# Implementing Linear Regression in TensorFlow

TensorFlow is a powerful library for machine learning that allows for the easy implementation of various algorithms, including linear regression. In this tutorial, we will be using TensorFlow tape gradient to implement a linear regression model and plot the loss graph and x and y on matplotlib.

First, we will start by importing the necessary libraries:

```
import tensorflow as tf
import matplotlib.pyplot as plt
```

Next, we will generate some fake data for our linear regression model. We will use the

function to generate random x and y values, and we will use a slope of 2 and an intercept of 1 to generate our y values.**tf.random.normal()**

```
# Generate fake data
num_samples = 1000
x = tf.random.normal([num_samples])
y = 2 * x + 1 + tf.random.normal([num_samples])
```

Now, we will define our model using the

function and add a single layer with one input and one output.**tf.keras.Sequential()**

```
# Define model
model = tf.keras.Sequential([
tf.keras.layers.Dense(1, input_shape=(1,))
])
```

Next, we will define our loss function and optimizer. We will use the mean squared error loss function and the Adam optimizer.

```
# Define loss and optimizer
loss_fn = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.Adam()
```

Now, we will use the TensorFlow tape gradient method to train our model. We will loop through the data for a specified number of iterations and update the model’s weights using the optimizer.

```
# Train model
num_epochs = 1000
loss_values = []
for epoch in range(num_epochs):
with tf.GradientTape() as tape:
y_pred = model(x)
loss = loss_fn(y, y_pred)
loss_values.append(loss.numpy())
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
```

Finally, we will plot the loss graph and x and y on matplotlib.

```
# Plot loss graph
plt.plot(loss)
plt.title('Loss vs. Epoch')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()
```

```
# Plot x and y
plt.scatter(x, y)
plt.plot(x, model(x), 'r')
plt.title('Linear Regression Model')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
```

And that’s it! We have successfully implemented a linear regression model using TensorFlow tape gradient and plotted the loss graph and x and y on matplotlib. You can experiment with different values for the number of samples, slope, and intercept to see how it affects the model’s performance. Happy coding!

Hi Nikhil, Thanks for your dedications and explored very well. it is great helps to me. by Palani

ImportError: cannot import name 'load_data' from 'data' (C:\Users\Aravinda\anaconda3\lib\site-packages\data\__init__.py)

sir can you share a code of using squeeze and excitation network on custom CNN for a classification

Hey Nikhil, Very nice, to-the-point article on transfer learning. For a small size of dataset, an image augmentation along with…

Truly good blog short article and also valuable.