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Neural Network Foundations with TensorFlow 2.0
For now, let's start with a simple code comparison just to give you some initial
intuition. If you have never installed TensorFlow before, then let's install it using pip:
You can find more options for installing TensorFlow at https://
www.tensorflow.org/install.
Only CPU support:
pip install tensorflow
With GPU support:
pip install tensorflow-gpu
In order to understand what's new in TensorFlow 2.0, it might be useful to have a
look at the traditional way of coding neural networks in TensorFlow 1.0. If this is the
first time you have seen a neural network, please do not pay attention to the details
but simply count the number of lines:
import tensorflow.compat.v1 as tf
in_a = tf.placeholder(dtype=tf.float32, shape=(2))
def model(x):
with tf.variable_scope("matmul"):
W = tf.get_variable("W", initializer=tf.ones(shape=(2,2)))
b = tf.get_variable("b", initializer=tf.zeros(shape=(2)))
return x * W + b
out_a = model(in_a)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
outs = sess.run([out_a],
feed_dict={in_a: [1, 0]})
In total, we have 11 lines here. Now let's install TensorFlow 2.0:
Only CPU support:
pip install tensorflow==2.0.0-alpha0
With GPU support:
pip install tensorflow-gpu==2.0.0-alpha0
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