Deep Learning with TensorFlow

Source code for GPU with soft placement

We report, just for better understanding, the complete source code:

import numpy as np import tensorflow as tf import datetime log_device_placement = True n = 10 A = np.random.rand(10000, 10000).astype('float32') B = np.random.rand(10000, 10000).astype('float32') c1 = [] def matpow(M, n):     if n < 1: #Abstract cases where n < 1         return M     else:         return tf.matmul(M, matpow(M, n-1)) with tf.device('/gpu:1'):     a = tf.placeholder(tf.float32, [10000, 10000])     b = tf.placeholder(tf.float32, [10000, 10000])     c1.append(matpow(a, n))     c1.append(matpow(b, n)) with tf.device('/cpu:0'):     sum = tf.add_n(c1)  t1_1 = datetime.datetime.now() with tf.Session(config=tf.ConfigProto\                  (allow_soft_placement=True,\  log_device_placement=log_device_placement))\ ...