Creating a Simple Linear Regression Model with TensorFlow

Linear regression is a machine learning algorithm that models the linear relationship (a relationship that can be represented by a straight line) between an input and an output.

For example, consider the following relationship:

• When x is 1, y is 2

• When x is 2, y is 4

• When x is 3, y is 6

This relationship can be expressed as a linear equation like y = 2x.

The code below is an example of creating a simple machine learning model that helps a computer learn the pattern of numbers.

To break it down simply, the computer learns to output twice the number it is given.

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import tensorflow as tf
import numpy as np

# Define input data
x_train = np.array([1, 2, 3, 4, 5], dtype=np.float32)
# Define output data
y_train = np.array([2, 4, 6, 8, 10], dtype=np.float32)

# Create model
model = tf.keras.Sequential([
    # Create a Dense layer with 1 input value and 1 output value
    tf.keras.layers.Dense(units=1, input_shape=[1])
])

# Compile model
model.compile(optimizer='sgd', loss='mean_squared_error')

# Train the model by repeating the data 100 times to learn the rule
model.fit(x_train, y_train, epochs=100, verbose=1)

# Make a prediction
print(model.predict([6]))  # Expected output: A value close to 12

Let's go over each part of the code.

x_train is the input data (1, 2, 3, 4, 5), and y_train is the output data (2, 4, 6, 8, 10).

In other words, the output is twice the input value.

Use tf.keras.Sequential() to create a neural network model.

Dense(units=1, input_shape=[1]) means a layer with one input and one output.

optimizer='sgd' indicates using Stochastic Gradient Descent (SGD) for training. SGD helps minimize the loss function.

loss='mean_squared_error' is a method for reducing error.

model.fit(x_train, y_train, epochs=100, verbose=1) trains the model by repeating the data 100 times to learn the rule.

When you execute model.predict([6]), it predicts a value close to twice 6, which is 12.

We've now created a simple linear regression model.

In the next class, we'll go through a simple quiz to review what we've learned about TensorFlow.