Welcome to the TensorFlow Image Recognition Tutorial! This guide will walk you through building a basic image classification model using TensorFlow.
🧠 Step-by-Step Guide
Install TensorFlow
Start by installing TensorFlow via pip:pip install tensorflow
Prepare 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()
Build the Model
Create a convolutional neural network (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') ])
Train the Model
Compile and train the model:model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=10)
Evaluate & Deploy
Test the model and deploy it for inference:test_loss = model.evaluate(x_test, y_test) predictions = model.predict(x_test)