ASMR Programming: Building a Basic Convolutional Neural Network using TensorFlow
Today, we will be diving into the world of deep learning by building a basic Convolutional Neural Network (CNN) using TensorFlow. And the best part? We’ll be doing it all without any talking – just relaxing sounds and soothing visuals to help you focus and unwind as we code.
What is a Convolutional Neural Network?
A Convolutional Neural Network is a type of deep neural network that is commonly used in image recognition and computer vision tasks. It is composed of multiple layers of neurons, each with their own learnable weights and biases. The convolutional layers in a CNN apply filters to the input image in order to detect features such as edges, corners, and textures.
Building the CNN using TensorFlow
Now, let’s get started on building our basic CNN using TensorFlow. We will first define the architecture of our network, including the convolutional layers, pooling layers, and fully connected layers. Then, we will train the model on a dataset of images and evaluate its performance.
# Import TensorFlow and other necessary libraries
import tensorflow as tf
from tensorflow.keras import layers
# Define the CNN architecture
model = tf.keras.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(train_images, train_labels, epochs=10)
# Evaluate the model
test_loss, test_accuracy = model.evaluate(test_images, test_labels)
And there you have it – a basic Convolutional Neural Network built using TensorFlow. We hope you enjoyed this ASMR programming session and found it relaxing and informative. Stay tuned for more coding sessions with soothing sounds and visuals!
Good luck 👍