Theano vs TensorFlow: A Quick Comparision of Frameworks
The era of Deep Learning and Machine Learning is at its peak. It is going to create 2.3 Million Jobs by 2020. With new frameworks coming up every month, TensorFlow and Theano have been there for a while and have gained a good amount of popularity as well. So in this article, I’ll be discussing the following topics:
- What is Theano?
- What is TensorFlow?
- Theano vs Tensorflow
- Popularity
- Execution Speed
- Technology Benefits
- Compatibility
- Community Support
- Code Readability
- Final Verdict: Theano vs TensorFlow
What is Theano?
Theano can be defined as a library for Scientific Computing. It was developed by the Université de Montréal and has been available since 2007.
It allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can run on both CPU and GPU.
What is TensorFlow?
TensorFlow is an open-source software library by Google Brain for dataflow programming across a range of tasks.
It is a symbolic math library that is used for machine learning applications like neural networks.
Theano vs TensorFlow
We will compare Theano vs TensorFlow based on the following Metrics:
Popularity:
Execution Speed:
Technology Benefits:
Compatibility:
Community Support:
Code Readability:
Let us Compare Theano vs TensorFlow based on their Code. Here I’m Taking a Basic Example Script where we will take some Phony data and initialize the best fit for that data so it can predict future data points.
Theano Code:
import theano
import theano.tensor as T
import numpy
# Again, make 100 points in numpy
x_data = numpy.float32(numpy.random.rand(2, 100))
y_data = numpy.dot([0.100, 0.200], x_data) + 0.3
# Intialise the Theano model
X = T.matrix()
Y = T.vector()
b = theano.shared(numpy.random.uniform(-1, 1), name="b")
W = theano.shared(numpy.random.uniform(-1.0, 1.0, (1, 2)), name="W")
y = W.dot(X) + b
# Compute the gradients WRT the mean-squared-error for each parameter
cost = T.mean(T.sqr(y - Y))
gradientW = T.grad(cost=cost, wrt=W)
gradientB = T.grad(cost=cost, wrt=b)
updates = [[W, W - gradientW * 0.5], [b, b - gradientB * 0.5]]
train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True)
for i in xrange(0, 201):
train(x_data, y_data)
print W.get_value(), b.get_value()Equivalent TensorFlow Code:
import tensorflow as tf
import numpy as np
# Make 100 phony data points in NumPy.
x_data = np.float32(np.random.rand(2, 100)) # Random input
y_data = np.dot([0.100, 0.200], x_data) + 0.300
# Construct a linear model.
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
y = tf.matmul(W, x_data) + b
# Minimize the squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# For initializing the variables.
init = tf.initialize_all_variables()
# Launch the graph
sess = tf.Session()
sess.run(init)
# Fit the plane.
for step in xrange(0, 201):
sess.run(train)
if step % 20 == 0:
print step, sess.run(W), sess.run(b)
# Learns best fit is W: [[0.100 0.200]], b: [0.300]Length Wise Both the Code are almost Similar there’s not much difference. Two identically-generated NumPy arrays describing the input, and the target output. But if we have a look at the Model Initialization.
Model Initialization:
# TensorFlow
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
y = tf.matmul(W, x_data) + b
# Theano
X = T.matrix()
Y = T.vector()
b = theano.shared(numpy.random.uniform(-1, 1), name="b")
W = theano.shared(numpy.random.uniform(-1.0, 1.0, (1, 2)), name="W")
y = W.dot(X) + bAs you can see here that TensorFlow doesn’t require any Special Treatment of X and Y Variables. On the other hand, Theano requires an extra effort to make sure that the variables are Symbolic Inputs to the Function. The definition of b and W are explanatory and also nicer.
The Learning: Optimization
# Tensorflow
loss = tf.reduce_mean(tf.square(y - y_data)) # (1)
optimizer = tf.train.GradientDescentOptimizer(0.5) # (2)
train = optimizer.minimize(loss) # (3)
# Theano
cost = T.mean(T.sqr(y - Y)) # (1)
gradientW = T.grad(cost=cost, wrt=W) # (2)
gradientB = T.grad(cost=cost, wrt=b) # (2)
updates = [[W, W - gradientW * 0.5], [b, b - gradientB * 0.5]] # (2)
train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True) # (3)For (1) the MSE is almost the same for Theano vs TensorFlow.
For (2) Defining the Optimizer is easy and simple as it gets in case of TensorFlow, but Theanno gives you a great deal of Control overt the Optimizers although its quite lengthy and increases Verification Effort.
For (3) Training Function the Code is almost Similar
Training Body:
# TensorFlow
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for step in xrange(0, 201):
sess.run(train)
# Theano
for i in xrange(0, 201):
train(x_data, y_data)
print W.get_value(), b.get_value()The code for Training is almost Identical, but Encapsulating the Graph Execution in Session Object is Conceptually Cleaner than Theano.
Final Verdict: Theano vs TensorFlow
On a Concluding Note, it can be said that both APIs have a similar Interface. But TensorFlow is comparatively easier yo use as it provides a lot of Monitoring and Debugging Tools. Theano takes the Lead in Usability and Speed, but TensorFlow is better suited for Deployment. Paperwork or Documentation for Theano is more than TensorFlow and TensorFlow being a new Language people don’t have many resources, to begin with. Open-source deep-libraries such as Keras, Lasagne, and blocks have been built on top of Theano.
I hope this comparison was enough for you to decide which framework to opt for. If you wish to check out more articles on the market’s most trending technologies like Artificial Intelligence, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series which will explain the various other aspects of Deep Learning.
3. Perceptron learning Algorithm
6. Convolutional Neural Networks
10. Restricted Boltzmann Machine Tutorial
13. Artificial Intelligence Tutorial
14. TensorFlow Image Classification
15. Artificial Intelligence Applications
16. How to Become an Artificial Intelligence Engineer?
17. Q Learning
20. Artificial Intelligence Algorithms
21. Best Laptops for Machine Learning
22. Top 12 Artificial Intelligence Tools
23. Artificial Intelligence (AI) Interview Questions
Originally published at https://www.edureka.co on July 24, 2019.
