The code in these files implements a machine learning model using TensorFlow. The first file, tensorflow1.py, is a basic example that creates a simple neural network, which is a type of machine learning algorithm. It takes input from the user and uses that data to make predictions. The second file, tensorflow2.py, is a more advanced example using deep learning. It uses a convolutional neural network to identify objects in an image. Both files illustrate how TensorFlow can be used to create powerful machine learning models.
What is TensorFlow?
TensorFlow is a open-source library developed by Google for dataflow programming within a machine learning system. It is a high-level API (Application Programming Interface) that provides tools for building and training models in the form of neural networks. TensorFlow is based on a computational graph, which allows for an efficient and flexible way to represent mathematical operations and data flow. This makes it suitable for solving a wide range of problems, from image recognition to natural language processing.
TensorFlow is a powerful library that can be used to solve a wide range of problems, from image recognition to natural language processing. It uses a computational graph to represent mathematical operations and data flow. This makes it suitable for solving a wide range of problems, from image recognition to natural language processing.
TensorFlow Basics
TensorFlow is a library developed by Google for dataflow programming within a machine learning system. It is a high-level API that provides tools for building and training models in the form of neural networks. TensorFlow is based on a computational graph, which allows for an efficient and flexible way to represent mathematical operations and data flow. This makes it suitable for solving a wide range of problems, from image recognition to natural language processing.
The installation procedure for TensorFlow depends on the system you are using. For example, on a Mac, you may need to disable “System Integrity Protection” (rootless) temporarily by booting into recovery mode, typing in csrutil disable, and then rebooting. You can check if it is disabled or enabled by typing csrutil status in your console.
Once you have TensorFlow installed, you can begin to use it to solve problems. To begin, let’s look at a simple matrix multiplication example. First, import the library as usual:
import tensorflow as tf
With TensorFlow, we have to specify the type (Theano variable = TensorFlow placeholder):
A = tf.placeholder(tf.float32, shape=(5, 5), name='A')But shape and name are optional:
v = tf.placeholder(tf.float32)We use the ‘matmul’ function in TensorFlow. This name is more appropriate than ‘dot’:
u = tf.matmul(A, v)Similar to Theano, you need to “feed” the variables values. In TensorFlow, you do the “actual work” in a “session”.
with tf.Session() as session:
# the values are fed in via the argument “feed_dict”
# v needs to be of shape=(5, 1) not just shape=(5,)
# it’s more like “real” matrix multiplication
output = session.run(w, feed_dict={A: np.random.randn(5, 5), v: np.random.randn(5, 1)})
print output, type(output)
Simple Optimization Problem in TensorFlow
Analogous to the last chapter, we are going to optimize a quadratic in TensorFlow. Since you should already know how to calculate the answer by hand, this will help you reinforce your TensorFlow coding and feel more comfortable coding a neural network.
Start by creating a TensorFlow variable (in Theano, this would be a shared):
u = tf.Variable(20.0)
Next, create your cost function / expression:
cost = u*u + u + 1.0
Create an optimizer.
train_op = tf.train.GradientDescentOptimizer(0.3).minimize(cost)
This is the part that differs greatly from Theano. Not only does TensorFlow compute the gradient for you, it does the entire optimization for you, without you having to specify the parameter updates.
The downside to this is you are stuck with the optimization methods that Google has implemented. There are a wide variety in addition to pure gradient descent, including RMSProp (an adaptive learning rate method), and MomentumOptimizer (which allows you to move out of local minima using the speed of past weight changes).
I suspect that the full list will be updated in the near future, since forum posts indicate that Nesterov momentum is currently being worked on.
Next, create an op to initialize your variables (for this problem, it’s just “u”):
init = tf.initialize_all_variables()
And lastly, run your session:
with tf.Session() as session:
session.run(init)
for i in xrange(12):
session.run(train_op)
print “i = %d, cost = %.3f, u = %.3f” % (i, cost.eval(), u.eval())
Conclusion
TensorFlow is a powerful library that can be used to solve a wide range of problems, from image recognition to natural language processing. It uses a computational graph to represent mathematical operations and data flow. This makes it suitable for solving a wide range of problems, from image recognition to natural language processing.
TensorFlow also provides tools for building and training models in the form of neural networks. It is based on a computational graph, which allows for an efficient and flexible way to represent mathematical operations and data flow. Additionally, TensorFlow provides an optimizer that computes the gradient and performs the optimization, without the user having to specify the parameter updates.