![Code Readability
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()
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]
www.edureka.co](https://image.slidesharecdn.com/theano-vs-tensorflow-191023053902/85/Theano-vs-TensorFlow-Edureka-14-320.jpg)
![Code Readability
# 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) + b
www.edureka.co](https://image.slidesharecdn.com/theano-vs-tensorflow-191023053902/85/Theano-vs-TensorFlow-Edureka-15-320.jpg)
![Code Readability
# 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)
www.edureka.co](https://image.slidesharecdn.com/theano-vs-tensorflow-191023053902/85/Theano-vs-TensorFlow-Edureka-16-320.jpg)
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Theano vs TensorFlow | Edureka
The document compares two scientific computing libraries, Theano and TensorFlow, highlighting their features, performance, and support for various operations. It outlines differences in speed, compatibility with high-level APIs, community support, and code readability. Additionally, the document includes code snippets demonstrating the use of both libraries for training a model.
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Theano vs TensorFlow | Edureka
- 1. Copyright © 2017,edureka and/or its affiliates. All rights reserved.
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- 3. Theano Theano can bedefined as a library for Scientific Computing Multi-Dimensional Array CPU and GPU www.edureka.co
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- 5. TensorFlow TensorFlow is anopen-source software library by Google Brain DataFlow Graphs www.edureka.co
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- 8. Theano vs TensorFlow SPEED SingleCore Multi Core www.edureka.co
- 9. Theano vs TensorFlow Technology Benefits Supportsa wide range of Operations Still has to come at par with Theano Computes the gradient when determining the Backpropagation error That’s not the case for TensorFlow Full control over Optimizers Gives access to lots of good Optimizers out of the box www.edureka.co
- 10. Theano vs TensorFlow Compatibility Integrationwith High Level API like Keras It’s not quite there yet. It has Native Windows Support. Currently, TensorFlow lacks this Support. Supports High-Level Wrappers like Lasagne. No Support for Lasagne. www.edureka.co
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- 13. Theano vs TensorFlow CodeReadability www.edureka.co
- 14. Code Readability import theano importtheano.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() 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] www.edureka.co
- 15. Code Readability # 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) + b www.edureka.co
- 16. Code Readability # 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) www.edureka.co
- 17. Code Readability # 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() www.edureka.co
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- 19. Copyright © 2017,edureka and/or its affiliates. All rights reserved. www.edureka.co
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