In the Ioffe and Szegedy 2015, the authors state that “we would like to ensure that for any parameter values, the network always produces activations with the desired distribution”. So the Batch Normalization Layer is actually inserted right after a Conv Layer/Fully Connected Layer, but before feeding into ReLu (or any other kinds of) activation. … Read more
If you have a Tensor t, calling t.eval() is equivalent to calling tf.get_default_session().run(t). You can make a session the default as follows: t = tf.constant(42.0) sess = tf.Session() with sess.as_default(): # or `with sess:` to close on exit assert sess is tf.get_default_session() assert t.eval() == sess.run(t) The most important difference is that you can use … Read more
There is an undocumented method called device_lib.list_local_devices() that enables you to list the devices available in the local process. (N.B. As an undocumented method, this is subject to backwards incompatible changes.) The function returns a list of DeviceAttributes protocol buffer objects. You can extract a list of string device names for the GPU devices as … Read more
Just type the command you want execute with the user permission, if you don’t want to change the permission: pip3 install –upgrade tensorflow-gpu –user
TL;DR) See this table: https://www.tensorflow.org/install/source#gpu Generally: Check the CUDA version: cat /usr/local/cuda/version.txt and cuDNN version: grep CUDNN_MAJOR -A 2 /usr/local/cuda/include/cudnn.h and install a combination as given below in the images or here. The following images and the link provide an overview of the officially supported/tested combinations of CUDA and TensorFlow on Linux, macOS and Windows: … Read more
TensorFlow 2.x Eager Execution is enabled by default, so just call .numpy() on the Tensor object. import tensorflow as tf a = tf.constant([[1, 2], [3, 4]]) b = tf.add(a, 1) a.numpy() # array([[1, 2], # [3, 4]], dtype=int32) b.numpy() # array([[2, 3], # [4, 5]], dtype=int32) tf.multiply(a, b).numpy() # array([[ 2, 6], # [12, 20]], … Read more
Let’s begin by a short introduction to variable sharing. It is a mechanism in TensorFlow that allows for sharing variables accessed in different parts of the code without passing references to the variable around. The method tf.get_variable can be used with the name of the variable as the argument to either create a new variable … Read more
In short, you use tf.Variable for trainable variables such as weights (W) and biases (B) for your model. weights = tf.Variable( tf.truncated_normal([IMAGE_PIXELS, hidden1_units], stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name=”weights”) biases = tf.Variable(tf.zeros([hidden1_units]), name=”biases”) tf.placeholder is used to feed actual training examples. images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IMAGE_PIXELS)) labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size)) This is how you feed the training … Read more
The latest requirements for running TensorFlow are documented in the installation documentation. TensorFlow only supports 64-bit Python TensorFlow only supports certain versions of Python (for example, Python 3.6 is not supported) So, if you’re using an out-of-range version of Python (older or newer) or a 32-bit version, then you’ll need to use a different version.