/HackRPI-TensorFlow-Template

Project Template: Getting Started with TensorFlow

Tools and Technologies:

  • Primary Framework: TensorFlow
  • Additional Libraries: None required for the basic example.

Setup and Installation:

  1. Install TensorFlow with CUDA using pip:

    pip install tensorflow[and-cuda]

  2. Verify the installation by running the following Python commands:

    import tensorflow as tf
print("TensorFlow version:", tf.__version__)

Basic TensorFlow Operations:

  1. Perform a simple addition operation using TensorFlow:

    import tensorflow as tf

# Define constants
const1 = tf.constant([[1,2,3], [1,2,3]])
const2 = tf.constant([[3,4,5], [3,4,5]])

# Perform addition
result = tf.add(const1, const2)

# In TensorFlow 2.x, you can directly obtain the numerical values using the numpy() method
print(result.numpy())

Building a Simple Machine Learning Model:

  1. Load a dataset, build, and train a machine learning model using TensorFlow:

    import tensorflow as tf

# Load the MNIST dataset
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Build a tf.keras.Sequential model
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

Running the Model:

  1. Compile and train the model:

    model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Train the model
model.fit(x_train, y_train, epochs=5)

# Evaluate the model
model.evaluate(x_test,  y_test, verbose=2)

Template Author:

References: