Welcome to the TensorFlow Image Recognition guide! This tutorial will walk you through building a basic image classification model using TensorFlow. 🚀

Getting Started

  1. Install TensorFlow
    Start by installing TensorFlow via pip: 

    pip install tensorflow
    TensorFlow Logo

  2. Load Dataset
    Use the CIFAR-10 dataset for training: 

    from tensorflow.keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
    CIFAR-10 Dataset

  3. Build Model
    Create a simple CNN architecture: 

    model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
    Convolutional Neural Network

Training & Evaluation

  • Compile the model: 
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  • Train the model: 
    model.fit(x_train, y_train, epochs=10)
  • Evaluate accuracy: 
    test_loss = model.evaluate(x_test, y_test)
    Image Classification Accuracy

Expand Your Knowledge

Check out our TensorFlow Core Concepts tutorial to deepen your understanding of neural networks! 💡

Note: All images are placeholders and should be replaced with actual content in a real implementation.