InceptionNet is a popular convolutional neural network (CNN) architecture that has been widely used for image classification tasks. It is known for its deep architecture and the use of inception modules that help capture features at multiple scales.
In this blog post, we will explore how to use TensorFlow to implement the InceptionNet architecture in Python.
Installing TensorFlow
Before we begin, make sure you have TensorFlow installed on your machine. If you don’t have it installed, you can install it using pip with the following command:
pip install tensorflow
Importing TensorFlow and Required Libraries
To start with, let’s import the required libraries and TensorFlow itself.
import tensorflow as tf
from tensorflow.keras import layers
Building the InceptionNet Model
Now, let’s define the InceptionNet model using the TensorFlow’s Sequential API. We will create various inception modules and stack them together.
def inception_module(filters_1x1, filters_3x3_reduce, filters_3x3, filters_5x5_reduce, filters_5x5, filters_pool):
conv1x1 = layers.Conv2D(filters_1x1, (1, 1), padding='same', activation='relu')
conv3x3_reduce = layers.Conv2D(filters_3x3_reduce, (1, 1), padding='same', activation='relu')
conv3x3 = layers.Conv2D(filters_3x3, (3, 3), padding='same', activation='relu')
conv5x5_reduce = layers.Conv2D(filters_5x5_reduce, (1, 1), padding='same', activation='relu')
conv5x5 = layers.Conv2D(filters_5x5, (5, 5), padding='same', activation='relu')
pool_proj = layers.Conv2D(filters_pool, (1, 1), padding='same', activation='relu')
return tf.keras.Sequential([conv1x1, conv3x3_reduce, conv3x3, conv5x5_reduce, conv5x5, pool_proj])
def InceptionNet():
input_shape = (224, 224, 3)
inputs = layers.Input(shape=input_shape)
net = layers.Conv2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu')(inputs)
net = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(net)
net = layers.Conv2D(64, (1, 1), padding='same', activation='relu')(net)
net = layers.Conv2D(192, (3, 3), padding='same', activation='relu')(net)
net = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(net)
net = inception_module(64, 96, 128, 16, 32, 32)(net)
net = inception_module(128, 128, 192, 32, 96, 64)(net)
net = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(net)
net = inception_module(192, 96, 208, 16, 48, 64)(net)
net = inception_module(160, 112, 224, 24, 64, 64)(net)
net = inception_module(128, 128, 256, 24, 64, 64)(net)
net = inception_module(112, 144, 288, 32, 64, 64)(net)
net = inception_module(256, 160, 320, 32, 128, 128)(net)
net = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(net)
net = inception_module(256, 160, 320, 32, 128, 128)(net)
net = inception_module(384, 192, 384, 48, 128, 128)(net)
net = layers.AvgPool2D((7, 7), strides=(7, 7), padding='same')(net)
net = layers.Flatten()(net)
net = layers.Dropout(0.4)(net)
outputs = layers.Dense(1000, activation='softmax')(net)
model = tf.keras.Model(inputs, outputs)
return model
Training the InceptionNet Model
To train the InceptionNet model, we need a dataset of images and corresponding labels. We won’t go into the details of dataset preparation in this blog post, so let’s assume we already have a preprocessed dataset.
# Load dataset and preprocess
# Split the dataset into train and test
# Instantiate the model
model = InceptionNet()
# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test))
Conclusion
In this blog post, we have learned how to implement the InceptionNet architecture using TensorFlow in Python. The InceptionNet model is known for its deep architecture and use of inception modules that capture features at multiple scales. By training the model on a dataset, we can use it for image classification tasks.
Feel free to explore further with the InceptionNet model and see how it performs on your own image classification problems. Happy coding!