TensorFlow v1 in Colab

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. TensorFlow is widely used in the machine learning community for various applications such as classification, regression, and unsupervised learning. In this tutorial, we will look at how to use TensorFlow v1 in Colab.

Setting up TensorFlow v1 in Colab

By default, Colab comes with TensorFlow v2 pre-installed. However, we can still use TensorFlow v1 in Colab by installing it manually. To install TensorFlow v1, we can use the following command:

!pip install tensorflow==1.15

This command installs TensorFlow v1.15.0. We can also install other versions of TensorFlow v1 by changing the version number accordingly.

Using TensorFlow v1 in Colab

Once TensorFlow v1 is installed, we can import it in our Colab notebook as follows:

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

The first line imports TensorFlow v1, while the second line disables the TensorFlow v2 behavior. This allows us to use TensorFlow v1 syntax in our notebook.

Example: Linear regression with TensorFlow v1

As an example, let's look at how to implement linear regression using TensorFlow v1 in Colab. Suppose we have a dataset of height and weight measurements of individuals, and we want to predict the weight of a person given their height.

First, let's generate some sample data:

import numpy as np

np.random.seed(0)

heights = np.random.normal(loc=170, scale=5, size=100)
weights = 2 * heights - 120 + np.random.normal(loc=0, scale=10, size=100)

Here, we generate 100 random height measurements with mean 170cm and standard deviation 5cm. We then use the equation weight = 2 * height - 120 + noise to generate corresponding weight measurements, where the noise has mean 0 and standard deviation 10.

Next, let's define the TensorFlow model:

x = tf.placeholder(tf.float32, name="x")
y = tf.placeholder(tf.float32, name="y")

w = tf.Variable(0.0, name="w")
b = tf.Variable(0.0, name="b")

y_pred = w * x + b

Here, we define two placeholders for the input data x and y, and two variables w and b for the model parameters. We also define the model prediction y_pred as a linear function of x.

We can now define the loss function and optimizer:

loss = tf.reduce_sum(tf.square(y_pred - y))
optimizer = tf.train.GradientDescentOptimizer(0.0001).minimize(loss)

Here, we use the mean squared error as the loss function, and the gradient descent optimizer with learning rate 0.0001.

Finally, we can train the model:

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    for i in range(1000):
        sess.run(optimizer, feed_dict={x: heights, y: weights})

    w_val, b_val = sess.run([w, b])

print("w =", w_val)
print("b =", b_val)

Here, we initialize the variables, and then run the optimizer for 1000 iterations. We also feed in the height and weight data using the feed_dict argument. After training, we print the learned parameter values.

Conclusion

In this tutorial, we have seen how to use TensorFlow v1 in Colab. We have also implemented a simple linear regression model using TensorFlow v1. TensorFlow v1 is a powerful library for machine learning and can be used for various applications.