Introduction
TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. It is used for Machine Learning applications such as neural networks. TensorFlow is widely used for both research and production purposes. It has become the most popular machine learning library for deep learning and other applications.
In this article, we will discuss the basics of TensorFlow and how it is used in Machine Learning applications. We will also look at a full TensorFlow code example on the MNIST dataset.
What is TensorFlow?
TensorFlow is a library for numerical computation using data flow graphs. It is used for deep learning and other applications. It is an open source software library developed by Google.
TensorFlow was originally developed to facilitate machine learning research. It has since become the most popular library for deep learning and other machine learning applications.
TensorFlow enables developers to create and deploy machine learning models on a variety of platforms, including mobile, embedded, and cloud. It also provides a range of tools and libraries for training and deploying models.
TensorFlow can be used for a wide range of tasks, including image recognition, natural language processing, and time series analysis. It is also used for reinforcement learning, robotics, and other applications.
How Does TensorFlow Work?
TensorFlow is based on a data flow graph. Each node in the graph represents an operation, such as a mathematical operation or an activation function. The edges represent the data that flow between the nodes.
The data flow graph is used to represent a sequence of operations that are performed on a set of data. Each operation is represented as a node in the graph. The edges represent the data that flow between the nodes.
The data flow graph can be used to represent any computable function or process. It is used to represent the computations performed in Machine Learning applications.
TensorFlow Code Example on MNIST
The MNIST dataset is a set of images of handwritten digits. It is a widely used dataset for Machine Learning applications.
In this example, we will use TensorFlow to build a convolutional neural network (CNN) to classify the images in the MNIST dataset. The code for this example is given below.
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from util import get_normalized_data, y2indicator
def error_rate(p, t):
return np.mean(p != t)
def main():
# step 1: get the data and define all the usual variables
X, Y = get_normalized_data()
max_iter = 15
print_period = 10
lr = 0.00004
reg = 0.01
Xtrain = X[:-1000,]
Ytrain = Y[:-1000]
Xtest = X[-1000:,]
Ytest = Y[-1000:]
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N / batch_sz
# add an extra layer just for fun
M1 = 300
M2 = 100
K = 10
W1_init = np.random.randn(D, M1) / 28
b1_init = np.zeros(M1)
W2_init = np.random.randn(M1, M2) / np.sqrt(M1)
b2_init = np.zeros(M2)
W3_init = np.random.randn(M2, K) / np.sqrt(M2)
b3_init = np.zeros(K)
# define variables and expressions
X = tf.placeholder(tf.float32, shape=(None, D), name='X')
T = tf.placeholder(tf.float32, shape=(None, K), name='T')
W1 = tf.Variable(W1_init.astype(np.float32))
b1 = tf.Variable(b1_init.astype(np.float32))
W2 = tf.Variable(W2_init.astype(np.float32))
b2 = tf.Variable(b2_init.astype(np.float32))
W3 = tf.Variable(W3_init.astype(np.float32))
b3 = tf.Variable(b3_init.astype(np.float32))
# define the model
Z1 = tf.nn.relu( tf.matmul(X, W1) + b1 )
Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2 )
Yish = tf.matmul(Z2, W3) + b3 # remember, the cost function does the soft- maxing! weird, right?
# softmax_cross_entropy_with_logits take in the “logits”
# if you wanted to know the actual output of the neural net,
# you could pass “Yish” into tf.nn.softmax(logits)
cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(Yish, T))
# we choose the optimizer but don’t implement the algorithm ourselves
# let’s go with RMSprop, since we just learned about it.
# it includes momentum!
train_op = tf.train.RMSPropOptimizer(lr, decay=0.99, momentum=0.9).min-
imize(cost)
# we’ll use this to calculate the error rate
predict_op = tf.argmax(Yish, 1)
LL = []
init = tf.initialize_all_variables()
with tf.Session() as session:
session.run(init)
for i in xrange(max_iter):
for j in xrange(n_batches):
Xbatch = Xtrain[jbatch_sz:(jbatch_sz + batch_sz),]
Ybatch = Ytrain_ind[jbatch_sz:(jbatch_sz + batch_sz),]
session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
if j % print_period == 0:
test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest_ind})
prediction = session.run(predict_op, feed_dict={X: Xtest})
err = error_rate(prediction, Ytest)
print “Cost / err at iteration i=%d, j=%d: %.3f / %.3f” % (i, j,
test_cost, err)
LL.append(test_cost)
plt.plot(LL)
plt.show()
# increase max_iter and notice how the test cost starts to increase.
# are we overfitting by adding that extra layer?
# how would you add regularization to this model?
if name == ‘main‘:
main()
Conclusion
In this article, we discussed the basics of TensorFlow and how it is used in Machine Learning applications. We also looked at a full TensorFlow code example on the MNIST dataset.
TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. It is used for Machine Learning applications such as neural networks. TensorFlow is widely used for both research and production purposes.
TensorFlow enables developers to create and deploy machine learning models on a variety of platforms, including mobile, embedded, and cloud. It also provides a range of tools and libraries for training and deploying models.
TensorFlow can be used for a wide range of tasks, including image recognition, natural language processing, and time series analysis. It is also used for reinforcement learning, robotics, and other applications.