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====== Tensorflow examples ======
 

**1. Hello, TensorFlow!**

This is a simple example to ensure that TensorFlow is installed correctly. It prints "Hello, TensorFlow!" to the console.


import tensorflow as tf

# Create a TensorFlow constant
hello = tf.constant('Hello, TensorFlow!')

# Start a TensorFlow session
with tf.Session() as session:
    # Run the session and print the output
    print(session.run(hello))

**2. Linear Regression:**

Linear regression is a fundamental machine learning task. Here's a simple example using TensorFlow:


import tensorflow as tf
import numpy as np

# Generate random data
x = np.random.rand(100).astype(np.float32)
y = 2 * x + 1

# Create TensorFlow variables for the model parameters
W = tf.Variable(tf.random.normal([1]))
b = tf.Variable(tf.zeros([1]))

# Define the linear regression model
y_pred = W * x + b

# Define the loss function (Mean Squared Error)
loss = tf.reduce_mean(tf.square(y_pred - y))

# Create an optimizer (e.g., Gradient Descent)
optimizer = tf.train.GradientDescentOptimizer(0.1)
train_op = optimizer.minimize(loss)

# Initialize the variables
init = tf.global_variables_initializer()

# Create a TensorFlow session and train the model
with tf.Session() as session:
    session.run(init)
    for step in range(1000):
        session.run(train_op)
    # Print the learned parameters
    learned_W, learned_b = session.run([W, b])
    print(f'Learned W: {learned_W[0]}, Learned b: {learned_b[0]}')


**3. Image Classification with Convolutional Neural Network (CNN):**

This example demonstrates image classification using a CNN with TensorFlow and the MNIST dataset.


import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

# Load the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

# Preprocess the data
train_images, test_images = train_images / 255.0, test_images / 255.0

# Build a simple CNN model
model = models.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)
])

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

# Train the model
model.fit(train_images, train_labels, epochs=5)

# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test accuracy: {test_acc}")

# Make predictions
predictions = model.predict(test_images)


These examples cover a range of TensorFlow use cases, from simple "Hello, TensorFlow!" to more complex tasks like linear regression and image classification with convolutional neural networks. TensorFlow is a powerful library with extensive documentation and resources for further exploration and learning.