inception’s Output Layers
inception_layers = ['mixed_7a', 'mixed_8a', 'mixed_9a', 'mixed_10a'] layer_outputs = {} for layer_name in inception_layers: layer_output = base_model.get_layer(layer_name).output layer_outputs[layer_name] = layer_output inception_hierarchy = Model(inputs=base_model.input, outputs=layer_outputs) # Get layer outputs hierarchy_output = inception_hierarchy.predict(x) # Displaying the Inception Hierarchy Output for layer_name, output in hierarchy_output.items(): print(f"Layer: {layer_name}") print(output[0].shape) # Displaying or processing the output for each layer # ...
Results and Interpretation
The output for each Inception layer provides a hierarchical representation of the image. By analyzing these features, you can gain insights into the image content at different levels of abstraction, enhancing your understanding of the image’s characteristics and facilitating further analysis or downstream tasks.
Conclusion
By leveraging a pre-trained Inception model, we’ve successfully demonstrated how to incorporate new classes and extract the complete image hierarchy. This process empowers us to make accurate predictions while simultaneously gaining deeper understanding of the image’s content, leading to richer analysis and application possibilities.