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# aymericdamien/TensorFlow-Examples

4c8c201 Mar 7, 2018
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 ''' Basic introduction to TensorFlow's Eager API. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ What is Eager API? " Eager execution is an imperative, define-by-run interface where operations are executed immediately as they are called from Python. This makes it easier to get started with TensorFlow, and can make research and development more intuitive. A vast majority of the TensorFlow API remains the same whether eager execution is enabled or not. As a result, the exact same code that constructs TensorFlow graphs (e.g. using the layers API) can be executed imperatively by using eager execution. Conversely, most models written with Eager enabled can be converted to a graph that can be further optimized and/or extracted for deployment in production without changing code. " - Rajat Monga ''' from __future__ import absolute_import, division, print_function import numpy as np import tensorflow as tf import tensorflow.contrib.eager as tfe # Set Eager API print("Setting Eager mode...") tfe.enable_eager_execution() # Define constant tensors print("Define constant tensors") a = tf.constant(2) print("a = %i" % a) b = tf.constant(3) print("b = %i" % b) # Run the operation without the need for tf.Session print("Running operations, without tf.Session") c = a + b print("a + b = %i" % c) d = a * b print("a * b = %i" % d) # Full compatibility with Numpy print("Mixing operations with Tensors and Numpy Arrays") # Define constant tensors a = tf.constant([[2., 1.], [1., 0.]], dtype=tf.float32) print("Tensor:\n a = %s" % a) b = np.array([[3., 0.], [5., 1.]], dtype=np.float32) print("NumpyArray:\n b = %s" % b) # Run the operation without the need for tf.Session print("Running operations, without tf.Session") c = a + b print("a + b = %s" % c) d = tf.matmul(a, b) print("a * b = %s" % d) print("Iterate through Tensor 'a':") for i in range(a.shape[0]): for j in range(a.shape[1]): print(a[i][j])
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